UAV Swarm Tactics: An Agent-Based Simulation and Markov Process Analysis

DTIC Science & Technology

2013-06-01

CRN Common Random Numbers CSV Comma Separated Values DoE Design of Experiment GLM Generalized Linear Model HVT High Value Target JAR Java ARchive JMF... Java Media Framework JRE Java runtime environment Mason Multi-Agent Simulator Of Networks MOE Measure Of Effectiveness MOP Measures Of Performance...with every set several times, and to write a CSV file with the results. Rather than scripting the agent behavior deterministically, the agents should

SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling.

PubMed

Solovyev, Alexey; Mikheev, Maxim; Zhou, Leming; Dutta-Moscato, Joyeeta; Ziraldo, Cordelia; An, Gary; Vodovotz, Yoram; Mi, Qi

2010-01-01

Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.

A Novel Framework for Characterizing Exposure-Related Behaviors Using Agent-Based Models Embedded with Needs-Based Artificial Intelligence (CSSSA2016)

EPA Science Inventory

Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that is able to simulate longitudinal patterns in behaviors. By basing o...

Agent-Based Crowd Simulation Considering Emotion Contagion for Emergency Evacuation Problem

NASA Astrophysics Data System (ADS)

Faroqi, H.; Mesgari, M.-S.

2015-12-01

During emergencies, emotions greatly affect human behaviour. For more realistic multi-agent systems in simulations of emergency evacuations, it is important to incorporate emotions and their effects on the agents. In few words, emotional contagion is a process in which a person or group influences the emotions or behavior of another person or group through the conscious or unconscious induction of emotion states and behavioral attitudes. In this study, we simulate an emergency situation in an open square area with three exits considering Adults and Children agents with different behavior. Also, Security agents are considered in order to guide Adults and Children for finding the exits and be calm. Six levels of emotion levels are considered for each agent in different scenarios and situations. The agent-based simulated model initialize with the random scattering of agent populations and then when an alarm occurs, each agent react to the situation based on its and neighbors current circumstances. The main goal of each agent is firstly to find the exit, and then help other agents to find their ways. Numbers of exited agents along with their emotion levels and damaged agents are compared in different scenarios with different initialization in order to evaluate the achieved results of the simulated model. NetLogo 5.2 is used as the multi-agent simulation framework with R language as the developing language.

High performance cellular level agent-based simulation with FLAME for the GPU.

PubMed

Richmond, Paul; Walker, Dawn; Coakley, Simon; Romano, Daniela

2010-05-01

Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with 'bottom-up' simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.

A decentralised multi-agent approach to enhance the stability of smart microgrids with renewable energy

NASA Astrophysics Data System (ADS)

Rahman, M. S.; Pota, H. R.; Mahmud, M. A.; Hossain, M. J.

2016-05-01

This paper presents the impact of large penetration of wind power on the transient stability through a dynamic evaluation of the critical clearing times (CCTs) by using intelligent agent-based approach. A decentralised multi-agent-based framework is developed, where agents represent a number of physical device models to form a complex infrastructure for computation and communication. They enable the dynamic flow of information and energy for the interaction between the physical processes and their activities. These agents dynamically adapt online measurements and use the CCT information for relay coordination to improve the transient stability of power systems. Simulations are carried out on a smart microgrid system for faults at increasing wind power penetration levels and the improvement in transient stability using the proposed agent-based framework is demonstrated.

A Multi Agent-Based Framework for Simulating Household PHEV Distribution and Electric Distribution Network Impact

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Xiaohui; Liu, Cheng; Kim, Hoe Kyoung

2011-01-01

The variation of household attributes such as income, travel distance, age, household member, and education for different residential areas may generate different market penetration rates for plug-in hybrid electric vehicle (PHEV). Residential areas with higher PHEV ownership could increase peak electric demand locally and require utilities to upgrade the electric distribution infrastructure even though the capacity of the regional power grid is under-utilized. Estimating the future PHEV ownership distribution at the residential household level can help us understand the impact of PHEV fleet on power line congestion, transformer overload and other unforeseen problems at the local residential distribution network level.more » It can also help utilities manage the timing of recharging demand to maximize load factors and utilization of existing distribution resources. This paper presents a multi agent-based simulation framework for 1) modeling spatial distribution of PHEV ownership at local residential household level, 2) discovering PHEV hot zones where PHEV ownership may quickly increase in the near future, and 3) estimating the impacts of the increasing PHEV ownership on the local electric distribution network with different charging strategies. In this paper, we use Knox County, TN as a case study to show the simulation results of the agent-based model (ABM) framework. However, the framework can be easily applied to other local areas in the US.« less

An equation-free approach to agent-based computation: Bifurcation analysis and control of stationary states

NASA Astrophysics Data System (ADS)

Siettos, C. I.; Gear, C. W.; Kevrekidis, I. G.

2012-08-01

We show how the equation-free approach can be exploited to enable agent-based simulators to perform system-level computations such as bifurcation, stability analysis and controller design. We illustrate these tasks through an event-driven agent-based model describing the dynamic behaviour of many interacting investors in the presence of mimesis. Using short bursts of appropriately initialized runs of the detailed, agent-based simulator, we construct the coarse-grained bifurcation diagram of the (expected) density of agents and investigate the stability of its multiple solution branches. When the mimetic coupling between agents becomes strong enough, the stable stationary state loses its stability at a coarse turning point bifurcation. We also demonstrate how the framework can be used to design a wash-out dynamic controller that stabilizes open-loop unstable stationary states even under model uncertainty.

Ant-Based Cyber Defense (also known as

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glenn Fink, PNNL

2015-09-29

ABCD is a four-level hierarchy with human supervisors at the top, a top-level agent called a Sergeant controlling each enclave, Sentinel agents located at each monitored host, and mobile Sensor agents that swarm through the enclaves to detect cyber malice and misconfigurations. The code comprises four parts: (1) the core agent framework, (2) the user interface and visualization, (3) test-range software to create a network of virtual machines including a simulated Internet and user and host activity emulation scripts, and (4) a test harness to allow the safe running of adversarial code within the framework of monitored virtual machines.

Little by Little Does the Trick: Design and Construction of a Discrete Event Agent-Based Simulation Framework

DTIC Science & Technology

2007-12-01

model. Finally, we build a small agent-based model using the component architecture to demonstrate the library’s functionality. 15. NUMBER OF...and a Behavioral model. Finally, we build a small agent-based model using the component architecture to demonstrate the library’s functionality...prototypes an architectural design which is generalizable, reusable, and extensible. We have created an initial set of model elements that demonstrate

Agents in bioinformatics, computational and systems biology.

PubMed

Merelli, Emanuela; Armano, Giuliano; Cannata, Nicola; Corradini, Flavio; d'Inverno, Mark; Doms, Andreas; Lord, Phillip; Martin, Andrew; Milanesi, Luciano; Möller, Steffen; Schroeder, Michael; Luck, Michael

2007-01-01

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

A simulation framework for mapping risks in clinical processes: the case of in-patient transfers.

PubMed

Dunn, Adam G; Ong, Mei-Sing; Westbrook, Johanna I; Magrabi, Farah; Coiera, Enrico; Wobcke, Wayne

2011-05-01

To model how individual violations in routine clinical processes cumulatively contribute to the risk of adverse events in hospital using an agent-based simulation framework. An agent-based simulation was designed to model the cascade of common violations that contribute to the risk of adverse events in routine clinical processes. Clinicians and the information systems that support them were represented as a group of interacting agents using data from direct observations. The model was calibrated using data from 101 patient transfers observed in a hospital and results were validated for one of two scenarios (a misidentification scenario and an infection control scenario). Repeated simulations using the calibrated model were undertaken to create a distribution of possible process outcomes. The likelihood of end-of-chain risk is the main outcome measure, reported for each of the two scenarios. The simulations demonstrate end-of-chain risks of 8% and 24% for the misidentification and infection control scenarios, respectively. Over 95% of the simulations in both scenarios are unique, indicating that the in-patient transfer process diverges from prescribed work practices in a variety of ways. The simulation allowed us to model the risk of adverse events in a clinical process, by generating the variety of possible work subject to violations, a novel prospective risk analysis method. The in-patient transfer process has a high proportion of unique trajectories, implying that risk mitigation may benefit from focusing on reducing complexity rather than augmenting the process with further rule-based protocols.

GridLAB-D: An Agent-Based Simulation Framework for Smart Grids

DOE PAGES

Chassin, David P.; Fuller, Jason C.; Djilali, Ned

2014-01-01

Simulation of smart grid technologies requires a fundamentally new approach to integrated modeling of power systems, energy markets, building technologies, and the plethora of other resources and assets that are becoming part of modern electricity production, delivery, and consumption systems. As a result, the US Department of Energy’s Office of Electricity commissioned the development of a new type of power system simulation tool called GridLAB-D that uses an agent-based approach to simulating smart grids. This paper presents the numerical methods and approach to time-series simulation used by GridLAB-D and reviews applications in power system studies, market design, building control systemmore » design, and integration of wind power in a smart grid.« less

GridLAB-D: An Agent-Based Simulation Framework for Smart Grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chassin, David P.; Fuller, Jason C.; Djilali, Ned

2014-06-23

Simulation of smart grid technologies requires a fundamentally new approach to integrated modeling of power systems, energy markets, building technologies, and the plethora of other resources and assets that are becoming part of modern electricity production, delivery, and consumption systems. As a result, the US Department of Energy’s Office of Electricity commissioned the development of a new type of power system simulation tool called GridLAB-D that uses an agent-based approach to simulating smart grids. This paper presents the numerical methods and approach to time-series simulation used by GridLAB-D and reviews applications in power system studies, market design, building control systemmore » design, and integration of wind power in a smart grid.« less

A hybrid simulation approach for integrating safety behavior into construction planning: An earthmoving case study.

PubMed

Goh, Yang Miang; Askar Ali, Mohamed Jawad

2016-08-01

One of the key challenges in improving construction safety and health is the management of safety behavior. From a system point of view, workers work unsafely due to system level issues such as poor safety culture, excessive production pressure, inadequate allocation of resources and time and lack of training. These systemic issues should be eradicated or minimized during planning. However, there is a lack of detailed planning tools to help managers assess the impact of their upstream decisions on worker safety behavior. Even though simulation had been used in construction planning, the review conducted in this study showed that construction safety management research had not been exploiting the potential of simulation techniques. Thus, a hybrid simulation framework is proposed to facilitate integration of safety management considerations into construction activity simulation. The hybrid framework consists of discrete event simulation (DES) as the core, but heterogeneous, interactive and intelligent (able to make decisions) agents replace traditional entities and resources. In addition, some of the cognitive processes and physiological aspects of agents are captured using system dynamics (SD) approach. The combination of DES, agent-based simulation (ABS) and SD allows a more "natural" representation of the complex dynamics in construction activities. The proposed hybrid framework was demonstrated using a hypothetical case study. In addition, due to the lack of application of factorial experiment approach in safety management simulation, the case study demonstrated sensitivity analysis and factorial experiment to guide future research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evolutionary game theory using agent-based methods.

PubMed

Adami, Christoph; Schossau, Jory; Hintze, Arend

2016-12-01

Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection-strong mutation limit), but that mathematics is crucial to validate the computational simulations. Copyright © 2016 Elsevier B.V. All rights reserved.

Agent-based models in translational systems biology

PubMed Central

An, Gary; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram

2013-01-01

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing. PMID:20835989

An Empirical Agent-Based Model to Simulate the Adoption of Water Reuse Using the Social Amplification of Risk Framework.

PubMed

Kandiah, Venu; Binder, Andrew R; Berglund, Emily Z

2017-10-01

Water reuse can serve as a sustainable alternative water source for urban areas. However, the successful implementation of large-scale water reuse projects depends on community acceptance. Because of the negative perceptions that are traditionally associated with reclaimed water, water reuse is often not considered in the development of urban water management plans. This study develops a simulation model for understanding community opinion dynamics surrounding the issue of water reuse, and how individual perceptions evolve within that context, which can help in the planning and decision-making process. Based on the social amplification of risk framework, our agent-based model simulates consumer perceptions, discussion patterns, and their adoption or rejection of water reuse. The model is based on the "risk publics" model, an empirical approach that uses the concept of belief clusters to explain the adoption of new technology. Each household is represented as an agent, and parameters that define their behavior and attributes are defined from survey data. Community-level parameters-including social groups, relationships, and communication variables, also from survey data-are encoded to simulate the social processes that influence community opinion. The model demonstrates its capabilities to simulate opinion dynamics and consumer adoption of water reuse. In addition, based on empirical data, the model is applied to investigate water reuse behavior in different regions of the United States. Importantly, our results reveal that public opinion dynamics emerge differently based on membership in opinion clusters, frequency of discussion, and the structure of social networks. © 2017 Society for Risk Analysis.

Crowdsourcing: A Primer and Its implications for Systems Engineering

DTIC Science & Technology

2012-08-01

detailing areas to be improved within current crowdsourcing frameworks. Finally, an agent-based simulation using machine learning techniques is defined, preliminary results are presented, and future research directions are described.

A hybrid computational model to explore the topological characteristics of epithelial tissues.

PubMed

González-Valverde, Ismael; García-Aznar, José Manuel

2017-11-01

Epithelial tissues show a particular topology where cells resemble a polygon-like shape, but some biological processes can alter this tissue topology. During cell proliferation, mitotic cell dilation deforms the tissue and modifies the tissue topology. Additionally, cells are reorganized in the epithelial layer and these rearrangements also alter the polygon distribution. We present here a computer-based hybrid framework focused on the simulation of epithelial layer dynamics that combines discrete and continuum numerical models. In this framework, we consider topological and mechanical aspects of the epithelial tissue. Individual cells in the tissue are simulated by an off-lattice agent-based model, which keeps the information of each cell. In addition, we model the cell-cell interaction forces and the cell cycle. Otherwise, we simulate the passive mechanical behaviour of the cell monolayer using a material that approximates the mechanical properties of the cell. This continuum approach is solved by the finite element method, which uses a dynamic mesh generated by the triangulation of cell polygons. Forces generated by cell-cell interaction in the agent-based model are also applied on the finite element mesh. Cell movement in the agent-based model is driven by the displacements obtained from the deformed finite element mesh of the continuum mechanical approach. We successfully compare the results of our simulations with some experiments about the topology of proliferating epithelial tissues in Drosophila. Our framework is able to model the emergent behaviour of the cell monolayer that is due to local cell-cell interactions, which have a direct influence on the dynamics of the epithelial tissue. Copyright © 2017 John Wiley & Sons, Ltd.

BioASF: a framework for automatically generating executable pathway models specified in BioPAX.

PubMed

Haydarlou, Reza; Jacobsen, Annika; Bonzanni, Nicola; Feenstra, K Anton; Abeln, Sanne; Heringa, Jaap

2016-06-15

Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models. However, a generic software framework for simulating BioPAX models is missing. Here, we attempt to fill this gap by introducing a generic simulation framework for BioPAX. The framework explicitly separates the execution model from the model structure as provided by BioPAX, with the advantage that the modelling process becomes more reproducible and intrinsically more modular; this ensures natural biological constraints are satisfied upon execution. The framework is based on the principles of discrete event systems and multi-agent systems, and is capable of automatically generating a hierarchical multi-agent system for a given BioPAX model. To demonstrate the applicability of the framework, we simulated two types of biological network models: a gene regulatory network modeling the haematopoietic stem cell regulators and a signal transduction network modeling the Wnt/β-catenin signaling pathway. We observed that the results of the simulations performed using our framework were entirely consistent with the simulation results reported by the researchers who developed the original models in a proprietary language. The framework, implemented in Java, is open source and its source code, documentation and tutorial are available at http://www.ibi.vu.nl/programs/BioASF CONTACT: j.heringa@vu.nl. © The Author 2016. Published by Oxford University Press.

Agent Based Modeling and Simulation Framework for Supply Chain Risk Management

DTIC Science & Technology

2012-03-01

Christopher and Peck 2004) macroeconomic , policy, competition, and resource (Ghoshal 1987) value chain, operational, event, and recurring (Shi 2004...clustering algorithms in agent logic to protect company privacy ( da Silva et al. 2006), aggregation of domain context in agent data analysis logic (Xiang...Operational Availability ( OA ) for FMC and PMC. 75 Mission Capable (MICAP) Hours is the measure of total time (in a month) consumable or reparable

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions.

PubMed

Wilmoth, Jared L; Doak, Peter W; Timm, Andrea; Halsted, Michelle; Anderson, John D; Ginovart, Marta; Prats, Clara; Portell, Xavier; Retterer, Scott T; Fuentes-Cabrera, Miguel

2018-01-01

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P . aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density and local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions

PubMed Central

Wilmoth, Jared L.; Doak, Peter W.; Timm, Andrea; Halsted, Michelle; Anderson, John D.; Ginovart, Marta; Prats, Clara; Portell, Xavier; Retterer, Scott T.; Fuentes-Cabrera, Miguel

2018-01-01

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density and local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models. PMID:29467721

Integrated control of lateral and vertical vehicle dynamics based on multi-agent system

NASA Astrophysics Data System (ADS)

Huang, Chen; Chen, Long; Yun, Chaochun; Jiang, Haobin; Chen, Yuexia

2014-03-01

The existing research of the integrated chassis control mainly focuses on the different evaluation indexes and control strategy. Among the different evaluation indexes, the comprehensive properties are usually not considered based on the non-linear superposition principle. But, the control strategy has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, based on belief, desire and intention(BDI)-agent model framework, the TYRE agent, electric power steering(EPS) agent and active suspension system(ASS) agent are proposed. In the system(SYS) agent, the coordination mechanism is employed to manage interdependences and conflicts among other agents, so as to improve the flexibility, adaptability, and robustness of the global control system. Due to the existence of the simulation demand of dynamic performance, the vehicle multi-body dynamics model is established by SIMPACK. And then the co-simulation analysis is conducted to evaluate the proposed multi-agent system(MAS) controller. The simulation results demonstrate that the MAS has good effect on the performance of EPS and ASS. Meantime, the better road feeling for the driver is provided considering the multiple and complex driving traffic. Finally, the MAS rapid control prototyping is built to conduct the real vehicle test. The test results are consistent to the simulation results, which verifies the correctness of simulation. The proposed research ensures the driving safety, enhances the handling stability, and improves the ride comfort.

Modelling the B2C Marketplace: Evaluation of a Reputation Metric for e-Commerce

NASA Astrophysics Data System (ADS)

Gutowska, Anna; Sloane, Andrew

This paper evaluates recently developed novel and comprehensive reputation metric designed for the distributed multi-agent reputation system for the Business-to-Consumer (B2C) E-commerce applications. To do that an agent-based simulation framework was implemented which models different types of behaviours in the marketplace. The trustworthiness of different types of providers is investigated to establish whether the simulation models behaviour of B2C e-Commerce systems as they are expected to behave in real life.

Intelligent microchip networks: an agent-on-chip synthesis framework for the design of smart and robust sensor networks

NASA Astrophysics Data System (ADS)

Bosse, Stefan

2013-05-01

Sensorial materials consisting of high-density, miniaturized, and embedded sensor networks require new robust and reliable data processing and communication approaches. Structural health monitoring is one major field of application for sensorial materials. Each sensor node provides some kind of sensor, electronics, data processing, and communication with a strong focus on microchip-level implementation to meet the goals of miniaturization and low-power energy environments, a prerequisite for autonomous behaviour and operation. Reliability requires robustness of the entire system in the presence of node, link, data processing, and communication failures. Interaction between nodes is required to manage and distribute information. One common interaction model is the mobile agent. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves, which actions are performed, and they are capable of flexible behaviour, reacting on the environment and other agents, providing some degree of robustness. Traditionally multi-agent systems are abstract programming models which are implemented in software and executed on program controlled computer architectures. This approach does not well scale to micro-chip level and requires full equipped computers and communication structures, and the hardware architecture does not consider and reflect the requirements for agent processing and interaction. We propose and demonstrate a novel design paradigm for reliable distributed data processing systems and a synthesis methodology and framework for multi-agent systems implementable entirely on microchip-level with resource and power constrained digital logic supporting Agent-On-Chip architectures (AoC). The agent behaviour and mobility is fully integrated on the micro-chip using pipelined communicating processes implemented with finite-state machines and register-transfer logic. The agent behaviour, interaction (communication), and mobility features are modelled and specified on a machine-independent abstract programming level using a state-based agent behaviour language (APL). With this APL a high-level agent compiler is able to synthesize a hardware model (RTL, VHDL), a software model (C, ML), or a simulation model (XML) suitable to simulate a multi-agent system using the SeSAm simulator framework. Agent communication is provided by a simple tuple-space database implemented on node level providing fault tolerant access of global data. A novel synthesis development kit (SynDK) based on a graph-structured database approach is introduced to support the rapid development of compilers and synthesis tools, used for example for the design and implementation of the APL compiler.

On-lattice agent-based simulation of populations of cells within the open-source Chaste framework.

PubMed

Figueredo, Grazziela P; Joshi, Tanvi V; Osborne, James M; Byrne, Helen M; Owen, Markus R

2013-04-06

Over the years, agent-based models have been developed that combine cell division and reinforced random walks of cells on a regular lattice, reaction-diffusion equations for nutrients and growth factors; and ordinary differential equations for the subcellular networks regulating the cell cycle. When linked to a vascular layer, this multiple scale model framework has been applied to tumour growth and therapy. Here, we report on the creation of an agent-based multi-scale environment amalgamating the characteristics of these models within a Virtual Physiological Human (VPH) Exemplar Project. This project enables reuse, integration, expansion and sharing of the model and relevant data. The agent-based and reaction-diffusion parts of the multi-scale model have been implemented and are available for download as part of the latest public release of Chaste (Cancer, Heart and Soft Tissue Environment; http://www.cs.ox.ac.uk/chaste/), part of the VPH Toolkit (http://toolkit.vph-noe.eu/). The environment functionalities are verified against the original models, in addition to extra validation of all aspects of the code. In this work, we present the details of the implementation of the agent-based environment, including the system description, the conceptual model, the development of the simulation model and the processes of verification and validation of the simulation results. We explore the potential use of the environment by presenting exemplar applications of the 'what if' scenarios that can easily be studied in the environment. These examples relate to tumour growth, cellular competition for resources and tumour responses to hypoxia (low oxygen levels). We conclude our work by summarizing the future steps for the expansion of the current system.

The Unified Behavior Framework for the Simulation of Autonomous Agents

DTIC Science & Technology

2015-03-01

1980s, researchers have designed a variety of robot control architectures intending to imbue robots with some degree of autonomy. A recently developed ...Identification Friend or Foe viii THE UNIFIED BEHAVIOR FRAMEWORK FOR THE SIMULATION OF AUTONOMOUS AGENTS I. Introduction The development of autonomy has...room for research by utilizing methods like simulation and modeling that consume less time and fewer monetary resources. A recently developed reactive

Self-organizing network services with evolutionary adaptation.

PubMed

Nakano, Tadashi; Suda, Tatsuya

2005-09-01

This paper proposes a novel framework for developing adaptive and scalable network services. In the proposed framework, a network service is implemented as a group of autonomous agents that interact in the network environment. Agents in the proposed framework are autonomous and capable of simple behaviors (e.g., replication, migration, and death). In this paper, an evolutionary adaptation mechanism is designed using genetic algorithms (GAs) for agents to evolve their behaviors and improve their fitness values (e.g., response time to a service request) to the environment. The proposed framework is evaluated through simulations, and the simulation results demonstrate the ability of autonomous agents to adapt to the network environment. The proposed framework may be suitable for disseminating network services in dynamic and large-scale networks where a large number of data and services need to be replicated, moved, and deleted in a decentralized manner.

A framework for the use of agent based modeling to simulate inter- and intraindividual variation in human behaviors

EPA Science Inventory

Simulation of human behavior in exposure modeling is a complex task. Traditionally, inter-individual variation in human activity has been modeled by drawing from a pool of single day time-activity diaries such as the US EPA Consolidated Human Activity Database (CHAD). Here, an ag...

Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson's arms race model.

PubMed

Riaz, Faisal; Niazi, Muaz A

2017-01-01

This paper presents the concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior. The presented AVs also have the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. Exploratory Agent Based Modeling (EABM) level of the Cognitive Agent Based Computing (CABC) framework has been utilized to design the proposed social agent. Furthermore, to emulate the functionality of mentalizing and mirroring modules of proposed social agent, a tailored mathematical model of the Richardson's arms race model has also been presented. The performance of the proposed social agent has been validated at two levels-firstly it has been simulated using NetLogo, a standard agent-based modeling tool and also, at a practical level using a prototype AV. The simulation results have confirmed that the proposed social agent-based collision avoidance strategy is 78.52% more efficient than Random walk based collision avoidance strategy in congested flock-like topologies. Whereas practical results have confirmed that the proposed scheme can avoid rear end and lateral collisions with the efficiency of 99.876% as compared with the IEEE 802.11n-based existing state of the art mirroring neuron-based collision avoidance scheme.

Towards social autonomous vehicles: Efficient collision avoidance scheme using Richardson’s arms race model

PubMed Central

Niazi, Muaz A.

2017-01-01

This paper presents the concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior. The presented AVs also have the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. Exploratory Agent Based Modeling (EABM) level of the Cognitive Agent Based Computing (CABC) framework has been utilized to design the proposed social agent. Furthermore, to emulate the functionality of mentalizing and mirroring modules of proposed social agent, a tailored mathematical model of the Richardson’s arms race model has also been presented. The performance of the proposed social agent has been validated at two levels–firstly it has been simulated using NetLogo, a standard agent-based modeling tool and also, at a practical level using a prototype AV. The simulation results have confirmed that the proposed social agent-based collision avoidance strategy is 78.52% more efficient than Random walk based collision avoidance strategy in congested flock-like topologies. Whereas practical results have confirmed that the proposed scheme can avoid rear end and lateral collisions with the efficiency of 99.876% as compared with the IEEE 802.11n-based existing state of the art mirroring neuron-based collision avoidance scheme. PMID:29040294

Agent-based large-scale emergency evacuation using real-time open government data.

DOT National Transportation Integrated Search

2014-01-01

The open government initiatives have provided tremendous data resources for the : transportation system and emergency services in urban areas. This paper proposes : a traffic simulation framework using high temporal resolution demographic data : and ...

Exploring Tradeoffs in Demand-side and Supply-side Management of Urban Water Resources using Agent-based Modeling and Evolutionary Computation

NASA Astrophysics Data System (ADS)

Kanta, L.; Berglund, E. Z.

2015-12-01

Urban water supply systems may be managed through supply-side and demand-side strategies, which focus on water source expansion and demand reductions, respectively. Supply-side strategies bear infrastructure and energy costs, while demand-side strategies bear costs of implementation and inconvenience to consumers. To evaluate the performance of demand-side strategies, the participation and water use adaptations of consumers should be simulated. In this study, a Complex Adaptive Systems (CAS) framework is developed to simulate consumer agents that change their consumption to affect the withdrawal from the water supply system, which, in turn influences operational policies and long-term resource planning. Agent-based models are encoded to represent consumers and a policy maker agent and are coupled with water resources system simulation models. The CAS framework is coupled with an evolutionary computation-based multi-objective methodology to explore tradeoffs in cost, inconvenience to consumers, and environmental impacts for both supply-side and demand-side strategies. Decisions are identified to specify storage levels in a reservoir that trigger (1) increases in the volume of water pumped through inter-basin transfers from an external reservoir and (2) drought stages, which restrict the volume of water that is allowed for residential outdoor uses. The proposed methodology is demonstrated for Arlington, Texas, water supply system to identify non-dominated strategies for an historic drought decade. Results demonstrate that pumping costs associated with maximizing environmental reliability exceed pumping costs associated with minimizing restrictions on consumer water use.

POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Auld, Joshua; Hope, Michael; Ley, Hubert

This paper discusses the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typicallymore » done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center impacts on various network events such as accidents, congestion and weather events, show the potential of the system.« less

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions

DOE PAGES

Wilmoth, Jared L.; Doak, Peter W.; Timm, Andrea; ...

2018-02-06

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density andmore » local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.« less

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilmoth, Jared L.; Doak, Peter W.; Timm, Andrea

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density andmore » local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.« less

Agent-based model for the h-index - exact solution

NASA Astrophysics Data System (ADS)

Żogała-Siudem, Barbara; Siudem, Grzegorz; Cena, Anna; Gagolewski, Marek

2016-01-01

Hirsch's h-index is perhaps the most popular citation-based measure of scientific excellence. In 2013, Ionescu and Chopard proposed an agent-based model describing a process for generating publications and citations in an abstract scientific community [G. Ionescu, B. Chopard, Eur. Phys. J. B 86, 426 (2013)]. Within such a framework, one may simulate a scientist's activity, and - by extension - investigate the whole community of researchers. Even though the Ionescu and Chopard model predicts the h-index quite well, the authors provided a solution based solely on simulations. In this paper, we complete their results with exact, analytic formulas. What is more, by considering a simplified version of the Ionescu-Chopard model, we obtained a compact, easy to compute formula for the h-index. The derived approximate and exact solutions are investigated on a simulated and real-world data sets.

Multi-agent systems and their applications

DOE PAGES

Xie, Jing; Liu, Chen-Ching

2017-07-14

The number of distributed energy components and devices continues to increase globally. As a result, distributed control schemes are desirable for managing and utilizing these devices, together with the large amount of data. In recent years, agent-based technology becomes a powerful tool for engineering applications. As a computational paradigm, multi agent systems (MASs) provide a good solution for distributed control. Here in this paper, MASs and applications are discussed. A state-of-the-art literature survey is conducted on the system architecture, consensus algorithm, and multi-agent platform, framework, and simulator. In addition, a distributed under-frequency load shedding (UFLS) scheme is proposed using themore » MAS. Simulation results for a case study are presented. The future of MASs is discussed in the conclusion.« less

Multi-agent systems and their applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Jing; Liu, Chen-Ching

The number of distributed energy components and devices continues to increase globally. As a result, distributed control schemes are desirable for managing and utilizing these devices, together with the large amount of data. In recent years, agent-based technology becomes a powerful tool for engineering applications. As a computational paradigm, multi agent systems (MASs) provide a good solution for distributed control. Here in this paper, MASs and applications are discussed. A state-of-the-art literature survey is conducted on the system architecture, consensus algorithm, and multi-agent platform, framework, and simulator. In addition, a distributed under-frequency load shedding (UFLS) scheme is proposed using themore » MAS. Simulation results for a case study are presented. The future of MASs is discussed in the conclusion.« less

Multiagent intelligent systems

NASA Astrophysics Data System (ADS)

Krause, Lee S.; Dean, Christopher; Lehman, Lynn A.

2003-09-01

This paper will discuss a simulation approach based upon a family of agent-based models. As the demands placed upon simulation technology by such applications as Effects Based Operations (EBO), evaluations of indicators and warnings surrounding homeland defense and commercial demands such financial risk management current single thread based simulations will continue to show serious deficiencies. The types of "what if" analysis required to support these types of applications, demand rapidly re-configurable approaches capable of aggregating large models incorporating multiple viewpoints. The use of agent technology promises to provide a broad spectrum of models incorporating differing viewpoints through a synthesis of a collection of models. Each model would provide estimates to the overall scenario based upon their particular measure or aspect. An agent framework, denoted as the "family" would provide a common ontology in support of differing aspects of the scenario. This approach permits the future of modeling to change from viewing the problem as a single thread simulation, to take into account multiple viewpoints from different models. Even as models are updated or replaced the agent approach permits rapid inclusion in new or modified simulations. In this approach a variety of low and high-resolution information and its synthesis requires a family of models. Each agent "publishes" its support for a given measure and each model provides their own estimates on the scenario based upon their particular measure or aspect. If more than one agent provides the same measure (e.g. cognitive) then the results from these agents are combined to form an aggregate measure response. The objective would be to inform and help calibrate a qualitative model, rather than merely to present highly aggregated statistical information. As each result is processed, the next action can then be determined. This is done by a top-level decision system that communicates to the family at the ontology level without any specific understanding of the processes (or model) behind each agent. The increasingly complex demands upon simulation for the necessity to incorporate the breadth and depth of influencing factors makes a family of agent based models a promising solution. This paper will discuss that solution with syntax and semantics necessary to support the approach.

Predicted range expansion of Chinese tallow tree (Triadica sebifera) in forestlands of the southern United States

Treesearch

Hsiao-Hsuan Wang; William Grant; Todd Swannack; Jianbang Gan; William Rogers; Tomasz Koralewski; James Miller; John W. Taylor Jr.

2011-01-01

We present an integrated approach for predicting future range expansion of an invasive species (Chinese tallow tree) that incorporates statistical forecasting and analytical techniques within a spatially explicit, agent-based, simulation framework.

Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

DOE PAGES

Schryver, Jack; Nutaro, James; Shankar, Mallikarjun

2015-10-30

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease statesmore » in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.« less

Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schryver, Jack; Nutaro, James; Shankar, Mallikarjun

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease statesmore » in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.« less

Breaking Down Chemical Weapons by Metal-Organic Frameworks.

PubMed

Mondal, Suvendu Sekhar; Holdt, Hans-Jürgen

2016-01-04

Seek and destroy: Filtration schemes and self-detoxifying protective fabrics based on the Zr(IV)-containing metal-organic frameworks (MOFs) MOF-808 and UiO-66 doped with LiOtBu have been developed that capture and hydrolytically detoxify simulants of nerve agents and mustard gas. Both MOFs function as highly catalytic elements in these applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linking MODFLOW with an agent-based land-use model to support decision making

USGS Publications Warehouse

Reeves, H.W.; Zellner, M.L.

2010-01-01

The U.S. Geological Survey numerical groundwater flow model, MODFLOW, was integrated with an agent-based land-use model to yield a simulator for environmental planning studies. Ultimately, this integrated simulator will be used as a means to organize information, illustrate potential system responses, and facilitate communication within a participatory modeling framework. Initial results show the potential system response to different zoning policy scenarios in terms of the spatial patterns of development, which is referred to as urban form, and consequent impacts on groundwater levels. These results illustrate how the integrated simulator is capable of representing the complexity of the system. From a groundwater modeling perspective, the most important aspect of the integration is that the simulator generates stresses on the groundwater system within the simulation in contrast to the traditional approach that requires the user to specify the stresses through time. Copyright ?? 2010 The Author(s). Journal compilation ?? 2010 National Ground Water Association.

An Information Theoretic Framework and Self-organizing Agent- based Sensor Network Architecture for Power Plant Condition Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loparo, Kenneth; Kolacinski, Richard; Threeanaew, Wanchat

A central goal of the work was to enable both the extraction of all relevant information from sensor data, and the application of information gained from appropriate processing and fusion at the system level to operational control and decision-making at various levels of the control hierarchy through: 1. Exploiting the deep connection between information theory and the thermodynamic formalism, 2. Deployment using distributed intelligent agents with testing and validation in a hardware-in-the loop simulation environment. Enterprise architectures are the organizing logic for key business processes and IT infrastructure and, while the generality of current definitions provides sufficient flexibility, the currentmore » architecture frameworks do not inherently provide the appropriate structure. Of particular concern is that existing architecture frameworks often do not make a distinction between ``data'' and ``information.'' This work defines an enterprise architecture for health and condition monitoring of power plant equipment and further provides the appropriate foundation for addressing shortcomings in current architecture definition frameworks through the discovery of the information connectivity between the elements of a power generation plant. That is, to identify the correlative structure between available observations streams using informational measures. The principle focus here is on the implementation and testing of an emergent, agent-based, algorithm based on the foraging behavior of ants for eliciting this structure and on measures for characterizing differences between communication topologies. The elicitation algorithms are applied to data streams produced by a detailed numerical simulation of Alstom’s 1000 MW ultra-super-critical boiler and steam plant. The elicitation algorithm and topology characterization can be based on different informational metrics for detecting connectivity, e.g. mutual information and linear correlation.« less

Collaborative enterprise and virtual prototyping (CEVP): a product-centric approach to distributed simulation

NASA Astrophysics Data System (ADS)

Saunders, Vance M.

1999-06-01

The downsizing of the Department of Defense (DoD) and the associated reduction in budgets has re-emphasized the need for commonality, reuse, and standards with respect to the way DoD does business. DoD has implemented significant changes in how it buys weapon systems. The new emphasis is on concurrent engineering with Integrated Product and Process Development and collaboration with Integrated Product Teams. The new DoD vision includes Simulation Based Acquisition (SBA), a process supported by robust, collaborative use of simulation technology that is integrated across acquisition phases and programs. This paper discusses the Air Force Research Laboratory's efforts to use Modeling and Simulation (M&S) resources within a Collaborative Enterprise Environment to support SBA and other Collaborative Enterprise and Virtual Prototyping (CEVP) applications. The paper will discuss four technology areas: (1) a Processing Ontology that defines a hierarchically nested set of collaboration contexts needed to organize and support multi-disciplinary collaboration using M&S, (2) a partial taxonomy of intelligent agents needed to manage different M&S resource contributions to advancing the state of product development, (3) an agent- based process for interfacing disparate M&S resources into a CEVP framework, and (4) a Model-View-Control based approach to defining `a new way of doing business' for users of CEVP frameworks/systems.

A survey on adaptive engine technology for serious games

NASA Astrophysics Data System (ADS)

Rasim, Langi, Armein Z. R.; Munir, Rosmansyah, Yusep

2016-02-01

Serious Games has become a priceless tool in learning because it can simulate abstract concept to appear more realistic. The problem faced is that the players have different ability in playing the games. This causes the players to become frustrated if the game is too difficult or to get bored if it is too easy. Serious games have non-player character (NPC) in it. The NPC should be able to adapt to the players in such a way so that the players can feel comfortable in playing the games. Because of that, serious games development must involve an adaptive engine, which is by applying a learning machine that can adapt to different players. The development of adaptive engine can be viewed in terms of the frameworks and the algorithms. Frameworks include rules based, plan based, organization description based, proficiency of player based, and learning style and cognitive state based. Algorithms include agents based and non-agent based

Agent-based Modeling with MATSim for Hazards Evacuation Planning

NASA Astrophysics Data System (ADS)

Jones, J. M.; Ng, P.; Henry, K.; Peters, J.; Wood, N. J.

2015-12-01

Hazard evacuation planning requires robust modeling tools and techniques, such as least cost distance or agent-based modeling, to gain an understanding of a community's potential to reach safety before event (e.g. tsunami) arrival. Least cost distance modeling provides a static view of the evacuation landscape with an estimate of travel times to safety from each location in the hazard space. With this information, practitioners can assess a community's overall ability for timely evacuation. More information may be needed if evacuee congestion creates bottlenecks in the flow patterns. Dynamic movement patterns are best explored with agent-based models that simulate movement of and interaction between individual agents as evacuees through the hazard space, reacting to potential congestion areas along the evacuation route. The multi-agent transport simulation model MATSim is an agent-based modeling framework that can be applied to hazard evacuation planning. Developed jointly by universities in Switzerland and Germany, MATSim is open-source software written in Java and freely available for modification or enhancement. We successfully used MATSim to illustrate tsunami evacuation challenges in two island communities in California, USA, that are impacted by limited escape routes. However, working with MATSim's data preparation, simulation, and visualization modules in an integrated development environment requires a significant investment of time to develop the software expertise to link the modules and run a simulation. To facilitate our evacuation research, we packaged the MATSim modules into a single application tailored to the needs of the hazards community. By exposing the modeling parameters of interest to researchers in an intuitive user interface and hiding the software complexities, we bring agent-based modeling closer to practitioners and provide access to the powerful visual and analytic information that this modeling can provide.

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chassin, David P.; Behboodi, Sahand; Crawford, Curran

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council (WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methodsmore » presented.« less

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

DOE PAGES

Chassin, David P.; Behboodi, Sahand; Crawford, Curran; ...

2015-12-23

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council (WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methodsmore » presented.« less

Integrating macro and micro scale approaches in the agent-based modeling of residential dynamics

NASA Astrophysics Data System (ADS)

Saeedi, Sara

2018-06-01

With the advancement of computational modeling and simulation (M&S) methods as well as data collection technologies, urban dynamics modeling substantially improved over the last several decades. The complex urban dynamics processes are most effectively modeled not at the macro-scale, but following a bottom-up approach, by simulating the decisions of individual entities, or residents. Agent-based modeling (ABM) provides the key to a dynamic M&S framework that is able to integrate socioeconomic with environmental models, and to operate at both micro and macro geographical scales. In this study, a multi-agent system is proposed to simulate residential dynamics by considering spatiotemporal land use changes. In the proposed ABM, macro-scale land use change prediction is modeled by Artificial Neural Network (ANN) and deployed as the agent environment and micro-scale residential dynamics behaviors autonomously implemented by household agents. These two levels of simulation interacted and jointly promoted urbanization process in an urban area of Tehran city in Iran. The model simulates the behavior of individual households in finding ideal locations to dwell. The household agents are divided into three main groups based on their income rank and they are further classified into different categories based on a number of attributes. These attributes determine the households' preferences for finding new dwellings and change with time. The ABM environment is represented by a land-use map in which the properties of the land parcels change dynamically over the simulation time. The outputs of this model are a set of maps showing the pattern of different groups of households in the city. These patterns can be used by city planners to find optimum locations for building new residential units or adding new services to the city. The simulation results show that combining macro- and micro-level simulation can give full play to the potential of the ABM to understand the driving mechanism of urbanization and provide decision-making support for urban management.

Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheppard, Colin; Waraich, Rashid; Campbell, Andrew

This report summarizes the BEAM modeling framework (Behavior, Energy, Mobility, and Autonomy) and its application to simulating plug-in electric vehicle (PEV) mobility, energy consumption, and spatiotemporal charging demand. BEAM is an agent-based model of PEV mobility and charging behavior designed as an extension to MATSim (the Multi-Agent Transportation Simulation model). We apply BEAM to the San Francisco Bay Area and conduct a preliminary calibration and validation of its prediction of charging load based on observed charging infrastructure utilization for the region in 2016. We then explore the impact of a variety of common modeling assumptions in the literature regarding chargingmore » infrastructure availability and driver behavior. We find that accurately reproducing observed charging patterns requires an explicit representation of spatially disaggregated charging infrastructure as well as a more nuanced model of the decision to charge that balances tradeoffs people make with regards to time, cost, convenience, and range anxiety.« less

Agent Based Modeling Applications for Geosciences

NASA Astrophysics Data System (ADS)

Stein, J. S.

2004-12-01

Agent-based modeling techniques have successfully been applied to systems in which complex behaviors or outcomes arise from varied interactions between individuals in the system. Each individual interacts with its environment, as well as with other individuals, by following a set of relatively simple rules. Traditionally this "bottom-up" modeling approach has been applied to problems in the fields of economics and sociology, but more recently has been introduced to various disciplines in the geosciences. This technique can help explain the origin of complex processes from a relatively simple set of rules, incorporate large and detailed datasets when they exist, and simulate the effects of extreme events on system-wide behavior. Some of the challenges associated with this modeling method include: significant computational requirements in order to keep track of thousands to millions of agents, methods and strategies of model validation are lacking, as is a formal methodology for evaluating model uncertainty. Challenges specific to the geosciences, include how to define agents that control water, contaminant fluxes, climate forcing and other physical processes and how to link these "geo-agents" into larger agent-based simulations that include social systems such as demographics economics and regulations. Effective management of limited natural resources (such as water, hydrocarbons, or land) requires an understanding of what factors influence the demand for these resources on a regional and temporal scale. Agent-based models can be used to simulate this demand across a variety of sectors under a range of conditions and determine effective and robust management policies and monitoring strategies. The recent focus on the role of biological processes in the geosciences is another example of an area that could benefit from agent-based applications. A typical approach to modeling the effect of biological processes in geologic media has been to represent these processes in a thermodynamic framework as a set of reactions that roll-up the integrated effect that diverse biological communities exert on a geological system. This approach may work well to predict the effect of certain biological communities in specific environments in which experimental data is available. However, it does not further our knowledge of how the geobiological system actually functions on a micro scale. Agent-based techniques may provide a framework to explore the fundamental interactions required to explain the system-wide behavior. This presentation will present a survey of several promising applications of agent-based modeling approaches to problems in the geosciences and describe specific contributions to some of the inherent challenges facing this approach.

Agent-based modeling of the immune system: NetLogo, a promising framework.

PubMed

Chiacchio, Ferdinando; Pennisi, Marzio; Russo, Giulia; Motta, Santo; Pappalardo, Francesco

2014-01-01

Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.

Pedestrian simulation and distribution in urban space based on visibility analysis and agent simulation

NASA Astrophysics Data System (ADS)

Ying, Shen; Li, Lin; Gao, Yurong

2009-10-01

Spatial visibility analysis is the important direction of pedestrian behaviors because our visual conception in space is the straight method to get environment information and navigate your actions. Based on the agent modeling and up-tobottom method, the paper develop the framework about the analysis of the pedestrian flow depended on visibility. We use viewshed in visibility analysis and impose the parameters on agent simulation to direct their motion in urban space. We analyze the pedestrian behaviors in micro-scale and macro-scale of urban open space. The individual agent use visual affordance to determine his direction of motion in micro-scale urban street on district. And we compare the distribution of pedestrian flow with configuration in macro-scale urban environment, and mine the relationship between the pedestrian flow and distribution of urban facilities and urban function. The paper first computes the visibility situations at the vantage point in urban open space, such as street network, quantify the visibility parameters. The multiple agents use visibility parameters to decide their direction of motion, and finally pedestrian flow reach to a stable state in urban environment through the simulation of multiple agent system. The paper compare the morphology of visibility parameters and pedestrian distribution with urban function and facilities layout to confirm the consistence between them, which can be used to make decision support in urban design.

Contrast Gradient-Based Blood Velocimetry With Computed Tomography: Theory, Simulations, and Proof of Principle in a Dynamic Flow Phantom.

PubMed

Korporaal, Johannes G; Benz, Matthias R; Schindera, Sebastian T; Flohr, Thomas G; Schmidt, Bernhard

2016-01-01

The aim of this study was to introduce a new theoretical framework describing the relationship between the blood velocity, computed tomography (CT) acquisition velocity, and iodine contrast enhancement in CT images, and give a proof of principle of contrast gradient-based blood velocimetry with CT. The time-averaged blood velocity (v(blood)) inside an artery along the axis of rotation (z axis) is described as the mathematical division of a temporal (Hounsfield unit/second) and spatial (Hounsfield unit/centimeter) iodine contrast gradient. From this new theoretical framework, multiple strategies for calculating the time-averaged blood velocity from existing clinical CT scan protocols are derived, and contrast gradient-based blood velocimetry was introduced as a new method that can calculate v(blood) directly from contrast agent gradients and the changes therein. Exemplarily, the behavior of this new method was simulated for image acquisition with an adaptive 4-dimensional spiral mode consisting of repeated spiral acquisitions with alternating scan direction. In a dynamic flow phantom with flow velocities between 5.1 and 21.2 cm/s, the same acquisition mode was used to validate the simulations and give a proof of principle of contrast gradient-based blood velocimetry in a straight cylinder of 2.5 cm diameter, representing the aorta. In general, scanning with the direction of blood flow results in decreased and scanning against the flow in increased temporal contrast agent gradients. Velocity quantification becomes better for low blood and high acquisition speeds because the deviation of the measured contrast agent gradient from the temporal gradient will increase. In the dynamic flow phantom, a modulation of the enhancement curve, and thus alternation of the contrast agent gradients, can be observed for the adaptive 4-dimensional spiral mode and is in agreement with the simulations. The measured flow velocities in the downslopes of the enhancement curves were in good agreement with the expected values, although the accuracy and precision worsened with increasing flow velocities. The new theoretical framework increases the understanding of the relationship between the blood velocity, CT acquisition velocity, and iodine contrast enhancement in CT images, and it interconnects existing blood velocimetry methods with research on transluminary attenuation gradients. With these new insights, novel strategies for CT blood velocimetry, such as the contrast gradient-based method presented in this article, may be developed.

A proposal for a computer-based framework of support for public health in the management of biological incidents: the Czech Republic experience.

PubMed

Bures, Vladimír; Otcenásková, Tereza; Cech, Pavel; Antos, Karel

2012-11-01

Biological incidents jeopardising public health require decision-making that consists of one dominant feature: complexity. Therefore, public health decision-makers necessitate appropriate support. Based on the analogy with business intelligence (BI) principles, the contextual analysis of the environment and available data resources, and conceptual modelling within systems and knowledge engineering, this paper proposes a general framework for computer-based decision support in the case of a biological incident. At the outset, the analysis of potential inputs to the framework is conducted and several resources such as demographic information, strategic documents, environmental characteristics, agent descriptors and surveillance systems are considered. Consequently, three prototypes were developed, tested and evaluated by a group of experts. Their selection was based on the overall framework scheme. Subsequently, an ontology prototype linked with an inference engine, multi-agent-based model focusing on the simulation of an environment, and expert-system prototypes were created. All prototypes proved to be utilisable support tools for decision-making in the field of public health. Nevertheless, the research revealed further issues and challenges that might be investigated by both public health focused researchers and practitioners.

An Agent-based Model for Groundwater Allocation and Management at the Bakken Shale in Western North Dakota

NASA Astrophysics Data System (ADS)

Lin, T.; Lin, Z.; Lim, S.

2017-12-01

We present an integrated modeling framework to simulate groundwater level change under the dramatic increase of hydraulic fracturing water use in the Bakken Shale oil production area. The framework combines the agent-based model (ABM) with the Fox Hills-Hell Creek (FH-HC) groundwater model. In development of the ABM, institution theory is used to model the regulation policies from the North Dakota State Water Commission, while evolutionary programming and cognitive maps are used to model the social structure that emerges from the behavior of competing individual water businesses. Evolutionary programming allows individuals to select an appropriate strategy when annually applying for potential water use permits; whereas cognitive maps endow agent's ability and willingness to compete for more water sales. All agents have their own influence boundaries that inhibit their competitive behavior toward their neighbors but not to non-neighbors. The decision-making process is constructed and parameterized with both quantitative and qualitative information, i.e., empirical water use data and knowledge gained from surveys with stakeholders. By linking institution theory, evolutionary programming, and cognitive maps, our approach addresses a higher complexity of the real decision making process. Furthermore, this approach is a new exploration for modeling the dynamics of Coupled Human and Natural System. After integrating ABM with the FH-HC model, drought and limited water accessibility scenarios are simulated to predict FH-HC ground water level changes in the future. The integrated modeling framework of ABM and FH-HC model can be used to support making scientifically sound policies in water allocation and management.

A framework for the use of agent based modeling to simulate ...

EPA Pesticide Factsheets

Simulation of human behavior in exposure modeling is a complex task. Traditionally, inter-individual variation in human activity has been modeled by drawing from a pool of single day time-activity diaries such as the US EPA Consolidated Human Activity Database (CHAD). Here, an agent-based model (ABM) is used to simulate population distributions of longitudinal patterns of four macro activities (sleeping, eating, working, and commuting) in populations of adults over a period of one year. In this ABM, an individual is modeled as an agent whose movement through time and space is determined by a set of decision rules. The rules are based on the agent having time-varying “needs” that are satisfied by performing actions. Needs are modeled as increasing over time, and taking an action reduces the need. Need-satisfying actions include sleeping (meeting the need for rest), eating (meeting the need for food), and commuting/working (meeting the need for income). Every time an action is completed, the model determines the next action the agent will take based on the magnitude of each of the agent’s needs at that point in time. Different activities advertise their ability to satisfy various needs of the agent (such as food to eat or sleeping in a bed or on a couch). The model then chooses the activity that satisfies the greatest of the agent’s needs. When multiple actions could address a need, the model will choose the most effective of the actions (bed over the couc

Understanding agent-based models of financial markets: A bottom-up approach based on order parameters and phase diagrams

NASA Astrophysics Data System (ADS)

Lye, Ribin; Tan, James Peng Lung; Cheong, Siew Ann

2012-11-01

We describe a bottom-up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We illustrate this framework by starting with a deterministic toy model, whereby N independent traders buy and sell M stocks through an order book that acts as a clearing house. The price of a stock increases whenever it is bought and decreases whenever it is sold. Price changes are updated by the order book before the next transaction takes place. In this deterministic model, all traders based their buy decisions on a call utility function, and all their sell decisions on a put utility function. We then make the agent-based model more realistic, by either having a fraction fb of traders buy a random stock on offer, or a fraction fs of traders sell a random stock in their portfolio. Based on our simulations, we find that it is possible to identify useful order parameters from the steady-state price distributions of all three models. Using these order parameters as a guide, we find three phases: (i) the dead market; (ii) the boom market; and (iii) the jammed market in the phase diagram of the deterministic model. Comparing the phase diagrams of the stochastic models against that of the deterministic model, we realize that the primary effect of stochasticity is to eliminate the dead market phase.

Stationary average consensus protocol for a class of heterogeneous high-order multi-agent systems with application for aircraft

NASA Astrophysics Data System (ADS)

Rezaei, Mohammad Hadi; Menhaj, Mohammad Bagher

2018-01-01

This paper investigates the stationary average consensus problem for a class of heterogeneous-order multi-agent systems. The goal is to bring the positions of agents to the average of their initial positions while letting the other states converge to zero. To this end, three different consensus protocols are proposed. First, based on the auxiliary variables information among the agents under switching directed networks and state-feedback control, a protocol is proposed whereby all the agents achieve stationary average consensus. In the second and third protocols, by resorting to only measurements of relative positions of neighbouring agents under fixed balanced directed networks, two control frameworks are presented with two strategies based on state-feedback and output-feedback control. Finally, simulation results are given to illustrate the effectiveness of the proposed protocols.

A Scaffolding Framework to Support Learning of Emergent Phenomena Using Multi-Agent-Based Simulation Environments

ERIC Educational Resources Information Center

Basu, Satabdi; Sengupta, Pratim; Biswas, Gautam

2015-01-01

Students from middle school to college have difficulties in interpreting and understanding complex systems such as ecological phenomena. Researchers have suggested that students experience difficulties in reconciling the relationships between individuals, populations, and species, as well as the interactions between organisms and their environment…

Development of agent-based on-line adaptive signal control (ASC) framework using connected vehicle (CV) technology.

DOT National Transportation Integrated Search

2016-04-01

In this study, we developed an adaptive signal control (ASC) framework for connected vehicles (CVs) using agent-based modeling technique. : The proposed framework consists of two types of agents: 1) vehicle agents (VAs); and 2) signal controller agen...

Unified Simulation and Analysis Framework for Deep Space Navigation Design

NASA Technical Reports Server (NTRS)

Anzalone, Evan; Chuang, Jason; Olsen, Carrie

2013-01-01

As the technology that enables advanced deep space autonomous navigation continues to develop and the requirements for such capability continues to grow, there is a clear need for a modular expandable simulation framework. This tool's purpose is to address multiple measurement and information sources in order to capture system capability. This is needed to analyze the capability of competing navigation systems as well as to develop system requirements, in order to determine its effect on the sizing of the integrated vehicle. The development for such a framework is built upon Model-Based Systems Engineering techniques to capture the architecture of the navigation system and possible state measurements and observations to feed into the simulation implementation structure. These models also allow a common environment for the capture of an increasingly complex operational architecture, involving multiple spacecraft, ground stations, and communication networks. In order to address these architectural developments, a framework of agent-based modules is implemented to capture the independent operations of individual spacecraft as well as the network interactions amongst spacecraft. This paper describes the development of this framework, and the modeling processes used to capture a deep space navigation system. Additionally, a sample implementation describing a concept of network-based navigation utilizing digitally transmitted data packets is described in detail. This developed package shows the capability of the modeling framework, including its modularity, analysis capabilities, and its unification back to the overall system requirements and definition.

Cyber Security Research Frameworks For Coevolutionary Network Defense

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rush, George D.; Tauritz, Daniel Remy

Several architectures have been created for developing and testing systems used in network security, but most are meant to provide a platform for running cyber security experiments as opposed to automating experiment processes. In the first paper, we propose a framework termed Distributed Cyber Security Automation Framework for Experiments (DCAFE) that enables experiment automation and control in a distributed environment. Predictive analysis of adversaries is another thorny issue in cyber security. Game theory can be used to mathematically analyze adversary models, but its scalability limitations restrict its use. Computational game theory allows us to scale classical game theory to larger,more » more complex systems. In the second paper, we propose a framework termed Coevolutionary Agent-based Network Defense Lightweight Event System (CANDLES) that can coevolve attacker and defender agent strategies and capabilities and evaluate potential solutions with a custom network defense simulation. The third paper is a continuation of the CANDLES project in which we rewrote key parts of the framework. Attackers and defenders have been redesigned to evolve pure strategy, and a new network security simulation is devised which specifies network architecture and adds a temporal aspect. We also add a hill climber algorithm to evaluate the search space and justify the use of a coevolutionary algorithm.« less

A coupled modeling framework for sustainable watershed management in transboundary river basins

NASA Astrophysics Data System (ADS)

Furqan Khan, Hassaan; Yang, Y. C. Ethan; Xie, Hua; Ringler, Claudia

2017-12-01

There is a growing recognition among water resource managers that sustainable watershed management needs to not only account for the diverse ways humans benefit from the environment, but also incorporate the impact of human actions on the natural system. Coupled natural-human system modeling through explicit modeling of both natural and human behavior can help reveal the reciprocal interactions and co-evolution of the natural and human systems. This study develops a spatially scalable, generalized agent-based modeling (ABM) framework consisting of a process-based semi-distributed hydrologic model (SWAT) and a decentralized water system model to simulate the impacts of water resource management decisions that affect the food-water-energy-environment (FWEE) nexus at a watershed scale. Agents within a river basin are geographically delineated based on both political and watershed boundaries and represent key stakeholders of ecosystem services. Agents decide about the priority across three primary water uses: food production, hydropower generation and ecosystem health within their geographical domains. Agents interact with the environment (streamflow) through the SWAT model and interact with other agents through a parameter representing willingness to cooperate. The innovative two-way coupling between the water system model and SWAT enables this framework to fully explore the feedback of human decisions on the environmental dynamics and vice versa. To support non-technical stakeholder interactions, a web-based user interface has been developed that allows for role-play and participatory modeling. The generalized ABM framework is also tested in two key transboundary river basins, the Mekong River basin in Southeast Asia and the Niger River basin in West Africa, where water uses for ecosystem health compete with growing human demands on food and energy resources. We present modeling results for crop production, energy generation and violation of eco-hydrological indicators at both the agent and basin-wide levels to shed light on holistic FWEE management policies in these two basins.

Continuous Opinion Dynamics Under Bounded Confidence:. a Survey

NASA Astrophysics Data System (ADS)

Lorenz, Jan

Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al. in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines such as physics, mathematics, computer science, social psychology and philosophy. In these models agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte Carlo style, but they can also be reformulated on the agent's density in the opinion space in a master equation style. The contribution of this survey is fourfold. First, it will present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence. Second, it will give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions. Third, it will review the several extensions and the evolving phenomena which have been studied so far, and fourth it will state some open questions.

A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks

PubMed Central

Ye, Dayon; Zhang, Minji; Yang, Yu

2015-01-01

Wireless sensor networks (WSNs) have been widely investigated in recent years. One of the fundamental issues in WSNs is packet routing, because in many application domains, packets have to be routed from source nodes to destination nodes as soon and as energy efficiently as possible. To address this issue, a large number of routing approaches have been proposed. Although every existing routing approach has advantages, they also have some disadvantages. In this paper, a multi-agent framework is proposed that can assist existing routing approaches to improve their routing performance. This framework enables each sensor node to build a cooperative neighbour set based on past routing experience. Such cooperative neighbours, in turn, can help the sensor to effectively relay packets in the future. This framework is independent of existing routing approaches and can be used to assist many existing routing approaches. Simulation results demonstrate the good performance of this framework in terms of four metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage. PMID:25928063

Modeling the Population Dynamics of Antibiotic-Resistant Bacteria:. AN Agent-Based Approach

NASA Astrophysics Data System (ADS)

Murphy, James T.; Walshe, Ray; Devocelle, Marc

The response of bacterial populations to antibiotic treatment is often a function of a diverse range of interacting factors. In order to develop strategies to minimize the spread of antibiotic resistance in pathogenic bacteria, a sound theoretical understanding of the systems of interactions taking place within a colony must be developed. The agent-based approach to modeling bacterial populations is a useful tool for relating data obtained at the molecular and cellular level with the overall population dynamics. Here we demonstrate an agent-based model, called Micro-Gen, which has been developed to simulate the growth and development of bacterial colonies in culture. The model also incorporates biochemical rules and parameters describing the kinetic interactions of bacterial cells with antibiotic molecules. Simulations were carried out to replicate the development of methicillin-resistant S. aureus (MRSA) colonies growing in the presence of antibiotics. The model was explored to see how the properties of the system emerge from the interactions of the individual bacterial agents in order to achieve a better mechanistic understanding of the population dynamics taking place. Micro-Gen provides a good theoretical framework for investigating the effects of local environmental conditions and cellular properties on the response of bacterial populations to antibiotic exposure in the context of a simulated environment.

Agent-based Modeling to Simulate the Diffusion of Water-Efficient Innovations and the Emergence of Urban Water Sustainability

NASA Astrophysics Data System (ADS)

Kanta, L.; Giacomoni, M.; Shafiee, M. E.; Berglund, E.

2014-12-01

The sustainability of water resources is threatened by urbanization, as increasing demands deplete water availability, and changes to the landscape alter runoff and the flow regime of receiving water bodies. Utility managers typically manage urban water resources through the use of centralized solutions, such as large reservoirs, which may be limited in their ability balance the needs of urbanization and ecological systems. Decentralized technologies, on the other hand, may improve the health of the water resources system and deliver urban water services. For example, low impact development technologies, such as rainwater harvesting, and water-efficient technologies, such as low-flow faucets and toilets, may be adopted by households to retain rainwater and reduce demands, offsetting the need for new centralized infrastructure. Decentralized technologies may create new complexities in infrastructure and water management, as decentralization depends on community behavior and participation beyond traditional water resources planning. Messages about water shortages and water quality from peers and the water utility managers can influence the adoption of new technologies. As a result, feedbacks between consumers and water resources emerge, creating a complex system. This research develops a framework to simulate the diffusion of water-efficient innovations and the sustainability of urban water resources, by coupling models of households in a community, hydrologic models of a water resources system, and a cellular automata model of land use change. Agent-based models are developed to simulate the land use and water demand decisions of individual households, and behavioral rules are encoded to simulate communication with other agents and adoption of decentralized technologies, using a model of the diffusion of innovation. The framework is applied for an illustrative case study to simulate water resources sustainability over a long-term planning horizon.

Estimation of the age-specific per-contact probability of Ebola virus transmission in Liberia using agent-based simulations

NASA Astrophysics Data System (ADS)

Siettos, Constantinos I.; Anastassopoulou, Cleo; Russo, Lucia; Grigoras, Christos; Mylonakis, Eleftherios

2016-06-01

Based on multiscale agent-based computations we estimated the per-contact probability of transmission by age of the Ebola virus disease (EVD) that swept through Liberia from May 2014 to March 2015. For the approximation of the epidemic dynamics we have developed a detailed agent-based model with small-world interactions between individuals categorized by age. For the estimation of the structure of the evolving contact network as well as the per-contact transmission probabilities by age group we exploited the so called Equation-Free framework. Model parameters were fitted to official case counts reported by the World Health Organization (WHO) as well as to recently published data of key epidemiological variables, such as the mean time to death, recovery and the case fatality rate.

A reinforcement learning model of joy, distress, hope and fear

NASA Astrophysics Data System (ADS)

Broekens, Joost; Jacobs, Elmer; Jonker, Catholijn M.

2015-07-01

In this paper we computationally study the relation between adaptive behaviour and emotion. Using the reinforcement learning framework, we propose that learned state utility, ?, models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-based simulation experiments that show that this model replicates psychological and behavioural dynamics of emotion. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework - coupling it to the literature on habituation, development, extinction and hope theory. Our results support the idea that the function of emotion is to provide a complex feedback signal for an organism to adapt its behaviour. Our work is relevant for understanding the relation between emotion and adaptation in animals, as well as for human-robot interaction, in particular how emotional signals can be used to communicate between adaptive agents and humans.

Integrating GIS and ABM to Explore Spatiotemporal Dynamics

NASA Astrophysics Data System (ADS)

Sun, M.; Jiang, Y.; Yang, C.

2013-12-01

Agent-based modeling as a methodology for the bottom-up exploration with the account of adaptive behavior and heterogeneity of system components can help discover the development and pattern of the complex social and environmental system. However, ABM is a computationally intensive process especially when the number of system components becomes large and the agent-agent/agent-environmental interaction is modeled very complex. Most of traditional ABM frameworks developed based on CPU do not have a satisfying computing capacity. To address the problem and as the emergence of advanced techniques, GPU computing with CUDA can provide powerful parallel structure to enable the complex simulation of spatiotemporal dynamics. In this study, we first develop a GPU-based ABM system. Secondly, in order to visualize the dynamics generated from the movement of agent and the change of agent/environmental attributes during the simulation, we integrate GIS into the ABM system. Advanced geovisualization technologies can be utilized for representing the spatiotemporal change events, such as proper 2D/3D maps with state-of-the-art symbols, space-time cube and multiple layers each of which presents pattern in one time-stamp, etc. Thirdly, visual analytics which include interactive tools (e.g. grouping, filtering, linking, etc.) is included in our ABM-GIS system to help users conduct real-time data exploration during the progress of simulation. Analysis like flow analysis and spatial cluster analysis can be integrated according to the geographical problem we want to explore.

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

PubMed Central

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-01-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. PMID:29194393

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.

PubMed

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-12-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

Simulation Models for Socioeconomic Inequalities in Health: A Systematic Review

PubMed Central

Speybroeck, Niko; Van Malderen, Carine; Harper, Sam; Müller, Birgit; Devleesschauwer, Brecht

2013-01-01

Background: The emergence and evolution of socioeconomic inequalities in health involves multiple factors interacting with each other at different levels. Simulation models are suitable for studying such complex and dynamic systems and have the ability to test the impact of policy interventions in silico. Objective: To explore how simulation models were used in the field of socioeconomic inequalities in health. Methods: An electronic search of studies assessing socioeconomic inequalities in health using a simulation model was conducted. Characteristics of the simulation models were extracted and distinct simulation approaches were identified. As an illustration, a simple agent-based model of the emergence of socioeconomic differences in alcohol abuse was developed. Results: We found 61 studies published between 1989 and 2013. Ten different simulation approaches were identified. The agent-based model illustration showed that multilevel, reciprocal and indirect effects of social determinants on health can be modeled flexibly. Discussion and Conclusions: Based on the review, we discuss the utility of using simulation models for studying health inequalities, and refer to good modeling practices for developing such models. The review and the simulation model example suggest that the use of simulation models may enhance the understanding and debate about existing and new socioeconomic inequalities of health frameworks. PMID:24192788

Metal organic frameworks (MOFs) for degradation of nerve agent simulant parathion

USDA-ARS?s Scientific Manuscript database

Parathion, a simulant of nerve agent VX, has been studied for degradation on Fe3+, Fe2+ and zerovalent iron supported on chitosan. Chitosan, a naturally occurring biopolymer derivative of chitin, is a very good adsorbent for many chemicals including metals. Chitosan is used as supporting biopolymer ...

Metal organic frameworks (MOFs) for degrdation of nerve agent simulant parathion

USDA-ARS?s Scientific Manuscript database

Parathion, a simulant of nerve agent VX, has been studied for degradation on Fe3+, Fe2+ and zerovalent iron supported on chitosan. Chitosan, a naturally occurring biopolymer derivative of chitin, is a very good adsorbent for many chemicals including metals. Chitosan is used as supporting biopolymer ...

Agent-based power sharing scheme for active hybrid power sources

NASA Astrophysics Data System (ADS)

Jiang, Zhenhua

The active hybridization technique provides an effective approach to combining the best properties of a heterogeneous set of power sources to achieve higher energy density, power density and fuel efficiency. Active hybrid power sources can be used to power hybrid electric vehicles with selected combinations of internal combustion engines, fuel cells, batteries, and/or supercapacitors. They can be deployed in all-electric ships to build a distributed electric power system. They can also be used in a bulk power system to construct an autonomous distributed energy system. An important aspect in designing an active hybrid power source is to find a suitable control strategy that can manage the active power sharing and take advantage of the inherent scalability and robustness benefits of the hybrid system. This paper presents an agent-based power sharing scheme for active hybrid power sources. To demonstrate the effectiveness of the proposed agent-based power sharing scheme, simulation studies are performed for a hybrid power source that can be used in a solar car as the main propulsion power module. Simulation results clearly indicate that the agent-based control framework is effective to coordinate the various energy sources and manage the power/voltage profiles.

Distributed decision-making in electric power system transmission maintenance scheduling using multi-agent systems (MAS)

NASA Astrophysics Data System (ADS)

Zhang, Zhong

In this work, motivated by the need to coordinate transmission maintenance scheduling among a multiplicity of self-interested entities in restructured power industry, a distributed decision support framework based on multiagent negotiation systems (MANS) is developed. An innovative risk-based transmission maintenance optimization procedure is introduced. Several models for linking condition monitoring information to the equipment's instantaneous failure probability are presented, which enable quantitative evaluation of the effectiveness of maintenance activities in terms of system cumulative risk reduction. Methodologies of statistical processing, equipment deterioration evaluation and time-dependent failure probability calculation are also described. A novel framework capable of facilitating distributed decision-making through multiagent negotiation is developed. A multiagent negotiation model is developed and illustrated that accounts for uncertainty and enables social rationality. Some issues of multiagent negotiation convergence and scalability are discussed. The relationships between agent-based negotiation and auction systems are also identified. A four-step MAS design methodology for constructing multiagent systems for power system applications is presented. A generic multiagent negotiation system, capable of inter-agent communication and distributed decision support through inter-agent negotiations, is implemented. A multiagent system framework for facilitating the automated integration of condition monitoring information and maintenance scheduling for power transformers is developed. Simulations of multiagent negotiation-based maintenance scheduling among several independent utilities are provided. It is shown to be a viable alternative solution paradigm to the traditional centralized optimization approach in today's deregulated environment. This multiagent system framework not only facilitates the decision-making among competing power system entities, but also provides a tool to use in studying competitive industry relative to monopolistic industry.

Non-linear modelling and control of semi-active suspensions with variable damping

NASA Astrophysics Data System (ADS)

Chen, Huang; Long, Chen; Yuan, Chao-Chun; Jiang, Hao-Bin

2013-10-01

Electro-hydraulic dampers can provide variable damping force that is modulated by varying the command current; furthermore, they offer advantages such as lower power, rapid response, lower cost, and simple hardware. However, accurate characterisation of non-linear f-v properties in pre-yield and force saturation in post-yield is still required. Meanwhile, traditional linear or quarter vehicle models contain various non-linearities. The development of a multi-body dynamics model is very complex, and therefore, SIMPACK was used with suitable improvements for model development and numerical simulations. A semi-active suspension was built based on a belief-desire-intention (BDI)-agent model framework. Vehicle handling dynamics were analysed, and a co-simulation analysis was conducted in SIMPACK and MATLAB to evaluate the BDI-agent controller. The design effectively improved ride comfort, handling stability, and driving safety. A rapid control prototype was built based on dSPACE to conduct a real vehicle test. The test and simulation results were consistent, which verified the simulation.

Understanding coupled natural and human systems on fire prone landscapes: integrating wildfire simulation into an agent based planning system.

NASA Astrophysics Data System (ADS)

Barros, Ana; Ager, Alan; Preisler, Haiganoush; Day, Michelle; Spies, Tom; Bolte, John

2015-04-01

Agent-based models (ABM) allow users to examine the long-term effects of agent decisions in complex systems where multiple agents and processes interact. This framework has potential application to study the dynamics of coupled natural and human systems where multiple stimuli determine trajectories over both space and time. We used Envision, a landscape based ABM, to analyze long-term wildfire dynamics in a heterogeneous, multi-owner landscape in Oregon, USA. Landscape dynamics are affected by land management policies, actors decisions, and autonomous processes such as vegetation succession, wildfire, or at a broader scale, climate change. Key questions include: 1) How are landscape dynamics influenced by policies and institutions, and 2) How do land management policies and actor decisions interact to produce intended and unintended consequences with respect to wildfire on fire-prone landscapes. Applying Envision to address these questions required the development of a wildfire module that could accurately simulate wildfires on the heterogeneous landscapes within the study area in terms of replicating historical fire size distribution, spatial distribution and fire intensity. In this paper we describe the development and testing of a mechanistic fire simulation system within Envision and application of the model on a 3.2 million fire prone landscape in central Oregon USA. The core fire spread equations use the Minimum Travel Time algorithm developed by M Finney. The model operates on a daily time step and uses a fire prediction system based on the relationship between energy release component and historical fires. Specifically, daily wildfire probabilities and sizes are generated from statistical analyses of historical fires in relation to daily ERC values. The MTT was coupled with the vegetation dynamics module in Envision to allow communication between the respective subsystem and effectively model fire effects and vegetation dynamics after a wildfire. Canopy and surface fuels are modeled in a state and transition framework that accounts for succession, fire effects, and fuels management. Fire effects are modeled using simulated fire intensity (flame length) to calculate expected vegetation impacts for each vegetation state. This talk will describe the mechanics of the simulation system along with initial results of Envision simulations for the Central Oregon study area that explore the dynamics of wildfire, fuel management, and succession over time.

A framework for identifying water management typologies for agent based modeling of water resources and its application in the Boise River Basin, USA.

NASA Astrophysics Data System (ADS)

Kaiser, K. E.; Flores, A. N.; Hillis, V.; Moroney, J.; Schneider, J.

2017-12-01

Modeling the management of water resources necessitates incorporation of complex social and hydrologic dynamics. Simulation of these socio-ecological systems requires characterization of the decision-making process of relevant actors, the mechanisms through which they exert control on the biophysical system, their ability to react and adapt to regional environmental conditions, and the plausible behaviors in response to changes in those conditions. Agent based models (ABMs) are a useful tool in simulating these complex adaptive systems because they can dynamically couple hydrological models and the behavior of decision making actors. ABMs can provide a flexible, integrated framework that can represent multi-scale interactions, and the heterogeneity of information networks and sources. However, the variability in behavior of water management actors across systems makes characterizing agent behaviors and relationships challenging. Agent typologies, or agent functional types (AFTs), group together individuals and/or agencies with similar functional roles, management objectives, and decision-making strategies. AFTs have been used to represent archetypal land managers in the agricultural and forestry sectors in large-scale socio-economic system models. A similar typology of water actors could simplify the representation of water management across river basins, and increase transferability and scaling of resulting ABMs. Here, we present a framework for identifying and classifying major water actors and show how we will link an ABM of water management to a regional hydrologic model in a western river basin. The Boise River Basin in southwest Idaho is an interesting setting to apply our AFT framework because of the diverse stakeholders and associated management objectives which include managing urban growth pressures and water supply in the face of climate change. Precipitation in the upper basin supplies 90% of the surface water used in the basin, thus managers of the reservoir system (located in the upper basin) must balance flood control for the metropolitan area with water supply for downstream agricultural and hydropower use. Identifying dominant water management typologies that include state and federal agencies will increase the transferability of water management ABMs in the western US.

Combining patient journey modelling and visual multi-agent computer simulation: a framework to improving knowledge translation in a healthcare environment.

PubMed

Curry, Joanne; Fitzgerald, Anneke; Prodan, Ante; Dadich, Ann; Sloan, Terry

2014-01-01

This article focuses on a framework that will investigate the integration of two disparate methodologies: patient journey modelling and visual multi-agent simulation, and its impact on the speed and quality of knowledge translation to healthcare stakeholders. Literature describes patient journey modelling and visual simulation as discrete activities. This paper suggests that their combination and their impact on translating knowledge to practitioners are greater than the sum of the two technologies. The test-bed is ambulatory care and the goal is to determine if this approach can improve health services delivery, workflow, and patient outcomes and satisfaction. The multidisciplinary research team is comprised of expertise in patient journey modelling, simulation, and knowledge translation.

Data-driven agent-based modeling, with application to rooftop solar adoption

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Haifeng; Vorobeychik, Yevgeniy; Letchford, Joshua

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends andmore » provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house- holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.« less

Data-driven agent-based modeling, with application to rooftop solar adoption

DOE PAGES

Zhang, Haifeng; Vorobeychik, Yevgeniy; Letchford, Joshua; ...

2016-01-25

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends andmore » provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house- holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.« less

A stochastic agent-based model of pathogen propagation in dynamic multi-relational social networks

PubMed Central

Khan, Bilal; Dombrowski, Kirk; Saad, Mohamed

2015-01-01

We describe a general framework for modeling and stochastic simulation of epidemics in realistic dynamic social networks, which incorporates heterogeneity in the types of individuals, types of interconnecting risk-bearing relationships, and types of pathogens transmitted across them. Dynamism is supported through arrival and departure processes, continuous restructuring of risk relationships, and changes to pathogen infectiousness, as mandated by natural history; dynamism is regulated through constraints on the local agency of individual nodes and their risk behaviors, while simulation trajectories are validated using system-wide metrics. To illustrate its utility, we present a case study that applies the proposed framework towards a simulation of HIV in artificial networks of intravenous drug users (IDUs) modeled using data collected in the Social Factors for HIV Risk survey. PMID:25859056

An extensible simulation environment and movement metrics for testing walking behavior in agent-based models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paul M. Torrens; Atsushi Nara; Xun Li

2012-01-01

Human movement is a significant ingredient of many social, environmental, and technical systems, yet the importance of movement is often discounted in considering systems complexity. Movement is commonly abstracted in agent-based modeling (which is perhaps the methodological vehicle for modeling complex systems), despite the influence of movement upon information exchange and adaptation in a system. In particular, agent-based models of urban pedestrians often treat movement in proxy form at the expense of faithfully treating movement behavior with realistic agency. There exists little consensus about which method is appropriate for representing movement in agent-based schemes. In this paper, we examine popularly-usedmore » methods to drive movement in agent-based models, first by introducing a methodology that can flexibly handle many representations of movement at many different scales and second, introducing a suite of tools to benchmark agent movement between models and against real-world trajectory data. We find that most popular movement schemes do a relatively poor job of representing movement, but that some schemes may well be 'good enough' for some applications. We also discuss potential avenues for improving the representation of movement in agent-based frameworks.« less

IPA (v1): a framework for agent-based modelling of soil water movement

NASA Astrophysics Data System (ADS)

Mewes, Benjamin; Schumann, Andreas H.

2018-06-01

In the last decade, agent-based modelling (ABM) became a popular modelling technique in social sciences, medicine, biology, and ecology. ABM was designed to simulate systems that are highly dynamic and sensitive to small variations in their composition and their state. As hydrological systems, and natural systems in general, often show dynamic and non-linear behaviour, ABM can be an appropriate way to model these systems. Nevertheless, only a few studies have utilized the ABM method for process-based modelling in hydrology. The percolation of water through the unsaturated soil is highly responsive to the current state of the soil system; small variations in composition lead to major changes in the transport system. Hence, we present a new approach for modelling the movement of water through a soil column: autonomous water agents that transport water through the soil while interacting with their environment as well as with other agents under physical laws.

Simulating the behavior of patients who leave a public hospital emergency department without being seen by a physician: a cellular automaton and agent-based framework.

PubMed

Yousefi, Milad; Yousefi, Moslem; Fogliatto, F S; Ferreira, R P M; Kim, J H

2018-01-11

The objective of this study was to develop an agent based modeling (ABM) framework to simulate the behavior of patients who leave a public hospital emergency department (ED) without being seen (LWBS). In doing so, the study complements computer modeling and cellular automata (CA) techniques to simulate the behavior of patients in an ED. After verifying and validating the model by comparing it with data from a real case study, the significance of four preventive policies including increasing number of triage nurses, fast-track treatment, increasing the waiting room capacity and reducing treatment time were investigated by utilizing ordinary least squares regression. After applying the preventing policies in ED, an average of 42.14% reduction in the number of patients who leave without being seen and 6.05% reduction in the average length of stay (LOS) of patients was reported. This study is the first to apply CA in an ED simulation. Comparing the average LOS before and after applying CA with actual times from emergency department information system showed an 11% improvement. The simulation results indicated that the most effective approach to reduce the rate of LWBS is applying fast-track treatment. The ABM approach represents a flexible tool that can be constructed to reflect any given environment. It is also a support system for decision-makers to assess the relative impact of control strategies.

Simulating the behavior of patients who leave a public hospital emergency department without being seen by a physician: a cellular automaton and agent-based framework

PubMed Central

Yousefi, Milad; Yousefi, Moslem; Fogliatto, F.S.; Ferreira, R.P.M.; Kim, J.H.

2018-01-01

The objective of this study was to develop an agent based modeling (ABM) framework to simulate the behavior of patients who leave a public hospital emergency department (ED) without being seen (LWBS). In doing so, the study complements computer modeling and cellular automata (CA) techniques to simulate the behavior of patients in an ED. After verifying and validating the model by comparing it with data from a real case study, the significance of four preventive policies including increasing number of triage nurses, fast-track treatment, increasing the waiting room capacity and reducing treatment time were investigated by utilizing ordinary least squares regression. After applying the preventing policies in ED, an average of 42.14% reduction in the number of patients who leave without being seen and 6.05% reduction in the average length of stay (LOS) of patients was reported. This study is the first to apply CA in an ED simulation. Comparing the average LOS before and after applying CA with actual times from emergency department information system showed an 11% improvement. The simulation results indicated that the most effective approach to reduce the rate of LWBS is applying fast-track treatment. The ABM approach represents a flexible tool that can be constructed to reflect any given environment. It is also a support system for decision-makers to assess the relative impact of control strategies. PMID:29340526

Design of a multi-agent hydroeconomic model to simulate a complex human-water system: Early insights from the Jordan Water Project

NASA Astrophysics Data System (ADS)

Yoon, J.; Klassert, C. J. A.; Lachaut, T.; Selby, P. D.; Knox, S.; Gorelick, S.; Rajsekhar, D.; Tilmant, A.; Avisse, N.; Harou, J. J.; Gawel, E.; Klauer, B.; Mustafa, D.; Talozi, S.; Sigel, K.

2015-12-01

Our work focuses on development of a multi-agent, hydroeconomic model for purposes of water policy evaluation in Jordan. The model adopts a modular approach, integrating biophysical modules that simulate natural and engineered phenomena with human modules that represent behavior at multiple levels of decision making. The hydrologic modules are developed using spatially-distributed groundwater and surface water models, which are translated into compact simulators for efficient integration into the multi-agent model. For the groundwater model, we adopt a response matrix method approach in which a 3-dimensional MODFLOW model of a complex regional groundwater system is converted into a linear simulator of groundwater response by pre-processing drawdown results from several hundred numerical simulation runs. Surface water models for each major surface water basin in the country are developed in SWAT and similarly translated into simple rainfall-runoff functions for integration with the multi-agent model. The approach balances physically-based, spatially-explicit representation of hydrologic systems with the efficiency required for integration into a complex multi-agent model that is computationally amenable to robust scenario analysis. For the multi-agent model, we explicitly represent human agency at multiple levels of decision making, with agents representing riparian, management, supplier, and water user groups. The agents' decision making models incorporate both rule-based heuristics as well as economic optimization. The model is programmed in Python using Pynsim, a generalizable, open-source object-oriented code framework for modeling network-based water resource systems. The Jordan model is one of the first applications of Pynsim to a real-world water management case study. Preliminary results from a tanker market scenario run through year 2050 are presented in which several salient features of the water system are investigated: competition between urban and private farmer agents, the emergence of a private tanker market, disparities in economic wellbeing to different user groups caused by unique supply conditions, and response of the complex system to various policy interventions.

A Novel Framework for Characterizing Exposure-Related ...

EPA Pesticide Factsheets

Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that is able to simulate longitudinal patterns in behaviors. By basing our ABM upon a needs-based artificial intelligence (AI) system, we create agents that mimic human decisions on these exposure-relevant behaviors. In a case study of adults, we use the AI to predict the inter-individual variation in the start time and duration of four behaviors: sleeping, eating, commuting, and working. The results demonstrate that the ABM can capture both inter-individual variation and how decisions on one behavior can affect subsequent behaviors. Preset NERL's research on the use of agent based modeling in exposure assessments. To obtain feed back on the approach from the leading experts in the field.

Introduction to Architectures: HSCB Information - What It Is and How It Fits (or Doesn’t Fit)

DTIC Science & Technology

2010-10-01

Simulation Interoperability Workshop, 01E- SIW -080 [15] Barry G. Silverman, Gnana Gharathy, Kevin O’Brien, Jason Cornwell, “Human Behavior Models for Agents...Workshop, 10F- SIW -023, September 2010. [17] Christiansen, John H., “A flexible object-based software framework for modelling complex systems with

A Generic, Agent-Based Framework for Design and Development of UAV/UCAV Control Systems

DTIC Science & Technology

2004-02-27

37 EID Principles .................................................................................................. 38 Experimental Support for EID...Year 2 Interface design and implementation; creation of the simulation environment; Year 3 Demonstration of the concept and experimental evaluation...UAV/UCAV control in which operators can experience high cognitive workloads. There are several ways in which systems can construct user models by

The Umbra Simulation and Integration Framework Applied to Emergency Response Training

NASA Technical Reports Server (NTRS)

Hamilton, Paul Lawrence; Britain, Robert

2010-01-01

The Mine Emergency Response Interactive Training Simulation (MERITS) is intended to prepare personnel to manage an emergency in an underground coal mine. The creation of an effective training environment required realistic emergent behavior in response to simulation events and trainee interventions, exploratory modification of miner behavior rules, realistic physics, and incorporation of legacy code. It also required the ability to add rich media to the simulation without conflicting with normal desktop security settings. Our Umbra Simulation and Integration Framework facilitated agent-based modeling of miners and rescuers and made it possible to work with subject matter experts to quickly adjust behavior through script editing, rather than through lengthy programming and recompilation. Integration of Umbra code with the WebKit browser engine allowed the use of JavaScript-enabled local web pages for media support. This project greatly extended the capabilities of Umbra in support of training simulations and has implications for simulations that combine human behavior, physics, and rich media.

Metal-Organic Framework Modified Glass Substrate for Analysis of Highly Volatile Chemical Warfare Agents by Paper Spray Mass Spectrometry.

PubMed

Dhummakupt, Elizabeth S; Carmany, Daniel O; Mach, Phillip M; Tovar, Trenton M; Ploskonka, Ann M; Demond, Paul S; DeCoste, Jared B; Glaros, Trevor

2018-03-07

Paper spray mass spectrometry has been shown to successfully analyze chemical warfare agent (CWA) simulants. However, due to the volatility differences between the simulants and real G-series (i.e., sarin, soman) CWAs, analysis from an untreated paper substrate proved difficult. To extend the analytical lifetime of these G-agents, metal-organic frameworks (MOFs) were successfully integrated onto the paper spray substrates to increase adsorption and desorption. In this study, several MOFs and nanoparticles were tested to extend the analytical lifetimes of sarin, soman, and cyclosarin on paper spray substrates. It was found that the addition of either UiO-66 or HKUST-1 to the paper substrate increased the analytical lifetime of the G-agents from less than 5 min detectability to at least 50 min.

High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair.

PubMed

Seekhao, Nuttiiya; Shung, Caroline; JaJa, Joseph; Mongeau, Luc; Li-Jessen, Nicole Y K

2018-01-01

Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed.

High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair

PubMed Central

Seekhao, Nuttiiya; Shung, Caroline; JaJa, Joseph; Mongeau, Luc; Li-Jessen, Nicole Y. K.

2018-01-01

Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed. PMID:29706894

Multi-agent Simulations of Population Behavior: A Promising Tool for Systems Biology.

PubMed

Colosimo, Alfredo

2018-01-01

This contribution reports on the simulation of some dynamical events observed in the collective behavior of different kinds of populations, ranging from shape-changing cells in a Petri dish to functionally correlated brain areas in vivo. The unifying methodological approach, based upon a Multi-Agent Simulation (MAS) paradigm as incorporated in the NetLogo™ interpreter, is a direct consequence of the cornerstone that simple, individual actions within a population of interacting agents often give rise to complex, collective behavior.The discussion will mainly focus on the emergence and spreading of synchronous activities within the population, as well as on the modulation of the collective behavior exerted by environmental force-fields. A relevant section of this contribution is dedicated to the extension of the MAS paradigm to Brain Network models. In such a general framework some recent applications taken from the direct experience of the author, and exploring the activation patterns characteristic of specific brain functional states, are described, and their impact on the Systems-Biology universe underlined.

An Agent-Based Optimization Framework for Engineered Complex Adaptive Systems with Application to Demand Response in Electricity Markets

NASA Astrophysics Data System (ADS)

Haghnevis, Moeed

The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.

KODAMA and VPC based Framework for Ubiquitous Systems and its Experiment

NASA Astrophysics Data System (ADS)

Takahashi, Kenichi; Amamiya, Satoshi; Iwao, Tadashige; Zhong, Guoqiang; Kainuma, Tatsuya; Amamiya, Makoto

Recently, agent technologies have attracted a lot of interest as an emerging programming paradigm. With such agent technologies, services are provided through collaboration among agents. At the same time, the spread of mobile technologies and communication infrastructures has made it possible to access the network anytime and from anywhere. Using agents and mobile technologies to realize ubiquitous computing systems, we propose a new framework based on KODAMA and VPC. KODAMA provides distributed management mechanisms by using the concept of community and communication infrastructure to deliver messages among agents without agents being aware of the physical network. VPC provides a method of defining peer-to-peer services based on agent communication with policy packages. By merging the characteristics of both KODAMA and VPC functions, we propose a new framework for ubiquitous computing environments. It provides distributed management functions according to the concept of agent communities, agent communications which are abstracted from the physical environment, and agent collaboration with policy packages. Using our new framework, we conducted a large-scale experiment in shopping malls in Nagoya, which sent advertisement e-mails to users' cellular phones according to user location and attributes. The empirical results showed that our new framework worked effectively for sales in shopping malls.

Combining Bayesian Networks and Agent Based Modeling to develop a decision-support model in Vietnam

NASA Astrophysics Data System (ADS)

Nong, Bao Anh; Ertsen, Maurits; Schoups, Gerrit

2016-04-01

Complexity and uncertainty in natural resources management have been focus themes in recent years. Within these debates, with the aim to define an approach feasible for water management practice, we are developing an integrated conceptual modeling framework for simulating decision-making processes of citizens, in our case in the Day river area, Vietnam. The model combines Bayesian Networks (BNs) and Agent-Based Modeling (ABM). BNs are able to combine both qualitative data from consultants / experts / stakeholders, and quantitative data from observations on different phenomena or outcomes from other models. Further strengths of BNs are that the relationship between variables in the system is presented in a graphical interface, and that components of uncertainty are explicitly related to their probabilistic dependencies. A disadvantage is that BNs cannot easily identify the feedback of agents in the system once changes appear. Hence, ABM was adopted to represent the reaction among stakeholders under changes. The modeling framework is developed as an attempt to gain better understanding about citizen's behavior and factors influencing their decisions in order to reduce uncertainty in the implementation of water management policy.

Coupling Agent-Based and Groundwater Modeling to Explore Demand Management Strategies for Shared Resources

NASA Astrophysics Data System (ADS)

Al-Amin, S.

2015-12-01

Municipal water demands in growing population centers in the arid southwest US are typically met through increased groundwater withdrawals. Hydro-climatic uncertainties attributed to climate change and land use conversions may also alter demands and impact the replenishment of groundwater supply. Groundwater aquifers are not necessarily confined within municipal and management boundaries, and multiple diverse agencies may manage a shared resource in a decentralized approach, based on individual concerns and resources. The interactions among water managers, consumers, and the environment influence the performance of local management strategies and regional groundwater resources. This research couples an agent-based modeling (ABM) framework and a groundwater model to analyze the effects of different management approaches on shared groundwater resources. The ABM captures the dynamic interactions between household-level consumers and policy makers to simulate water demands under climate change and population growth uncertainties. The groundwater model is used to analyze the relative effects of management approaches on reducing demands and replenishing groundwater resources. The framework is applied for municipalities located in the Verde River Basin, Arizona that withdraw groundwater from the Verde Formation-Basin Fill-Carbonate aquifer system. Insights gained through this simulation study can be used to guide groundwater policy-making under changing hydro-climatic scenarios for a long-term planning horizon.

What makes virtual agents believable?

NASA Astrophysics Data System (ADS)

Bogdanovych, Anton; Trescak, Tomas; Simoff, Simeon

2016-01-01

In this paper we investigate the concept of believability and make an attempt to isolate individual characteristics (features) that contribute to making virtual characters believable. As the result of this investigation we have produced a formalisation of believability and based on this formalisation built a computational framework focused on simulation of believable virtual agents that possess the identified features. In order to test whether the identified features are, in fact, responsible for agents being perceived as more believable, we have conducted a user study. In this study we tested user reactions towards the virtual characters that were created for a simulation of aboriginal inhabitants of a particular area of Sydney, Australia in 1770 A.D. The participants of our user study were exposed to short simulated scenes, in which virtual agents performed some behaviour in two different ways (while possessing a certain aspect of believability vs. not possessing it). The results of the study indicate that virtual agents that appear resource bounded, are aware of their environment, own interaction capabilities and their state in the world, agents that can adapt to changes in the environment and exist in correct social context are those that are being perceived as more believable. Further in the paper we discuss these and other believability features and provide a quantitative analysis of the level of contribution for each such feature to the overall perceived believability of a virtual agent.

Simulation-Based Valuation of Transactive Energy Systems

DOE PAGES

Huang, Qiuhua; McDermott, Tom; Tang, Yingying; ...

2018-05-18

Transactive Energy (TE) has been recognized as a promising technique for integrating responsive loads and distributed energy resources as well as advancing grid modernization. To help the industry better understand the value of TE and compare different TE schemes in a systematic and transparent manner, a comprehensive simulation-based TE valuation method is developed. The method has the following salient features: 1) it formally defines the valuation scenarios, use cases, baseline and valuation metrics; 2) an open-source simulation platform for transactive energy systems has been developed by integrating transmission, distribution and building simulators, and plugin TE and non-TE agents through themore » Framework for Network Co-Simulation (FNCS); 3) transparency and flexibility of the valuation is enhanced through separation of simulation and valuation, base valuation metrics and final valuation metrics. In conclusion, a valuation example based on the Smart Grid Interoperability Panel (SGIP) Use Case 1 is provided to demonstrate the developed TE simulation program and the valuation method.« less

Simulation-Based Valuation of Transactive Energy Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Qiuhua; McDermott, Tom; Tang, Yingying

Transactive Energy (TE) has been recognized as a promising technique for integrating responsive loads and distributed energy resources as well as advancing grid modernization. To help the industry better understand the value of TE and compare different TE schemes in a systematic and transparent manner, a comprehensive simulation-based TE valuation method is developed. The method has the following salient features: 1) it formally defines the valuation scenarios, use cases, baseline and valuation metrics; 2) an open-source simulation platform for transactive energy systems has been developed by integrating transmission, distribution and building simulators, and plugin TE and non-TE agents through themore » Framework for Network Co-Simulation (FNCS); 3) transparency and flexibility of the valuation is enhanced through separation of simulation and valuation, base valuation metrics and final valuation metrics. In conclusion, a valuation example based on the Smart Grid Interoperability Panel (SGIP) Use Case 1 is provided to demonstrate the developed TE simulation program and the valuation method.« less

A minimally sufficient model for rib proximal-distal patterning based on genetic analysis and agent-based simulations

PubMed Central

Mah, In Kyoung

2017-01-01

For decades, the mechanism of skeletal patterning along a proximal-distal axis has been an area of intense inquiry. Here, we examine the development of the ribs, simple structures that in most terrestrial vertebrates consist of two skeletal elements—a proximal bone and a distal cartilage portion. While the ribs have been shown to arise from the somites, little is known about how the two segments are specified. During our examination of genetically modified mice, we discovered a series of progressively worsening phenotypes that could not be easily explained. Here, we combine genetic analysis of rib development with agent-based simulations to conclude that proximal-distal patterning and outgrowth could occur based on simple rules. In our model, specification occurs during somite stages due to varying Hedgehog protein levels, while later expansion refines the pattern. This framework is broadly applicable for understanding the mechanisms of skeletal patterning along a proximal-distal axis. PMID:29068314

Simulation Modeling of Resilience Assessment in Indonesian Fertiliser Industry Supply Networks

NASA Astrophysics Data System (ADS)

Utami, I. D.; Holt, R. J.; McKay, A.

2018-01-01

Supply network resilience is a significant aspect in the performance of the Indonesian fertiliser industry. Decision makers use risk assessment and port management reports to evaluate the availability of infrastructure. An opportunity was identified to incorporate both types of data into an approach for the measurement of resilience. A framework, based on a synthesis of literature and interviews with industry practitioners, covering both social and technical factors is introduced. A simulation model was then built to allow managers to explore implications for resilience and predict levels of risk in different scenarios. Result of interview with respondens from Indonesian fertiliser industry indicated that the simulation model could be valuable in the assessment. This paper provides details of the simulation model for decision makers to explore levels of risk in supply networks. For practitioners, the model could be used by government to assess the current condition of supply networks in Indonesian industries. On the other hand, for academia, the approach provides a new application of agent-based models in research on supply network resilience and presents a real example of how agent-based modeling could be used as to support the assessment approach.

Coordinating teams of autonomous vehicles: an architectural perspective

NASA Astrophysics Data System (ADS)

Czichon, Cary; Peterson, Robert W.; Mettala, Erik G.; Vondrak, Ivo

2005-05-01

In defense-related robotics research, a mission level integration gap exists between mission tasks (tactical) performed by ground, sea, or air applications and elementary behaviors enacted by processing, communications, sensors, and weaponry resources (platform specific). The gap spans ensemble (heterogeneous team) behaviors, automatic MOE/MOP tracking, and tactical task modeling/simulation for virtual and mixed teams comprised of robotic and human combatants. This study surveys robotic system architectures, compares approaches for navigating problem/state spaces by autonomous systems, describes an architecture for an integrated, repository-based modeling, simulation, and execution environment, and outlines a multi-tiered scheme for robotic behavior components that is agent-based, platform-independent, and extendable via plug-ins. Tools for this integrated environment, along with a distributed agent framework for collaborative task performance are being developed by a U.S. Army funded SBIR project (RDECOM Contract N61339-04-C-0005).

Self-Organization of Vocabularies under Different Interaction Orders.

PubMed

Vera, Javier

2017-01-01

Traditionally, the formation of vocabularies has been studied by agent-based models (primarily, the naming game) in which random pairs of agents negotiate word-meaning associations at each discrete time step. This article proposes a first approximation to a novel question: To what extent is the negotiation of word-meaning associations influenced by the order in which agents interact? Automata networks provide the adequate mathematical framework to explore this question. Computer simulations suggest that on two-dimensional lattices the typical features of the formation of word-meaning associations are recovered under random schemes that update small fractions of the population at the same time; by contrast, if larger subsets of the population are updated, a periodic behavior may appear.

Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes.

PubMed

Dong, Xu; Foteinou, Panagiota T; Calvano, Steven E; Lowry, Stephen F; Androulakis, Ioannis P

2010-02-18

Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions. An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self-limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades. The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system.

Distributed collaborative environments for virtual capability-based planning

NASA Astrophysics Data System (ADS)

McQuay, William K.

2003-09-01

Distributed collaboration is an emerging technology that will significantly change how decisions are made in the 21st century. Collaboration involves two or more geographically dispersed individuals working together to share and exchange data, information, knowledge, and actions. The marriage of information, collaboration, and simulation technologies provides the decision maker with a collaborative virtual environment for planning and decision support. This paper reviews research that is focusing on the applying open standards agent-based framework with integrated modeling and simulation to a new Air Force initiative in capability-based planning and the ability to implement it in a distributed virtual environment. Virtual Capability Planning effort will provide decision-quality knowledge for Air Force resource allocation and investment planning including examining proposed capabilities and cost of alternative approaches, the impact of technologies, identification of primary risk drivers, and creation of executable acquisition strategies. The transformed Air Force business processes are enabled by iterative use of constructive and virtual modeling, simulation, and analysis together with information technology. These tools are applied collaboratively via a technical framework by all the affected stakeholders - warfighter, laboratory, product center, logistics center, test center, and primary contractor.

Consensus for linear multi-agent system with intermittent information transmissions using the time-scale theory

NASA Astrophysics Data System (ADS)

Taousser, Fatima; Defoort, Michael; Djemai, Mohamed

2016-01-01

This paper investigates the consensus problem for linear multi-agent system with fixed communication topology in the presence of intermittent communication using the time-scale theory. Since each agent can only obtain relative local information intermittently, the proposed consensus algorithm is based on a discontinuous local interaction rule. The interaction among agents happens at a disjoint set of continuous-time intervals. The closed-loop multi-agent system can be represented using mixed linear continuous-time and linear discrete-time models due to intermittent information transmissions. The time-scale theory provides a powerful tool to combine continuous-time and discrete-time cases and study the consensus protocol under a unified framework. Using this theory, some conditions are derived to achieve exponential consensus under intermittent information transmissions. Simulations are performed to validate the theoretical results.

BTFS: The Border Trade Facilitation System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phillips, L.R.

The author demonstrates the Border Trade Facilitation System (BTFS), an agent-based bilingual e-commerce system built to expedite the regulation, control, and execution of commercial trans-border shipments during the delivery phase. The system was built to serve maquila industries at the US/Mexican border. The BTFS uses foundation technology developed here at Sandia Laboratories' Advanced Information Systems Lab (AISL), including a distributed object substrate, a general-purpose agent development framework, dynamically generated agent-human interaction via the World-Wide Web, and a collaborative agent architecture. This technology is also the substrate for the Multi-Agent Simulation Management System (MASMAS) proposed for demonstration at this conference. Themore » BTFS executes authenticated transactions among agents performing open trading over the Internet. With the BTFS in place, one could conduct secure international transactions from any site with an Internet connection and a web browser. The BTFS is currently being evaluated for commercialization.« less

Fast and Sustained Degradation of Chemical Warfare Agent Simulants Using Flexible Self-Supported Metal-Organic Framework Filters.

PubMed

Liang, Huixin; Yao, Aonan; Jiao, Xiuling; Li, Cheng; Chen, Dairong

2018-06-20

Self-detoxification filters against lethal chemical warfare agents (CWAs) are highly desirable for the protection of human beings and the environment. In this report, flexible self-supported filters of a series of Zr(IV)-based metal-organic frameworks (MOFs) including UiO-66, UiO-67, and UiO-66-NH 2 were successfully prepared and exhibited fast and sustained degradation of CWA simulants. A half-life as short as 2.4 min was obtained for the catalytic hydrolysis of dimethyl 4-nitrophenyl phosphate, and the percent conversion remained above 90% over a long-term exposure of 120 min, well exceeding those of the previously reported composite MOF filters and the corresponding MOF powders. The outstanding detoxification performance of the self-supported fibrous filter comes from the exceptionally high surface area, excellent pore accessibility, and hierarchical structure from the nano- to macroscale. This work demonstrates, for the first time, MOF-only filters as efficient self-detoxification media, which will offer new opportunities for the design and fabrication of functional materials for toxic chemical protection.

Compartmental and Spatial Rule-Based Modeling with Virtual Cell.

PubMed

Blinov, Michael L; Schaff, James C; Vasilescu, Dan; Moraru, Ion I; Bloom, Judy E; Loew, Leslie M

2017-10-03

In rule-based modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced rule-based modeling into the Virtual Cell (VCell) modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial modeling of rule-based models has been implemented within VCell. To enable rule-based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new rule-based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the rule-based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations deterministic solvers or the Smoldyn stochastic simulator. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

An agent-based hydroeconomic model to evaluate water policies in Jordan

NASA Astrophysics Data System (ADS)

Yoon, J.; Gorelick, S.

2014-12-01

Modern water systems can be characterized by a complex network of institutional and private actors that represent competing sectors and interests. Identifying solutions to enhance water security in such systems calls for analysis that can adequately account for this level of complexity and interaction. Our work focuses on the development of a hierarchical, multi-agent, hydroeconomic model that attempts to realistically represent complex interactions between hydrologic and multi-faceted human systems. The model is applied to Jordan, one of the most water-poor countries in the world. In recent years, the water crisis in Jordan has escalated due to an ongoing drought and influx of refugees from regional conflicts. We adopt a modular approach in which biophysical modules simulate natural and engineering phenomena, and human modules represent behavior at multiple scales of decision making. The human modules employ agent-based modeling, in which agents act as autonomous decision makers at the transboundary, state, organizational, and user levels. A systematic nomenclature and conceptual framework is used to characterize model agents and modules. Concepts from the Unified Modeling Language (UML) are adopted to promote clear conceptualization of model classes and process sequencing, establishing a foundation for full deployment of the integrated model in a scalable object-oriented programming environment. Although the framework is applied to the Jordanian water context, it is generalizable to other regional human-natural freshwater supply systems.

Incorporating individual health-protective decisions into disease transmission models: a mathematical framework.

PubMed

Durham, David P; Casman, Elizabeth A

2012-03-07

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak.

Incorporating individual health-protective decisions into disease transmission models: a mathematical framework

PubMed Central

Durham, David P.; Casman, Elizabeth A.

2012-01-01

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak. PMID:21775324

Discrete Element Framework for Modelling Extracellular Matrix, Deformable Cells and Subcellular Components.

PubMed

Gardiner, Bruce S; Wong, Kelvin K L; Joldes, Grand R; Rich, Addison J; Tan, Chin Wee; Burgess, Antony W; Smith, David W

2015-10-01

This paper presents a framework for modelling biological tissues based on discrete particles. Cell components (e.g. cell membranes, cell cytoskeleton, cell nucleus) and extracellular matrix (e.g. collagen) are represented using collections of particles. Simple particle to particle interaction laws are used to simulate and control complex physical interaction types (e.g. cell-cell adhesion via cadherins, integrin basement membrane attachment, cytoskeletal mechanical properties). Particles may be given the capacity to change their properties and behaviours in response to changes in the cellular microenvironment (e.g., in response to cell-cell signalling or mechanical loadings). Each particle is in effect an 'agent', meaning that the agent can sense local environmental information and respond according to pre-determined or stochastic events. The behaviour of the proposed framework is exemplified through several biological problems of ongoing interest. These examples illustrate how the modelling framework allows enormous flexibility for representing the mechanical behaviour of different tissues, and we argue this is a more intuitive approach than perhaps offered by traditional continuum methods. Because of this flexibility, we believe the discrete modelling framework provides an avenue for biologists and bioengineers to explore the behaviour of tissue systems in a computational laboratory.

Towards a Holistic Framework for the Evaluation of Emergency Plans in Indoor Environments

PubMed Central

Serrano, Emilio; Poveda, Geovanny; Garijo, Mercedes

2014-01-01

One of the most promising fields for ambient intelligence is the implementation of intelligent emergency plans. Because the use of drills and living labs cannot reproduce social behaviors, such as panic attacks, that strongly affect these plans, the use of agent-based social simulation provides an approach to evaluate these plans more thoroughly. (1) The hypothesis presented in this paper is that there has been little interest in describing the key modules that these simulators must include, such as formally represented knowledge and a realistic simulated sensor model, and especially in providing researchers with tools to reuse, extend and interconnect modules from different works. This lack of interest hinders researchers from achieving a holistic framework for evaluating emergency plans and forces them to reconsider and to implement the same components from scratch over and over. In addition to supporting this hypothesis by considering over 150 simulators, this paper: (2) defines the main modules identified and proposes the use of semantic web technologies as a cornerstone for the aforementioned holistic framework; (3) provides a basic methodology to achieve the framework; (4) identifies the main challenges; and (5) presents an open and free software tool to hint at the potential of such a holistic view of emergency plan evaluation in indoor environments. PMID:24662453

An experimental-computational platform for investigating microbial interactions and dynamics in communities with two codependent species

NASA Astrophysics Data System (ADS)

Fuentes-Cabrera, Miguel; Anderson, John D.; Wilmoth, Jared; Ginovart, Marta; Prats, Clara; Portell-Canal, Xavier; Retterer, Scott

Microbial interactions are critical for governing community behavior and structure in natural environments. Examination of microbial interactions in the lab involves growth under ideal conditions in batch culture; conditions that occur in nature are, however, characterized by disequilibrium. Of particular interest is the role that system variables play in shaping cell-to-cell interactions and organization at ultrafine spatial scales. We seek to use experiments and agent-based modeling to help discover mechanisms relevant to microbial dynamics and interactions in the environment. Currently, we are using an agent-based model to simulate microbial growth, dynamics and interactions that occur on a microwell-array device developed in our lab. Bacterial cells growing in the microwells of this platform can be studied with high-throughput and high-content image analyses using brightfield and fluorescence microscopy. The agent-based model is written in the language Netlogo, which in turn is ''plugged into'' a computational framework that allows submitting many calculations in parallel for different initial parameters; visualizing the outcomes in an interactive phase-like diagram; and searching, with a genetic algorithm, for the parameters that lead to the most optimal simulation outcome.

REDD+ and climate smart agriculture in landscapes: A case study in Vietnam using companion modelling.

PubMed

Salvini, G; Ligtenberg, A; van Paassen, A; Bregt, A K; Avitabile, V; Herold, M

2016-05-01

Finding land use strategies that merge land-based climate change mitigation measures and adaptation strategies is still an open issue in climate discourse. This article explores synergies and trade-offs between REDD+, a scheme that focuses mainly on mitigation through forest conservation, with "Climate Smart Agriculture", an approach that emphasizes adaptive agriculture. We introduce a framework for ex-ante assessment of the impact of land management policies and interventions and for quantifying their impacts on land-based mitigation and adaptation goals. The framework includes a companion modelling (ComMod) process informed by interviews with policymakers, local experts and local farmers. The ComMod process consists of a Role-Playing Game with local farmers and an Agent Based Model. The game provided a participatory means to develop policy and climate change scenarios. These scenarios were then used as inputs to the Agent Based Model, a spatially explicit model to simulate landscape dynamics and the associated carbon emissions over decades. We applied the framework using as case study a community in central Vietnam, characterized by deforestation for subsistence agriculture and cultivation of acacias as a cash crop. The main findings show that the framework is useful in guiding consideration of local stakeholders' goals, needs and constraints. Additionally the framework provided beneficial information to policymakers, pointing to ways that policies might be re-designed to make them better tailored to local circumstances and therefore more effective in addressing synergistically climate change mitigation and adaptation objectives. Copyright © 2015 Elsevier Ltd. All rights reserved.

Agent-Based Framework for Discrete Entity Simulations

DTIC Science & Technology

2006-11-01

Postgres database server for environment queries of neighbors and continuum data. As expected for raw database queries (no database optimizations in...form. Eventually the code was ported to GNU C++ on the same single Intel Pentium 4 CPU running RedHat Linux 9.0 and Postgres database server...Again Postgres was used for environmental queries, and the tool remained relatively slow because of the immense number of queries necessary to assess

An Agent-Based Dynamic Model for Analysis of Distributed Space Exploration Architectures

NASA Astrophysics Data System (ADS)

Sindiy, Oleg V.; DeLaurentis, Daniel A.; Stein, William B.

2009-07-01

A range of complex challenges, but also potentially unique rewards, underlie the development of exploration architectures that use a distributed, dynamic network of resources across the solar system. From a methodological perspective, the prime challenge is to systematically model the evolution (and quantify comparative performance) of such architectures, under uncertainty, to effectively direct further study of specialized trajectories, spacecraft technologies, concept of operations, and resource allocation. A process model for System-of-Systems Engineering is used to define time-varying performance measures for comparative architecture analysis and identification of distinguishing patterns among interoperating systems. Agent-based modeling serves as the means to create a discrete-time simulation that generates dynamics for the study of architecture evolution. A Solar System Mobility Network proof-of-concept problem is introduced representing a set of longer-term, distributed exploration architectures. Options within this set revolve around deployment of human and robotic exploration and infrastructure assets, their organization, interoperability, and evolution, i.e., a system-of-systems. Agent-based simulations quantify relative payoffs for a fully distributed architecture (which can be significant over the long term), the latency period before they are manifest, and the up-front investment (which can be substantial compared to alternatives). Verification and sensitivity results provide further insight on development paths and indicate that the framework and simulation modeling approach may be useful in architectural design of other space exploration mass, energy, and information exchange settings.

A causal analysis framework for land-use change and the potential role of bioenergy policy

DOE PAGES

Efroymson, Rebecca A.; Kline, Keith L.; Angelsen, Arild; ...

2016-10-05

Here we propose a causal analysis framework to increase the reliability of land-use change (LUC) models and the accuracy of net greenhouse gas (GHG) emissions calculations for biofuels. The health-sciences-inspired framework is used here to determine probable causes of LUC, with an emphasis on bioenergy and deforestation. Calculations of net GHG emissions for LUC are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under national (U.S., U.K.), state (California), and European Union regulations. Biofuel policymakers and scientists continue to discuss whether presumed indirect land-use change (ILUC) estimates, which often involve deforestation, should be includedmore » in GHG accounting for biofuel pathways. Current estimates of ILUC for bioenergy rely largely on economic simulation models that focus on causal pathways involving global commodity trade and use coarse land cover data with simple land classification systems. ILUC estimates are highly uncertain, partly because changes are not clearly defined and key causal links are not sufficiently included in the models. The proposed causal analysis framework begins with a definition of the change that has occurred and proceeds to a strength-of-evidence approach based on types of epidemiological evidence including plausibility of the relationship, completeness of the causal pathway, spatial co-occurrence, time order, analogous agents, simulation model results, and quantitative agent response relationships.Lastly, we discuss how LUC may be allocated among probable causes for policy purposes and how the application of the framework has the potential to increase the validity of LUC models and resolve ILUC and biofuel controversies.« less

A causal analysis framework for land-use change and the potential role of bioenergy policy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Efroymson, Rebecca A.; Kline, Keith L.; Angelsen, Arild

Here we propose a causal analysis framework to increase the reliability of land-use change (LUC) models and the accuracy of net greenhouse gas (GHG) emissions calculations for biofuels. The health-sciences-inspired framework is used here to determine probable causes of LUC, with an emphasis on bioenergy and deforestation. Calculations of net GHG emissions for LUC are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under national (U.S., U.K.), state (California), and European Union regulations. Biofuel policymakers and scientists continue to discuss whether presumed indirect land-use change (ILUC) estimates, which often involve deforestation, should be includedmore » in GHG accounting for biofuel pathways. Current estimates of ILUC for bioenergy rely largely on economic simulation models that focus on causal pathways involving global commodity trade and use coarse land cover data with simple land classification systems. ILUC estimates are highly uncertain, partly because changes are not clearly defined and key causal links are not sufficiently included in the models. The proposed causal analysis framework begins with a definition of the change that has occurred and proceeds to a strength-of-evidence approach based on types of epidemiological evidence including plausibility of the relationship, completeness of the causal pathway, spatial co-occurrence, time order, analogous agents, simulation model results, and quantitative agent response relationships.Lastly, we discuss how LUC may be allocated among probable causes for policy purposes and how the application of the framework has the potential to increase the validity of LUC models and resolve ILUC and biofuel controversies.« less

Capturing multi-stage fuzzy uncertainties in hybrid system dynamics and agent-based models for enhancing policy implementation in health systems research.

PubMed

Liu, Shiyong; Triantis, Konstantinos P; Zhao, Li; Wang, Youfa

2018-01-01

In practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model. In a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions. In this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM. This research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions.

Contrast-enhanced digital mammography (CEDM): imaging modeling, computer simulations, and phantom study

NASA Astrophysics Data System (ADS)

Chen, Biao; Jing, Zhenxue; Smith, Andrew

2005-04-01

Contrast enhanced digital mammography (CEDM), which is based upon the analysis of a series of x-ray projection images acquired before/after the administration of contrast agents, may provide physicians critical physiologic and morphologic information of breast lesions to determine the malignancy of lesions. This paper proposes to combine the kinetic analysis (KA) of contrast agent uptake/washout process and the dual-energy (DE) contrast enhancement together to formulate a hybrid contrast enhanced breast-imaging framework. The quantitative characteristics of materials and imaging components in the x-ray imaging chain, including x-ray tube (tungsten) spectrum, filter, breast tissues/lesions, contrast agents (non-ionized iodine solution), and selenium detector, were systematically modeled. The contrast-noise-ration (CNR) of iodinated lesions and mean absorbed glandular dose were estimated mathematically. The x-ray techniques optimization was conducted through a series of computer simulations to find the optimal tube voltage, filter thickness, and exposure levels for various breast thicknesses, breast density, and detectable contrast agent concentration levels in terms of detection efficiency (CNR2/dose). A phantom study was performed on a modified Selenia full field digital mammography system to verify the simulated results. The dose level was comparable to the dose in diagnostic mode (less than 4 mGy for an average 4.2 cm compressed breast). The results from the computer simulations and phantom study are being used to optimize an ongoing clinical study.

Towards an agent-oriented programming language based on Scala

NASA Astrophysics Data System (ADS)

Mitrović, Dejan; Ivanović, Mirjana; Budimac, Zoran

2012-09-01

Scala and its multi-threaded model based on actors represent an excellent framework for developing purely reactive agents. This paper presents an early research on extending Scala with declarative programming constructs, which would result in a new agent-oriented programming language suitable for developing more advanced, BDI agent architectures. The main advantage the new language over many other existing solutions for programming BDI agents is a natural and straightforward integration of imperative and declarative programming constructs, fitted under a single development framework.

A Framework to Describe, Analyze and Generate Interactive Motor Behaviors

PubMed Central

Jarrassé, Nathanaël; Charalambous, Themistoklis; Burdet, Etienne

2012-01-01

While motor interaction between a robot and a human, or between humans, has important implications for society as well as promising applications, little research has been devoted to its investigation. In particular, it is important to understand the different ways two agents can interact and generate suitable interactive behaviors. Towards this end, this paper introduces a framework for the description and implementation of interactive behaviors of two agents performing a joint motor task. A taxonomy of interactive behaviors is introduced, which can classify tasks and cost functions that represent the way each agent interacts. The role of an agent interacting during a motor task can be directly explained from the cost function this agent is minimizing and the task constraints. The novel framework is used to interpret and classify previous works on human-robot motor interaction. Its implementation power is demonstrated by simulating representative interactions of two humans. It also enables us to interpret and explain the role distribution and switching between roles when performing joint motor tasks. PMID:23226231

A framework to describe, analyze and generate interactive motor behaviors.

PubMed

Jarrassé, Nathanaël; Charalambous, Themistoklis; Burdet, Etienne

2012-01-01

While motor interaction between a robot and a human, or between humans, has important implications for society as well as promising applications, little research has been devoted to its investigation. In particular, it is important to understand the different ways two agents can interact and generate suitable interactive behaviors. Towards this end, this paper introduces a framework for the description and implementation of interactive behaviors of two agents performing a joint motor task. A taxonomy of interactive behaviors is introduced, which can classify tasks and cost functions that represent the way each agent interacts. The role of an agent interacting during a motor task can be directly explained from the cost function this agent is minimizing and the task constraints. The novel framework is used to interpret and classify previous works on human-robot motor interaction. Its implementation power is demonstrated by simulating representative interactions of two humans. It also enables us to interpret and explain the role distribution and switching between roles when performing joint motor tasks.

An Agent-Based Model of Farmer Decision Making in Jordan

NASA Astrophysics Data System (ADS)

Selby, Philip; Medellin-Azuara, Josue; Harou, Julien; Klassert, Christian; Yoon, Jim

2016-04-01

We describe an agent based hydro-economic model of groundwater irrigated agriculture in the Jordan Highlands. The model employs a Multi-Agent-Simulation (MAS) framework and is designed to evaluate direct and indirect outcomes of climate change scenarios and policy interventions on farmer decision making, including annual land use, groundwater use for irrigation, and water sales to a water tanker market. Land use and water use decisions are simulated for groups of farms grouped by location and their behavioural and economic similarities. Decreasing groundwater levels, and the associated increase in pumping costs, are important drivers for change within Jordan'S agricultural sector. We describe how this is considered by coupling of agricultural and groundwater models. The agricultural production model employs Positive Mathematical Programming (PMP), a method for calibrating agricultural production functions to observed planted areas. PMP has successfully been used with disaggregate models for policy analysis. We adapt the PMP approach to allow explicit evaluation of the impact of pumping costs, groundwater purchase fees and a water tanker market. The work demonstrates the applicability of agent-based agricultural decision making assessment in the Jordan Highlands and its integration with agricultural model calibration methods. The proposed approach is designed and implemented with software such that it could be used to evaluate a variety of physical and human influences on decision making in agricultural water management.

Modeling formalisms in Systems Biology

PubMed Central

2011-01-01

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future. PMID:22141422

Flow Cytometry with Gold Nanoparticles and their Clusters as scattering Contrast Agents: FDTD Simulation of Light-Cell Interaction

PubMed Central

Tanev, Stoyan; Sun, Wenbo; Pond, James; Tuchin, Valery V.; Zharov, Vladimir P.

2010-01-01

The formulation of the Finite-Difference Time-Domain (FDTD) approach is presented in the framework of its potential applications to in vivo flow cytometry based on light scattering. The consideration is focused on comparison of light scattering by a single biological cell alone in controlled refractive index matching conditions and by cells labeled by gold nanoparticles. The optical schematics including phase contrast (OPCM) microscopy as a prospective modality for in vivo flow cytometry is also analyzed. The validation of the FDTD approach for the simulation of flow cytometry may open a new avenue in the development of advanced cytometric techniques based on scattering effects from nanoscale targets. PMID:19670359

Agent autonomy approach to probabilistic physics-of-failure modeling of complex dynamic systems with interacting failure mechanisms

NASA Astrophysics Data System (ADS)

Gromek, Katherine Emily

A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007.

Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling.

PubMed

An, Gary; Christley, Scott

2012-01-01

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

Social Simulation for AmI Systems Engineering

NASA Astrophysics Data System (ADS)

Garcia-Valverde, Teresa; Serrano, Emilio; Botia, Juan A.

This paper propose the use of multi-agent based simulation (MABS) to allow testing, validating and verifying Ambient Intelligence (AmI) environments in a flexible and robust way. The development of AmI is very complex because of this technology must often adapt to contextual information as well as unpredictable and changeable behaviours. The concrete simulation is called Ubik and is integrated into the AmISim architecture which is also presented in this paper. This architecture deals with AmI applications in order to discover defects, estimate quality of applications, help to make decisions about the design, etc. The paper shows that Ubik and AmISim provide a simulation framework which can test scenarios that would be impossible in real environments or even with previous AmI simulation approaches.

An Agent-Based Model for the Role of Short-Term Memory Enhancement in the Emergence of Grammatical Agreement.

PubMed

Vera, Javier

2018-01-01

What is the influence of short-term memory enhancement on the emergence of grammatical agreement systems in multi-agent language games? Agreement systems suppose that at least two words share some features with each other, such as gender, number, or case. Previous work, within the multi-agent language-game framework, has recently proposed models stressing the hypothesis that the emergence of a grammatical agreement system arises from the minimization of semantic ambiguity. On the other hand, neurobiological evidence argues for the hypothesis that language evolution has mainly related to an increasing of short-term memory capacity, which has allowed the online manipulation of words and meanings participating particularly in grammatical agreement systems. Here, the main aim is to propose a multi-agent language game for the emergence of a grammatical agreement system, under measurable long-range relations depending on the short-term memory capacity. Computer simulations, based on a parameter that measures the amount of short-term memory capacity, suggest that agreement marker systems arise in a population of agents equipped at least with a critical short-term memory capacity.

Modeling and simulating human teamwork behaviors using intelligent agents

NASA Astrophysics Data System (ADS)

Fan, Xiaocong; Yen, John

2004-12-01

Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively gathering, fusing, and sharing information, and for building coherent teams with both humans and agents working effectively on intelligence-intensive problems. Teamwork modeling is also challenging because the research has spanned diverse disciplines from business management to cognitive science, human discourse, and distributed artificial intelligence. This article presents an extensive, but not exhaustive, list of work in the field, where the taxonomy is organized along two main dimensions: team social structure and social behaviors. Along the dimension of social structure, we consider agent-only teams and mixed human-agent teams. Along the dimension of social behaviors, we consider collaborative behaviors, communicative behaviors, helping behaviors, and the underpinning of effective teamwork-shared mental models. The contribution of this article is that it presents an organizational framework for analyzing a variety of teamwork simulation systems and for further studying simulated teamwork behaviors.

A framework for modelling the complexities of food and water security under globalisation

NASA Astrophysics Data System (ADS)

Dermody, Brian J.; Sivapalan, Murugesu; Stehfest, Elke; van Vuuren, Detlef P.; Wassen, Martin J.; Bierkens, Marc F. P.; Dekker, Stefan C.

2018-01-01

We present a new framework for modelling the complexities of food and water security under globalisation. The framework sets out a method to capture regional and sectoral interdependencies and cross-scale feedbacks within the global food system that contribute to emergent water use patterns. The framework integrates aspects of existing models and approaches in the fields of hydrology and integrated assessment modelling. The core of the framework is a multi-agent network of city agents connected by infrastructural trade networks. Agents receive socio-economic and environmental constraint information from integrated assessment models and hydrological models respectively and simulate complex, socio-environmental dynamics that operate within those constraints. The emergent changes in food and water resources are aggregated and fed back to the original models with minimal modification of the structure of those models. It is our conviction that the framework presented can form the basis for a new wave of decision tools that capture complex socio-environmental change within our globalised world. In doing so they will contribute to illuminating pathways towards a sustainable future for humans, ecosystems and the water they share.

Agent-based modelling in synthetic biology.

PubMed

Gorochowski, Thomas E

2016-11-30

Biological systems exhibit complex behaviours that emerge at many different levels of organization. These span the regulation of gene expression within single cells to the use of quorum sensing to co-ordinate the action of entire bacterial colonies. Synthetic biology aims to make the engineering of biology easier, offering an opportunity to control natural systems and develop new synthetic systems with useful prescribed behaviours. However, in many cases, it is not understood how individual cells should be programmed to ensure the emergence of a required collective behaviour. Agent-based modelling aims to tackle this problem, offering a framework in which to simulate such systems and explore cellular design rules. In this article, I review the use of agent-based models in synthetic biology, outline the available computational tools, and provide details on recently engineered biological systems that are amenable to this approach. I further highlight the challenges facing this methodology and some of the potential future directions. © 2016 The Author(s).

Validation techniques of agent based modelling for geospatial simulations

NASA Astrophysics Data System (ADS)

Darvishi, M.; Ahmadi, G.

2014-10-01

One of the most interesting aspects of modelling and simulation study is to describe the real world phenomena that have specific properties; especially those that are in large scales and have dynamic and complex behaviours. Studying these phenomena in the laboratory is costly and in most cases it is impossible. Therefore, Miniaturization of world phenomena in the framework of a model in order to simulate the real phenomena is a reasonable and scientific approach to understand the world. Agent-based modelling and simulation (ABMS) is a new modelling method comprising of multiple interacting agent. They have been used in the different areas; for instance, geographic information system (GIS), biology, economics, social science and computer science. The emergence of ABM toolkits in GIS software libraries (e.g. ESRI's ArcGIS, OpenMap, GeoTools, etc) for geospatial modelling is an indication of the growing interest of users to use of special capabilities of ABMS. Since ABMS is inherently similar to human cognition, therefore it could be built easily and applicable to wide range applications than a traditional simulation. But a key challenge about ABMS is difficulty in their validation and verification. Because of frequent emergence patterns, strong dynamics in the system and the complex nature of ABMS, it is hard to validate and verify ABMS by conventional validation methods. Therefore, attempt to find appropriate validation techniques for ABM seems to be necessary. In this paper, after reviewing on Principles and Concepts of ABM for and its applications, the validation techniques and challenges of ABM validation are discussed.

An integrated modeling framework of socio-economic, biophysical, and hydrological processes in Midwest landscapes: Remote sensing data, agro-hydrological model, and agent-based model

NASA Astrophysics Data System (ADS)

Ding, Deng

Intensive human-environment interactions are taking place in Midwestern agricultural systems. An integrated modeling framework is suitable for predicting dynamics of key variables of the socio-economic, biophysical, hydrological processes as well as exploring the potential transitions of system states in response to changes of the driving factors. The purpose of this dissertation is to address issues concerning the interacting processes and consequent changes in land use, water balance, and water quality using an integrated modeling framework. This dissertation is composed of three studies in the same agricultural watershed, the Clear Creek watershed in East-Central Iowa. In the first study, a parsimonious hydrologic model, the Threshold-Exceedance-Lagrangian Model (TELM), is further developed into RS-TELM (Remote Sensing TELM) to integrate remote sensing vegetation data for estimating evapotranspiration. The goodness of fit of RS-TELM is comparable to a well-calibrated SWAT (Soil and Water Assessment Tool) and even slightly superior in capturing intra-seasonal variability of stream flow. The integration of RS LAI (Leaf Area Index) data improves the model's performance especially over the agriculture dominated landscapes. The input of rainfall datasets with spatially explicit information plays a critical role in increasing the model's goodness of fit. In the second study, an agent-based model is developed to simulate farmers' decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. The comparison between simulated crop land percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields (yield drag). The simulation results given alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed show that, farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule. In the third study, the consequent water quantity and quality change of the potential land use transitions given alternative biofuel crop market scenarios is explored in a case study in the Clear Creek watershed. A computer program is developed to implement the loose-coupling strategy to couple an agent-based land use model with SWAT. The simulation results show that watershed-scale water quantity (water yield and runoff) and quality variables (sediment and nutrient loads) decrease in values as switchgrass price increases. However, negligence of farmers risk aversions towards biofuel crop adoption would cause overestimation of the impacts of switchgrass price on water quantity and quality.

Agent-Based Framework for Personalized Service Provisioning in Converged IP Networks

NASA Astrophysics Data System (ADS)

Podobnik, Vedran; Matijasevic, Maja; Lovrek, Ignac; Skorin-Kapov, Lea; Desic, Sasa

In a global multi-service and multi-provider market, the Internet Service Providers will increasingly need to differentiate in the service quality they offer and base their operation on new, consumer-centric business models. In this paper, we propose an agent-based framework for the Business-to-Consumer (B2C) electronic market, comprising the Consumer Agents, Broker Agents and Content Agents, which enable Internet consumers to select a content provider in an automated manner. We also discuss how to dynamically allocate network resources to provide end-to-end Quality of Service (QoS) for a given consumer and content provider.

Action Understanding as Inverse Planning

ERIC Educational Resources Information Center

Baker, Chris L.; Saxe, Rebecca; Tenenbaum, Joshua B.

2009-01-01

Humans are adept at inferring the mental states underlying other agents' actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents' behavior based on the…

Agent-based modeling: Methods and techniques for simulating human systems

PubMed Central

Bonabeau, Eric

2002-01-01

Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed. PMID:12011407

Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

PubMed Central

Senior, Alistair M.; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J.

2016-01-01

Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. PMID:26858671

Analytical model for effects of capsule shape on the healing efficiency in self-healing materials

PubMed Central

Li, Songpeng; Chen, Huisu

2017-01-01

The fundamental requirement for the autonomous capsule-based self-healing process to work is that cracks need to reach the capsules and break them such that the healing agent can be released. Ignoring all other aspects, the amount of healing agents released into the crack is essential to obtain a good healing. Meanwhile, from the perspective of the capsule shapes, spherical or elongated capsules (hollow tubes/fibres) are the main morphologies used in capsule-based self-healing materials. The focus of this contribution is the description of the effects of capsule shape on the efficiency of healing agent released in capsule-based self-healing material within the framework of the theory of geometrical probability and integral geometry. Analytical models are developed to characterize the amount of healing agent released per crack area from capsules for an arbitrary crack intersecting with capsules of various shapes in a virtual capsule-based self-healing material. The average crack opening distance is chosen to be a key parameter in defining the healing potential of individual cracks in the models. Furthermore, the accuracy of the developed models was verified by comparison to the data from a published numerical simulation study. PMID:29095862

A CSP-Based Agent Modeling Framework for the Cougaar Agent-Based Architecture

NASA Technical Reports Server (NTRS)

Gracanin, Denis; Singh, H. Lally; Eltoweissy, Mohamed; Hinchey, Michael G.; Bohner, Shawn A.

2005-01-01

Cognitive Agent Architecture (Cougaar) is a Java-based architecture for large-scale distributed agent-based applications. A Cougaar agent is an autonomous software entity with behaviors that represent a real-world entity (e.g., a business process). A Cougaar-based Model Driven Architecture approach, currently under development, uses a description of system's functionality (requirements) to automatically implement the system in Cougaar. The Communicating Sequential Processes (CSP) formalism is used for the formal validation of the generated system. Two main agent components, a blackboard and a plugin, are modeled as CSP processes. A set of channels represents communications between the blackboard and individual plugins. The blackboard is represented as a CSP process that communicates with every agent in the collection. The developed CSP-based Cougaar modeling framework provides a starting point for a more complete formal verification of the automatically generated Cougaar code. Currently it is used to verify the behavior of an individual agent in terms of CSP properties and to analyze the corresponding Cougaar society.

SCALING AN URBAN EMERGENCY EVACUATION FRAMEWORK: CHALLENGES AND PRACTICES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karthik, Rajasekar; Lu, Wei

2014-01-01

Critical infrastructure disruption, caused by severe weather events, natural disasters, terrorist attacks, etc., has significant impacts on urban transportation systems. We built a computational framework to simulate urban transportation systems under critical infrastructure disruption in order to aid real-time emergency evacuation. This framework will use large scale datasets to provide a scalable tool for emergency planning and management. Our framework, World-Wide Emergency Evacuation (WWEE), integrates population distribution and urban infrastructure networks to model travel demand in emergency situations at global level. Also, a computational model of agent-based traffic simulation is used to provide an optimal evacuation plan for traffic operationmore » purpose [1]. In addition, our framework provides a web-based high resolution visualization tool for emergency evacuation modelers and practitioners. We have successfully tested our framework with scenarios in both United States (Alexandria, VA) and Europe (Berlin, Germany) [2]. However, there are still some major drawbacks for scaling this framework to handle big data workloads in real time. On our back-end, lack of proper infrastructure limits us in ability to process large amounts of data, run the simulation efficiently and quickly, and provide fast retrieval and serving of data. On the front-end, the visualization performance of microscopic evacuation results is still not efficient enough due to high volume data communication between server and client. We are addressing these drawbacks by using cloud computing and next-generation web technologies, namely Node.js, NoSQL, WebGL, Open Layers 3 and HTML5 technologies. We will describe briefly about each one and how we are using and leveraging these technologies to provide an efficient tool for emergency management organizations. Our early experimentation demonstrates that using above technologies is a promising approach to build a scalable and high performance urban emergency evacuation framework that can improve traffic mobility and safety under critical infrastructure disruption in today s socially connected world.« less

Dyadic brain modelling, mirror systems and the ontogenetic ritualization of ape gesture

PubMed Central

Arbib, Michael; Ganesh, Varsha; Gasser, Brad

2014-01-01

The paper introduces dyadic brain modelling, offering both a framework for modelling the brains of interacting agents and a general framework for simulating and visualizing the interactions generated when the brains (and the two bodies) are each coded up in computational detail. It models selected neural mechanisms in ape brains supportive of social interactions, including putative mirror neuron systems inspired by macaque neurophysiology but augmented by increased access to proprioceptive state. Simulation results for a reduced version of the model show ritualized gesture emerging from interactions between a simulated child and mother ape. PMID:24778382

Dyadic brain modelling, mirror systems and the ontogenetic ritualization of ape gesture.

PubMed

Arbib, Michael; Ganesh, Varsha; Gasser, Brad

2014-01-01

The paper introduces dyadic brain modelling, offering both a framework for modelling the brains of interacting agents and a general framework for simulating and visualizing the interactions generated when the brains (and the two bodies) are each coded up in computational detail. It models selected neural mechanisms in ape brains supportive of social interactions, including putative mirror neuron systems inspired by macaque neurophysiology but augmented by increased access to proprioceptive state. Simulation results for a reduced version of the model show ritualized gesture emerging from interactions between a simulated child and mother ape.

Smart Grid as Multi-layer Interacting System for Complex Decision Makings

NASA Astrophysics Data System (ADS)

Bompard, Ettore; Han, Bei; Masera, Marcelo; Pons, Enrico

This chapter presents an approach to the analysis of Smart Grids based on a multi-layer representation of their technical, cyber, social and decision-making aspects, as well as the related environmental constraints. In the Smart Grid paradigm, self-interested active customers (prosumers), system operators and market players interact among themselves making use of an extensive cyber infrastructure. In addition, policy decision makers define regulations, incentives and constraints to drive the behavior of the competing operators and prosumers, with the objective of ensuring the global desired performance (e.g. system stability, fair prices). For these reasons, the policy decision making is more complicated than in traditional power systems, and needs proper modeling and simulation tools for assessing "in vitro" and ex-ante the possible impacts of the decisions assumed. In this chapter, we consider the smart grids as multi-layered interacting complex systems. The intricacy of the framework, characterized by several interacting layers, cannot be captured by closed-form mathematical models. Therefore, a new approach using Multi Agent Simulation is described. With case studies we provide some indications about how to develop agent-based simulation tools presenting some preliminary examples.

Distributed Processing System for Restoration of Electric Power Distribution Network Using Two-Layered Contract Net Protocol

NASA Astrophysics Data System (ADS)

Kodama, Yu; Hamagami, Tomoki

Distributed processing system for restoration of electric power distribution network using two-layered CNP is proposed. The goal of this study is to develop the restoration system which adjusts to the future power network with distributed generators. The state of the art of this study is that the two-layered CNP is applied for the distributed computing environment in practical use. The two-layered CNP has two classes of agents, named field agent and operating agent in the network. In order to avoid conflicts of tasks, operating agent controls privilege for managers to send the task announcement messages in CNP. This technique realizes the coordination between agents which work asynchronously in parallel with others. Moreover, this study implements the distributed processing system using a de-fact standard multi-agent framework, JADE(Java Agent DEvelopment framework). This study conducts the simulation experiments of power distribution network restoration and compares the proposed system with the previous system. We confirmed the results show effectiveness of the proposed system.

A Multi Agent Based Approach for Prehospital Emergency Management.

PubMed

Safdari, Reza; Shoshtarian Malak, Jaleh; Mohammadzadeh, Niloofar; Danesh Shahraki, Azimeh

2017-07-01

To demonstrate an architecture to automate the prehospital emergency process to categorize the specialized care according to the situation at the right time for reducing the patient mortality and morbidity. Prehospital emergency process were analyzed using existing prehospital management systems, frameworks and the extracted process were modeled using sequence diagram in Rational Rose software. System main agents were identified and modeled via component diagram, considering the main system actors and by logically dividing business functionalities, finally the conceptual architecture for prehospital emergency management was proposed. The proposed architecture was simulated using Anylogic simulation software. Anylogic Agent Model, State Chart and Process Model were used to model the system. Multi agent systems (MAS) had a great success in distributed, complex and dynamic problem solving environments, and utilizing autonomous agents provides intelligent decision making capabilities.  The proposed architecture presents prehospital management operations. The main identified agents are: EMS Center, Ambulance, Traffic Station, Healthcare Provider, Patient, Consultation Center, National Medical Record System and quality of service monitoring agent. In a critical condition like prehospital emergency we are coping with sophisticated processes like ambulance navigation health care provider and service assignment, consultation, recalling patients past medical history through a centralized EHR system and monitoring healthcare quality in a real-time manner. The main advantage of our work has been the multi agent system utilization. Our Future work will include proposed architecture implementation and evaluation of its impact on patient quality care improvement.

A Multi Agent Based Approach for Prehospital Emergency Management

PubMed Central

Safdari, Reza; Shoshtarian Malak, Jaleh; Mohammadzadeh, Niloofar; Danesh Shahraki, Azimeh

2017-01-01

Objective: To demonstrate an architecture to automate the prehospital emergency process to categorize the specialized care according to the situation at the right time for reducing the patient mortality and morbidity. Methods: Prehospital emergency process were analyzed using existing prehospital management systems, frameworks and the extracted process were modeled using sequence diagram in Rational Rose software. System main agents were identified and modeled via component diagram, considering the main system actors and by logically dividing business functionalities, finally the conceptual architecture for prehospital emergency management was proposed. The proposed architecture was simulated using Anylogic simulation software. Anylogic Agent Model, State Chart and Process Model were used to model the system. Results: Multi agent systems (MAS) had a great success in distributed, complex and dynamic problem solving environments, and utilizing autonomous agents provides intelligent decision making capabilities.  The proposed architecture presents prehospital management operations. The main identified agents are: EMS Center, Ambulance, Traffic Station, Healthcare Provider, Patient, Consultation Center, National Medical Record System and quality of service monitoring agent. Conclusion: In a critical condition like prehospital emergency we are coping with sophisticated processes like ambulance navigation health care provider and service assignment, consultation, recalling patients past medical history through a centralized EHR system and monitoring healthcare quality in a real-time manner. The main advantage of our work has been the multi agent system utilization. Our Future work will include proposed architecture implementation and evaluation of its impact on patient quality care improvement. PMID:28795061

Expert knowledge elicitation using computer simulation: the organization of frail elderly case management as an illustration.

PubMed

Chiêm, Jean-Christophe; Van Durme, Thérèse; Vandendorpe, Florence; Schmitz, Olivier; Speybroeck, Niko; Cès, Sophie; Macq, Jean

2014-08-01

Various elderly case management projects have been implemented in Belgium. This type of long-term health care intervention involves contextual factors and human interactions. These underlying complex mechanisms can be usefully informed with field experts' knowledge, which are hard to make explicit. However, computer simulation has been suggested as one possible method of overcoming the difficulty of articulating such elicited qualitative views. A simulation model of case management was designed using an agent-based methodology, based on the initial qualitative research material. Variables and rules of interaction were formulated into a simple conceptual framework. This model has been implemented and was used as a support for a structured discussion with experts in case management. The rigorous formulation provided by the agent-based methodology clarified the descriptions of the interventions and the problems encountered regarding: the diverse network topologies of health care actors in the project; the adaptation time required by the intervention; the communication between the health care actors; the institutional context; the organization of the care; and the role of the case manager and his or hers personal ability to interpret the informal demands of the frail older person. The simulation model should be seen primarily as a tool for thinking and learning. A number of insights were gained as part of a valuable cognitive process. Computer simulation supporting field experts' elicitation can lead to better-informed decisions in the organization of complex health care interventions. © 2013 John Wiley & Sons, Ltd.

A Multi-Level Approach to Modeling Rapidly Growing Mega-Regions as a Coupled Human-Natural System

NASA Astrophysics Data System (ADS)

Koch, J. A.; Tang, W.; Meentemeyer, R. K.

2013-12-01

The FUTure Urban-Regional Environment Simulation (FUTURES) integrates information on nonstationary drivers of land change (per capita land area demand, site suitability, and spatial structure of conversion events) into spatial-temporal projections of changes in landscape patterns (Meentemeyer et al., 2013). One striking feature of FUTURES is its patch-growth algorithm that includes feedback effects of former development events across several temporal and spatial scales: cell-level transition events are aggregated into patches of land change and their further growth is based on empirically derived parameters controlling its size, shape, and dispersion. Here, we augment the FUTURES modeling framework by expanding its multilevel structure and its representation of human decision making. The new modeling framework is hierarchically organized as nested subsystems including the latest theory on telecouplings in coupled human-natural systems (Liu et al., 2013). Each subsystem represents a specific level of spatial scale and embraces agents that have decision making authority at a particular level. The subsystems are characterized with regard to their spatial representation and are connected via flows of information (e.g. regulations and policies) or material (e.g. population migration). To provide a modeling framework that is applicable to a wide range of settings and geographical regions and to keep it computationally manageable, we implement a 'zooming factor' that allows to enable or disable subsystems (and hence the represented processes), based on the extent of the study region. The implementation of the FUTURES modeling framework for a specific case study follows the observational modeling approach described in Grimm et al. (2005), starting from the analysis of empirical data in order to capture the processes relevant for specific scales and to allow a rigorous calibration and validation of the model application. In this paper, we give an introduction to the basic concept of our modeling approach and describe its strengths and weaknesses. We furthermore use empirical data for the states of North and South Carolina to demonstrate how the modeling framework can be applied to a large, heterogeneous study system with diverse decision-making agents. Grimm et al. (2005) Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology. Science 310, 987-991. Liu et al. (2013) Framing Sustainability in a Telecoupled World. Ecology and Society 18(2), 26. Meentemeyer et al. (2013) FUTURES: Multilevel Simulations of Merging Urban-Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm. Annals of the Association of American Geographers 103(4), 785-807.

An Active Learning Exercise for Introducing Agent-Based Modeling

ERIC Educational Resources Information Center

Pinder, Jonathan P.

2013-01-01

Recent developments in agent-based modeling as a method of systems analysis and optimization indicate that students in business analytics need an introduction to the terminology, concepts, and framework of agent-based modeling. This article presents an active learning exercise for MBA students in business analytics that demonstrates agent-based…

Understanding Islamist political violence through computational social simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watkins, Jennifer H; Mackerrow, Edward P; Patelli, Paolo G

Understanding the process that enables political violence is of great value in reducing the future demand for and support of violent opposition groups. Methods are needed that allow alternative scenarios and counterfactuals to be scientifically researched. Computational social simulation shows promise in developing 'computer experiments' that would be unfeasible or unethical in the real world. Additionally, the process of modeling and simulation reveals and challenges assumptions that may not be noted in theories, exposes areas where data is not available, and provides a rigorous, repeatable, and transparent framework for analyzing the complex dynamics of political violence. This paper demonstrates themore » computational modeling process using two simulation techniques: system dynamics and agent-based modeling. The benefits and drawbacks of both techniques are discussed. In developing these social simulations, we discovered that the social science concepts and theories needed to accurately simulate the associated psychological and social phenomena were lacking.« less

Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

PubMed

Ligmann-Zielinska, Arika; Kramer, Daniel B; Spence Cheruvelil, Kendra; Soranno, Patricia A

2014-01-01

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.

Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance

PubMed Central

Ligmann-Zielinska, Arika; Kramer, Daniel B.; Spence Cheruvelil, Kendra; Soranno, Patricia A.

2014-01-01

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system. PMID:25340764

Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities

PubMed Central

2009-01-01

Background This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. Methods As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. Results An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. Conclusion This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it. PMID:19922684

Epidemic modeling with discrete-space scheduled walkers: extensions and research opportunities.

PubMed

Borkowski, Maciej; Podaima, Blake W; McLeod, Robert D

2009-11-18

This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it.

Collaborative Information Retrieval Method among Personal Repositories

NASA Astrophysics Data System (ADS)

Kamei, Koji; Yukawa, Takashi; Yoshida, Sen; Kuwabara, Kazuhiro

In this paper, we describe a collaborative information retrieval method among personal repositorie and an implementation of the method on a personal agent framework. We propose a framework for personal agents that aims to enable the sharing and exchange of information resources that are distributed unevenly among individuals. The kernel of a personal agent framework is an RDF(resource description framework)-based information repository for storing, retrieving and manipulating privately collected information, such as documents the user read and/or wrote, email he/she exchanged, web pages he/she browsed, etc. The repository also collects annotations to information resources that describe relationships among information resources and records of interaction between the user and information resources. Since the information resources in a personal repository and their structure are personalized, information retrieval from other users' is an important application of the personal agent. A vector space model with a personalized concept-base is employed as an information retrieval mechanism in a personal repository. Since a personalized concept-base is constructed from information resources in a personal repository, it reflects its user's knowledge and interests. On the other hand, it leads to another problem while querying other users' personal repositories; that is, simply transferring query requests does not provide desirable results. To solve this problem, we propose a query equalization scheme based on a relevance feedback method for collaborative information retrieval between personalized concept-bases. In this paper, we describe an implementation of the collaborative information retrieval method and its user interface on the personal agent framework.

Virtual tissues in toxicology.

PubMed

Shah, Imran; Wambaugh, John

2010-02-01

New approaches are vital for efficiently evaluating human health risk of thousands of chemicals in commerce. In vitro models offer a high-throughput approach for assaying chemical-induced molecular and cellular changes; however, bridging these perturbations to in vivo effects across chemicals, dose, time, and species remains challenging. Technological advances in multiresolution imaging and multiscale simulation are making it feasible to reconstruct tissues in silico. In toxicology, these "virtual" tissues (VT) aim to predict histopathological outcomes from alterations of cellular phenotypes that are controlled by chemical-induced perturbations in molecular pathways. The behaviors of thousands of heterogeneous cells in tissues are simulated discretely using agent-based modeling (ABM), in which computational "agents" mimic cell interactions and cellular responses to the microenvironment. The behavior of agents is constrained by physical laws and biological rules derived from experimental evidence. VT extend compartmental physiologic models to simulate both acute insults as well as the chronic effects of low-dose exposure. Furthermore, agent behavior can encode the logic of signaling and genetic regulatory networks to evaluate the role of different pathways in chemical-induced injury. To extrapolate toxicity across species, chemicals, and doses, VT require four main components: (a) organization of prior knowledge on physiologic events to define the mechanistic rules for agent behavior, (b) knowledge on key chemical-induced molecular effects, including activation of stress sensors and changes in molecular pathways that alter the cellular phenotype, (c) multiresolution quantitative and qualitative analysis of histologic data to characterize and measure chemical-, dose-, and time-dependent physiologic events, and (d) multiscale, spatiotemporal simulation frameworks to effectively calibrate and evaluate VT using experimental data. This investigation presents the motivation, implementation, and application of VT with examples from hepatotoxicity and carcinogenesis.

Cyberwar XXI: quantifying the unquantifiable: adaptive AI for next-generation conflict simulations

NASA Astrophysics Data System (ADS)

Miranda, Joseph; von Kleinsmid, Peter; Zalewski, Tony

2004-08-01

The era of the "Revolution in Military Affairs," "4th Generation Warfare" and "Asymmetric War" requires novel approaches to modeling warfare at the operational and strategic level of modern conflict. For example, "What if, in response to our planned actions, the adversary reacts in such-and-such a manner? What will our response be? What are the possible unintended consequences?" Next generation conflict simulation tools are required to help create and test novel courses of action (COA's) in support of real-world operations. Conflict simulations allow non-lethal and cost-effective exploration of the "what-if" of COA development. The challenge has been to develop an automated decision-support software tool which allows competing COA"s to be compared in simulated dynamic environments. Principal Investigator Joseph Miranda's research is based on modeling an integrated military, economic, social, infrastructure and information (PMESII) environment. The main effort was to develop an adaptive AI engine which models agents operating within an operational-strategic conflict environment. This was implemented in Cyberwar XXI - a simulation which models COA selection in a PMESII environment. Within this framework, agents simulate decision-making processes and provide predictive capability of the potential behavior of Command Entities. The 2003 Iraq is the first scenario ready for V&V testing.

Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling.

PubMed

Hulme, Adam; Thompson, Jason; Nielsen, Rasmus Oestergaard; Read, Gemma J M; Salmon, Paul M

2018-06-18

There have been recent calls for the application of the complex systems approach in sports injury research. However, beyond theoretical description and static models of complexity, little progress has been made towards formalising this approach in way that is practical to sports injury scientists and clinicians. Therefore, our objective was to use a computational modelling method and develop a dynamic simulation in sports injury research. Agent-based modelling (ABM) was used to model the occurrence of sports injury in a synthetic athlete population. The ABM was developed based on sports injury causal frameworks and was applied in the context of distance running-related injury (RRI). Using the acute:chronic workload ratio (ACWR), we simulated the dynamic relationship between changes in weekly running distance and RRI through the manipulation of various 'athlete management tools'. The findings confirmed that building weekly running distances over time, even within the reported ACWR 'sweet spot', will eventually result in RRI as athletes reach and surpass their individual physical workload limits. Introducing training-related error into the simulation and the modelling of a 'hard ceiling' dynamic resulted in a higher RRI incidence proportion across the population at higher absolute workloads. The presented simulation offers a practical starting point to further apply more sophisticated computational models that can account for the complex nature of sports injury aetiology. Alongside traditional forms of scientific inquiry, the use of ABM and other simulation-based techniques could be considered as a complementary and alternative methodological approach in sports injury research. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Decentralized Framework for Multi-Agent Robotic Systems

PubMed Central

2018-01-01

Over the past few years, decentralization of multi-agent robotic systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous and robust tasks. However, in some cases, the network communication process between robotic agents is overlooked, and this creates a dependency for each agent to maintain a permanent link with nearby units to be able to fulfill its goals. This article describes a communication framework, where each agent in the system can leave the network or accept new connections, sending its information based on the transfer history of all nodes in the network. To this end, each agent needs to comply with four processes to participate in the system, plus a fifth process for data transfer to the nearest nodes that is based on Received Signal Strength Indicator (RSSI) and data history. To validate this framework, we use differential robotic agents and a monitoring agent to generate a topological map of an environment with the presence of obstacles. PMID:29389849

Research on monocentric model of urbanization by agent-based simulation

NASA Astrophysics Data System (ADS)

Xue, Ling; Yang, Kaizhong

2008-10-01

Over the past years, GIS have been widely used for modeling urbanization from a variety of perspectives such as digital terrain representation and overlay analysis using cell-based data platform. Similarly, simulation of urban dynamics has been achieved with the use of Cellular Automata. In contrast to these approaches, agent-based simulation provides a much more powerful set of tools. This allows researchers to set up a counterpart for real environmental and urban systems in computer for experimentation and scenario analysis. This Paper basically reviews the research on the economic mechanism of urbanization and an agent-based monocentric model is setup for further understanding the urbanization process and mechanism in China. We build an endogenous growth model with dynamic interactions between spatial agglomeration and urban development by using agent-based simulation. It simulates the migration decisions of two main types of agents, namely rural and urban households between rural and urban area. The model contains multiple economic interactions that are crucial in understanding urbanization and industrial process in China. These adaptive agents can adjust their supply and demand according to the market situation by a learning algorithm. The simulation result shows this agent-based urban model is able to perform the regeneration and to produce likely-to-occur projections of reality.

Flow cytometry with gold nanoparticles and their clusters as scattering contrast agents: FDTD simulation of light-cell interaction.

PubMed

Tanev, Stoyan; Sun, Wenbo; Pond, James; Tuchin, Valery V; Zharov, Vladimir P

2009-09-01

The formulation of the finite-difference time-domain (FDTD) approach is presented in the framework of its potential applications to in-vivo flow cytometry based on light scattering. The consideration is focused on comparison of light scattering by a single biological cell alone in controlled refractive-index matching conditions and by cells labeled by gold nanoparticles. The optical schematics including phase contrast (OPCM) microscopy as a prospective modality for in-vivo flow cytometry is also analyzed. The validation of the FDTD approach for the simulation of flow cytometry may open up a new avenue in the development of advanced cytometric techniques based on scattering effects from nanoscale targets. 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

A Systematic Review of Agent-Based Modelling and Simulation Applications in the Higher Education Domain

ERIC Educational Resources Information Center

Gu, X.; Blackmore, K. L.

2015-01-01

This paper presents the results of a systematic review of agent-based modelling and simulation (ABMS) applications in the higher education (HE) domain. Agent-based modelling is a "bottom-up" modelling paradigm in which system-level behaviour (macro) is modelled through the behaviour of individual local-level agent interactions (micro).…

Simulating Microbial Community Patterning Using Biocellion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, Seung-Hwa; Kahan, Simon H.; Momeni, Babak

2014-04-17

Mathematical modeling and computer simulation are important tools for understanding complex interactions between cells and their biotic and abiotic environment: similarities and differences between modeled and observed behavior provide the basis for hypothesis forma- tion. Momeni et al. [5] investigated pattern formation in communities of yeast strains engaging in different types of ecological interactions, comparing the predictions of mathematical modeling and simulation to actual patterns observed in wet-lab experiments. However, simu- lations of millions of cells in a three-dimensional community are ex- tremely time-consuming. One simulation run in MATLAB may take a week or longer, inhibiting exploration of the vastmore » space of parameter combinations and assumptions. Improving the speed, scale, and accu- racy of such simulations facilitates hypothesis formation and expedites discovery. Biocellion is a high performance software framework for ac- celerating discrete agent-based simulation of biological systems with millions to trillions of cells. Simulations of comparable scale and accu- racy to those taking a week of computer time using MATLAB require just hours using Biocellion on a multicore workstation. Biocellion fur- ther accelerates large scale, high resolution simulations using cluster computers by partitioning the work to run on multiple compute nodes. Biocellion targets computational biologists who have mathematical modeling backgrounds and basic C++ programming skills. This chap- ter describes the necessary steps to adapt the original Momeni et al.'s model to the Biocellion framework as a case study.« less

Modelling brain emergent behaviours through coevolution of neural agents.

PubMed

Maniadakis, Michail; Trahanias, Panos

2006-06-01

Recently, many research efforts focus on modelling partial brain areas with the long-term goal to support cognitive abilities of artificial organisms. Existing models usually suffer from heterogeneity, which constitutes their integration very difficult. The present work introduces a computational framework to address brain modelling tasks, emphasizing on the integrative performance of substructures. Moreover, implemented models are embedded in a robotic platform to support its behavioural capabilities. We follow an agent-based approach in the design of substructures to support the autonomy of partial brain structures. Agents are formulated to allow the emergence of a desired behaviour after a certain amount of interaction with the environment. An appropriate collaborative coevolutionary algorithm, able to emphasize both the speciality of brain areas and their cooperative performance, is employed to support design specification of agent structures. The effectiveness of the proposed approach is illustrated through the implementation of computational models for motor cortex and hippocampus, which are successfully tested on a simulated mobile robot.

Comparing potential copper chelation mechanisms in Parkinson's disease protein

NASA Astrophysics Data System (ADS)

Rose, Frisco; Hodak, Miroslav; Bernholc, Jerry

2011-03-01

We have implemented the nudged elastic band (NEB) as a guided dynamics framework for our real-space multigrid method of DFT-based quantum simulations. This highly parallel approach resolves a minimum energy pathway (MEP) on the energy hypersurface by relaxing intermediates in a chain-of-states. As an initial application we present an investigation of chelating agents acting on copper ion bound to α -synuclein, whose misfolding is implicated in Parkinson's disease (PD). Copper ions are known to act as highly effective misfolding agents in a-synuclein and are thus an important target in understanding PD. Furthermore, chelation therapy has shown promise in the treatment of Alzheimer's and other neuro-degenerative diseases with similar metal-correlated pathologies. At present, our candidate chelating agents include nicotine, curcumin and clioquinol. We examine their MEP activation barriers in the context of a PD onset mechanism to assess the viability of various chelators for PD remediation.

Synthesis, Characterization, and Multimillion-Atom Simulation of Halogen-Based Energetic Materials for Agent Defeat

DTIC Science & Technology

2013-04-01

DTRA-TR-13-23 Synthesis, Characterization, and Multimillion -Atom Simulation of Halogen-Based Energetic Materials for Agent Defeat Approved for...reagents for the destruction of biologically active materials and a simulation of their reactions on a multimillion atom scale with quantum...explosives for destruction of chemical & biological agents. Multimillion -atom molecular dynamics simulations with quantum mechanical accuracy were

A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems

PubMed Central

Merrick, Kathryn E.; Shafi, Kamran

2013-01-01

An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players' optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots. PMID:24198797

A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems.

PubMed

Merrick, Kathryn E; Shafi, Kamran

2013-01-01

An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players' optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

Exploration of Force Transition in Stability Operations Using Multi-Agent Simulation

DTIC Science & Technology

2006-09-01

risk, mission failure risk, and time in the context of the operational threat environment. The Pythagoras Multi-Agent Simulation and Data Farming...NUMBER OF PAGES 173 14. SUBJECT TERMS Stability Operations, Peace Operations, Data Farming, Pythagoras , Agent- Based Model, Multi-Agent Simulation...the operational threat environment. The Pythagoras Multi-Agent Simulation and Data Farming techniques are used to investigate force-level

Principal-agent theory: a framework for improving health care reform in Tennessee.

PubMed

Sekwat, A

2000-01-01

Using a framework based on principal-agent theory, this study examines problems faced by managed care organizations (MCOs) and major health care providers under the state of Tennessee's current capitation-based managed care programs called TennCare. Based on agency theory, the study proposes a framework to show how an effective collaborative relationship can be forged between the state of Tennessee and participating MCOs which takes into account the major concerns of third-party health care providers. The proposed framework further enhances realization of the state's key health care reform goals which are to control the rising costs of health care delivery and to expand health care coverage to uninsured and underinsured Tennesseans.

Template-Based Geometric Simulation of Flexible Frameworks

PubMed Central

Wells, Stephen A.; Sartbaeva, Asel

2012-01-01

Specialised modelling and simulation methods implementing simplified physical models are valuable generators of insight. Template-based geometric simulation is a specialised method for modelling flexible framework structures made up of rigid units. We review the background, development and implementation of the method, and its applications to the study of framework materials such as zeolites and perovskites. The “flexibility window” property of zeolite frameworks is a particularly significant discovery made using geometric simulation. Software implementing geometric simulation of framework materials, “GASP”, is freely available to researchers. PMID:28817055

Designing Agent Collectives For Systems With Markovian Dynamics

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Lawson, John W.

2004-01-01

The Collective Intelligence (COIN) framework concerns the design of collectives of agents so that as those agents strive to maximize their individual utility functions, their interaction causes a provided world utility function concerning the entire collective to be also maximized. Here we show how to extend that framework to scenarios having Markovian dynamics when no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. Our approach transforms the (time-extended) argument of each agent's utility function before evaluating that function. This transformation has benefits in scenarios not involving Markovian dynamics of an agent's utility function are observable. We investigate this transformation in simulations involving both hear and quadratic (nonlinear) dynamics. In addition, we find that a certain subset of these transformations, which result in utilities that have low opacity (analogous to having high signal to noise) but are not factored (analogous to not being incentive compatible), reliably improve performance over that arising with factored utilities. We also present a Taylor Series method for the fully general nonlinear case.

iCrowd: agent-based behavior modeling and crowd simulator

NASA Astrophysics Data System (ADS)

Kountouriotis, Vassilios I.; Paterakis, Manolis; Thomopoulos, Stelios C. A.

2016-05-01

Initially designed in the context of the TASS (Total Airport Security System) FP-7 project, the Crowd Simulation platform developed by the Integrated Systems Lab of the Institute of Informatics and Telecommunications at N.C.S.R. Demokritos, has evolved into a complete domain-independent agent-based behavior simulator with an emphasis on crowd behavior and building evacuation simulation. Under continuous development, it reflects an effort to implement a modern, multithreaded, data-oriented simulation engine employing latest state-of-the-art programming technologies and paradigms. It is based on an extensible architecture that separates core services from the individual layers of agent behavior, offering a concrete simulation kernel designed for high-performance and stability. Its primary goal is to deliver an abstract platform to facilitate implementation of several Agent-Based Simulation solutions with applicability in several domains of knowledge, such as: (i) Crowd behavior simulation during [in/out] door evacuation. (ii) Non-Player Character AI for Game-oriented applications and Gamification activities. (iii) Vessel traffic modeling and simulation for Maritime Security and Surveillance applications. (iv) Urban and Highway Traffic and Transportation Simulations. (v) Social Behavior Simulation and Modeling.

A Sampling-Based Bayesian Approach for Cooperative Multiagent Online Search With Resource Constraints.

PubMed

Xiao, Hu; Cui, Rongxin; Xu, Demin

2018-06-01

This paper presents a cooperative multiagent search algorithm to solve the problem of searching for a target on a 2-D plane under multiple constraints. A Bayesian framework is used to update the local probability density functions (PDFs) of the target when the agents obtain observation information. To obtain the global PDF used for decision making, a sampling-based logarithmic opinion pool algorithm is proposed to fuse the local PDFs, and a particle sampling approach is used to represent the continuous PDF. Then the Gaussian mixture model (GMM) is applied to reconstitute the global PDF from the particles, and a weighted expectation maximization algorithm is presented to estimate the parameters of the GMM. Furthermore, we propose an optimization objective which aims to guide agents to find the target with less resource consumptions, and to keep the resource consumption of each agent balanced simultaneously. To this end, a utility function-based optimization problem is put forward, and it is solved by a gradient-based approach. Several contrastive simulations demonstrate that compared with other existing approaches, the proposed one uses less overall resources and shows a better performance of balancing the resource consumption.

A distributed model predictive control scheme for leader-follower multi-agent systems

NASA Astrophysics Data System (ADS)

Franzè, Giuseppe; Lucia, Walter; Tedesco, Francesco

2018-02-01

In this paper, we present a novel receding horizon control scheme for solving the formation problem of leader-follower configurations. The algorithm is based on set-theoretic ideas and is tuned for agents described by linear time-invariant (LTI) systems subject to input and state constraints. The novelty of the proposed framework relies on the capability to jointly use sequences of one-step controllable sets and polyhedral piecewise state-space partitions in order to online apply the 'better' control action in a distributed receding horizon fashion. Moreover, we prove that the design of both robust positively invariant sets and one-step-ahead controllable regions is achieved in a distributed sense. Simulations and numerical comparisons with respect to centralised and local-based strategies are finally performed on a group of mobile robots to demonstrate the effectiveness of the proposed control strategy.

Developing framework for agent- based diabetes disease management system: user perspective.

PubMed

Mohammadzadeh, Niloofar; Safdari, Reza; Rahimi, Azin

2014-02-01

One of the characteristics of agents is mobility which makes them very suitable for remote electronic health and tele medicine. The aim of this study is developing a framework for agent based diabetes information management at national level through identifying required agents. The main tool is a questioner that is designed in three sections based on studying library resources, performance of major organizations in the field of diabetes in and out of the country and interviews with experts in the medical, health information management and software fields. Questionnaires based on Delphi methods were distributed among 20 experts. In order to design and identify agents required in health information management for the prevention and appropriate and rapid treatment of diabetes, the results were analyzed using SPSS 17 and Results were plotted with FREEPLANE mind map software. ACCESS TO DATA TECHNOLOGY IN PROPOSED FRAMEWORK IN ORDER OF PRIORITY IS: mobile (mean 1/80), SMS, EMAIL (mean 2/80), internet, web (mean 3/30), phone (mean 3/60), WIFI (mean 4/60). In delivering health care to diabetic patients, considering social and human aspects is essential. Having a systematic view for implementation of agent systems and paying attention to all aspects such as feedbacks, user acceptance, budget, motivation, hierarchy, useful standards, affordability of individuals, identifying barriers and opportunities and so on, are necessary.

Evolution of a multi-agent system in a cyclical environment.

PubMed

Baptista, Tiago; Costa, Ernesto

2008-06-01

The synchronisation phenomena in biological systems is a current and recurring subject of scientific study. This topic, namely that of circadian clocks, served as inspiration to develop an agent-based simulation that serves the main purpose of being a proof-of-concept of the model used in the BitBang framework, that implements a modern autonomous agent model. Despite having been extensively studied, circadian clocks still have much to be investigated. Rather than wanting to learn more about the internals of this biological process, we look to study the emergence of this kind of adaptation to a daily cycle. To that end we implemented a world with a day/night cycle, and analyse the ways the agents adapt to that cycle. The results show the evolution of the agents' ability to gather food. If we look at the total number of agents over the course of an experiment, we can pinpoint the time when reproductive technology emerges. We also show that the agents adapt to the daily cycle. This circadian rhythm can be shown by analysing the variation on the agents metabolic rate, which is affected by the variation of their movement patterns. In the experiments conducted we can observe that the metabolic rate of the agents varies according to the daily cycle.

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors

NASA Astrophysics Data System (ADS)

Cenek, Martin; Dahl, Spencer K.

2016-11-01

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors.

PubMed

Cenek, Martin; Dahl, Spencer K

2016-11-01

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.

Logistics Process Analysis ToolProcess Analysis Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

2008-03-31

LPAT is the resulting integrated system between ANL-developed Enhanced Logistics Intra Theater Support Tool (ELIST) sponsored by SDDC-TEA and the Fort Future Virtual Installation Tool (sponsored by CERL). The Fort Future Simulation Engine was an application written in the ANL Repast Simphony framework and used as the basis for the process Anlysis Tool (PAT) which evolved into a stand=-along tool for detailed process analysis at a location. Combined with ELIST, an inter-installation logistics component was added to enable users to define large logistical agent-based models without having to program. PAT is the evolution of an ANL-developed software system called Fortmore » Future Virtual Installation Tool (sponsored by CERL). The Fort Future Simulation Engine was an application written in the ANL Repast Simphony framework and used as the basis for the Process Analysis Tool(PAT) which evolved into a stand-alone tool for detailed process analysis at a location (sponsored by the SDDC-TEA).« less

A Conceptual Framework for Representing Human Behavior Characteristics in a System of Systems Agent-based Survivability Simulation-Intelligent Networks

DTIC Science & Technology

2014-10-17

communication ), and those with â0â means no connectivity at all. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT SAR...that two edges of the network with “1” have a crisp connectivity (and hence good communication ), and those with “0” means no connectivity at all. By...1” simply means that two edges of the network with “1” have a crisp connectivity (and hence good communication ), and those with “0” means no

Agent-based Large-Scale Emergency Evacuation Using Real-Time Open Government Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Wei; Liu, Cheng; Bhaduri, Budhendra L

The open government initiatives have provided tremendous data resources for the transportation system and emergency services in urban areas. This paper proposes a traffic simulation framework using high temporal resolution demographic data and real time open government data for evacuation planning and operation. A comparison study using real-world data in Seattle, Washington is conducted to evaluate the framework accuracy and evacuation efficiency. The successful simulations of selected area prove the concept to take advantage open government data, open source data, and high resolution demographic data in emergency management domain. There are two aspects of parameters considered in this study: usermore » equilibrium (UE) conditions of traffic assignment model (simple Non-UE vs. iterative UE) and data temporal resolution (Daytime vs. Nighttime). Evacuation arrival rate, average travel time, and computation time are adopted as Measure of Effectiveness (MOE) for evacuation performance analysis. The temporal resolution of demographic data has significant impacts on urban transportation dynamics during evacuation scenarios. Better evacuation performance estimation can be approached by integrating both Non-UE and UE scenarios. The new framework shows flexibility in implementing different evacuation strategies and accuracy in evacuation performance. The use of this framework can be explored to day-to-day traffic assignment to support daily traffic operations.« less

A Software Framework for Remote Patient Monitoring by Using Multi-Agent Systems Support

PubMed Central

2017-01-01

Background Although there have been significant advances in network, hardware, and software technologies, the health care environment has not taken advantage of these developments to solve many of its inherent problems. Research activities in these 3 areas make it possible to apply advanced technologies to address many of these issues such as real-time monitoring of a large number of patients, particularly where a timely response is critical. Objective The objective of this research was to design and develop innovative technological solutions to offer a more proactive and reliable medical care environment. The short-term and primary goal was to construct IoT4Health, a flexible software framework to generate a range of Internet of things (IoT) applications, containing components such as multi-agent systems that are designed to perform Remote Patient Monitoring (RPM) activities autonomously. An investigation into its full potential to conduct such patient monitoring activities in a more proactive way is an expected future step. Methods A framework methodology was selected to evaluate whether the RPM domain had the potential to generate customized applications that could achieve the stated goal of being responsive and flexible within the RPM domain. As a proof of concept of the software framework’s flexibility, 3 applications were developed with different implementations for each framework hot spot to demonstrate potential. Agents4Health was selected to illustrate the instantiation process and IoT4Health’s operation. To develop more concrete indicators of the responsiveness of the simulated care environment, an experiment was conducted while Agents4Health was operating, to measure the number of delays incurred in monitoring the tasks performed by agents. Results IoT4Health’s construction can be highlighted as our contribution to the development of eHealth solutions. As a software framework, IoT4Health offers extensibility points for the generation of applications. Applications can extend the framework in the following ways: identification, collection, storage, recovery, visualization, monitoring, anomalies detection, resource notification, and dynamic reconfiguration. Based on other outcomes involving observation of the resulting applications, it was noted that its design contributed toward more proactive patient monitoring. Through these experimental systems, anomalies were detected in real time, with agents sending notifications instantly to the health providers. Conclusions We conclude that the cost-benefit of the construction of a more generic and complex system instead of a custom-made software system demonstrated the worth of the approach, making it possible to generate applications in this domain in a more timely fashion. PMID:28347973

An agent-based modeling framework for evaluating hypotheses on risks for developing autism: effects of the gut microbial environment.

PubMed

Weston, Bronson; Fogal, Benjamin; Cook, Daniel; Dhurjati, Prasad

2015-04-01

The number of cases diagnosed with Autism Spectrum Disorders is rising at an alarming rate with the Centers for Disease Control estimating the 2014 incidence rate as 1 in 68. Recently, it has been hypothesized that gut bacteria may contribute to the development of autism. Specifically, the relative balances between the inflammatory microbes clostridia and desulfovibrio and the anti-inflammatory microbe bifidobacteria may become destabilized prior to autism development. The imbalance leads to a leaky gut, characterized by a more porous epithelial membrane resulting in microbial toxin release into the blood, which may contribute to brain inflammation and autism development. To test how changes in population dynamics of the gut microbiome may lead to the imbalanced microbial populations associated with autism patients, we constructed a novel agent-based model of clostridia, desulfovibrio, and bifidobacteria population interactions in the gut. The model demonstrates how changing physiological conditions in the gut can affect the population dynamics of the microbiome. Simulations using our agent-based model indicate that despite large perturbations to initial levels of bacteria, the populations robustly achieve a single steady-state given similar gut conditions. These simulation results suggests that disturbance such as a prebiotic or antibiotic treatment may only transiently affect the gut microbiome. However, sustained prebiotic treatments may correct low population counts of bifidobacteria. Furthermore, our simulations suggest that clostridia growth rate is a key determinant of risk of autism development. Treatment of high-risk infants with supra-physiological levels of lysozymes may suppress clostridia growth rate, resulting in a steep decrease in the clostridia population and therefore reduced risk of autism development. Copyright © 2015 Elsevier Ltd. All rights reserved.

Applications of Agent Based Approaches in Business (A Three Essay Dissertation)

ERIC Educational Resources Information Center

Prawesh, Shankar

2013-01-01

The goal of this dissertation is to investigate the enabling role that agent based simulation plays in business and policy. The aforementioned issue has been addressed in this dissertation through three distinct, but related essays. The first essay is a literature review of different research applications of agent based simulation in various…

Model-based metrics of human-automation function allocation in complex work environments

NASA Astrophysics Data System (ADS)

Kim, So Young

Function allocation is the design decision which assigns work functions to all agents in a team, both human and automated. Efforts to guide function allocation systematically has been studied in many fields such as engineering, human factors, team and organization design, management science, and cognitive systems engineering. Each field focuses on certain aspects of function allocation, but not all; thus, an independent discussion of each does not address all necessary issues with function allocation. Four distinctive perspectives emerged from a review of these fields: technology-centered, human-centered, team-oriented, and work-oriented. Each perspective focuses on different aspects of function allocation: capabilities and characteristics of agents (automation or human), team structure and processes, and work structure and the work environment. Together, these perspectives identify the following eight issues with function allocation: 1) Workload, 2) Incoherency in function allocations, 3) Mismatches between responsibility and authority, 4) Interruptive automation, 5) Automation boundary conditions, 6) Function allocation preventing human adaptation to context, 7) Function allocation destabilizing the humans' work environment, and 8) Mission Performance. Addressing these issues systematically requires formal models and simulations that include all necessary aspects of human-automation function allocation: the work environment, the dynamics inherent to the work, agents, and relationships among them. Also, addressing these issues requires not only a (static) model, but also a (dynamic) simulation that captures temporal aspects of work such as the timing of actions and their impact on the agent's work. Therefore, with properly modeled work as described by the work environment, the dynamics inherent to the work, agents, and relationships among them, a modeling framework developed by this thesis, which includes static work models and dynamic simulation, can capture the issues with function allocation. Then, based on the eight issues, eight types of metrics are established. The purpose of these metrics is to assess the extent to which each issue exists with a given function allocation. Specifically, the eight types of metrics assess workload, coherency of a function allocation, mismatches between responsibility and authority, interruptive automation, automation boundary conditions, human adaptation to context, stability of the human's work environment, and mission performance. Finally, to validate the modeling framework and the metrics, a case study was conducted modeling four different function allocations between a pilot and flight deck automation during the arrival and approach phases of flight. A range of pilot cognitive control modes and maximum human taskload limits were also included in the model. The metrics were assessed for these four function allocations and analyzed to validate capability of the metrics to identify important issues in given function allocations. In addition, the design insights provided by the metrics are highlighted. This thesis concludes with a discussion of mechanisms for further validating the modeling framework and function allocation metrics developed here, and highlights where these developments can be applied in research and in the design of function allocations in complex work environments such as aviation operations.

A Software Framework for Remote Patient Monitoring by Using Multi-Agent Systems Support.

PubMed

Fernandes, Chrystinne Oliveira; Lucena, Carlos José Pereira De

2017-03-27

Although there have been significant advances in network, hardware, and software technologies, the health care environment has not taken advantage of these developments to solve many of its inherent problems. Research activities in these 3 areas make it possible to apply advanced technologies to address many of these issues such as real-time monitoring of a large number of patients, particularly where a timely response is critical. The objective of this research was to design and develop innovative technological solutions to offer a more proactive and reliable medical care environment. The short-term and primary goal was to construct IoT4Health, a flexible software framework to generate a range of Internet of things (IoT) applications, containing components such as multi-agent systems that are designed to perform Remote Patient Monitoring (RPM) activities autonomously. An investigation into its full potential to conduct such patient monitoring activities in a more proactive way is an expected future step. A framework methodology was selected to evaluate whether the RPM domain had the potential to generate customized applications that could achieve the stated goal of being responsive and flexible within the RPM domain. As a proof of concept of the software framework's flexibility, 3 applications were developed with different implementations for each framework hot spot to demonstrate potential. Agents4Health was selected to illustrate the instantiation process and IoT4Health's operation. To develop more concrete indicators of the responsiveness of the simulated care environment, an experiment was conducted while Agents4Health was operating, to measure the number of delays incurred in monitoring the tasks performed by agents. IoT4Health's construction can be highlighted as our contribution to the development of eHealth solutions. As a software framework, IoT4Health offers extensibility points for the generation of applications. Applications can extend the framework in the following ways: identification, collection, storage, recovery, visualization, monitoring, anomalies detection, resource notification, and dynamic reconfiguration. Based on other outcomes involving observation of the resulting applications, it was noted that its design contributed toward more proactive patient monitoring. Through these experimental systems, anomalies were detected in real time, with agents sending notifications instantly to the health providers. We conclude that the cost-benefit of the construction of a more generic and complex system instead of a custom-made software system demonstrated the worth of the approach, making it possible to generate applications in this domain in a more timely fashion. ©Chrystinne Oliveira Fernandes, Carlos José Pereira De Lucena. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 27.03.2017.

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and NSGA2-BASED Multiobjective Optimization Approaches

NASA Astrophysics Data System (ADS)

Sahelgozin, M.; Alimohammadi, A.

2015-12-01

Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO) problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA) that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

Evaluation of wholesale electric power market rules and financial risk management by agent-based simulations

NASA Astrophysics Data System (ADS)

Yu, Nanpeng

As U.S. regional electricity markets continue to refine their market structures, designs and rules of operation in various ways, two critical issues are emerging. First, although much experience has been gained and costly and valuable lessons have been learned, there is still a lack of a systematic platform for evaluation of the impact of a new market design from both engineering and economic points of view. Second, the transition from a monopoly paradigm characterized by a guaranteed rate of return to a competitive market created various unfamiliar financial risks for various market participants, especially for the Investor Owned Utilities (IOUs) and Independent Power Producers (IPPs). This dissertation uses agent-based simulation methods to tackle the market rules evaluation and financial risk management problems. The California energy crisis in 2000-01 showed what could happen to an electricity market if it did not go through a comprehensive and rigorous testing before its implementation. Due to the complexity of the market structure, strategic interaction between the participants, and the underlying physics, it is difficult to fully evaluate the implications of potential changes to market rules. This dissertation presents a flexible and integrative method to assess market designs through agent-based simulations. Realistic simulation scenarios on a 225-bus system are constructed for evaluation of the proposed PJM-like market power mitigation rules of the California electricity market. Simulation results show that in the absence of market power mitigation, generation company (GenCo) agents facilitated by Q-learning are able to exploit the market flaws and make significantly higher profits relative to the competitive benchmark. The incorporation of PJM-like local market power mitigation rules is shown to be effective in suppressing the exercise of market power. The importance of financial risk management is exemplified by the recent financial crisis. In this dissertation, basic financial risk management concepts relevant for wholesale electric power markets are carefully explained and illustrated. In addition, the financial risk management problem in wholesale electric power markets is generalized as a four-stage process. Within the proposed financial risk management framework, the critical problem of financial bilateral contract negotiation is addressed. This dissertation analyzes a financial bilateral contract negotiation process between a generating company and a load-serving entity in a wholesale electric power market with congestion managed by locational marginal pricing. Nash bargaining theory is used to model a Pareto-efficient settlement point. The model predicts negotiation results under varied conditions and identifies circumstances in which the two parties might fail to reach an agreement. Both analysis and agent-based simulation are used to gain insight regarding how relative risk aversion and biased price estimates influence negotiated outcomes. These results should provide useful guidance to market participants in their bilateral contract negotiation processes.

Integrating robotic action with biologic perception: A brain-machine symbiosis theory

NASA Astrophysics Data System (ADS)

Mahmoudi, Babak

In patients with motor disability the natural cyclic flow of information between the brain and external environment is disrupted by their limb impairment. Brain-Machine Interfaces (BMIs) aim to provide new communication channels between the brain and environment by direct translation of brain's internal states into actions. For enabling the user in a wide range of daily life activities, the challenge is designing neural decoders that autonomously adapt to different tasks, environments, and to changes in the pattern of neural activity. In this dissertation, a novel decoding framework for BMIs is developed in which a computational agent autonomously learns how to translate neural states into action based on maximization of a measure of shared goal between user and the agent. Since the agent and brain share the same goal, a symbiotic relationship between them will evolve therefore this decoding paradigm is called a Brain-Machine Symbiosis (BMS) framework. A decoding agent was implemented within the BMS framework based on the Actor-Critic method of Reinforcement Learning. The rule of the Actor as a neural decoder was to find mapping between the neural representation of motor states in the primary motor cortex (MI) and robot actions in order to solve reaching tasks. The Actor learned the optimal control policy using an evaluative feedback that was estimated by the Critic directly from the user's neural activity of the Nucleus Accumbens (NAcc). Through a series of computational neuroscience studies in a cohort of rats it was demonstrated that NAcc could provide a useful evaluative feedback by predicting the increase or decrease in the probability of earning reward based on the environmental conditions. Using a closed-loop BMI simulator it was demonstrated the Actor-Critic decoding architecture was able to adapt to different tasks as well as changes in the pattern of neural activity. The custom design of a dual micro-wire array enabled simultaneous implantation of MI and NAcc for the development of a full closed-loop system. The Actor-Critic decoding architecture was able to solve the brain-controlled reaching task using a robotic arm by capturing the interdependency between the simultaneous action representation in MI and reward expectation in NAcc.

Workflow Agents vs. Expert Systems: Problem Solving Methods in Work Systems Design

NASA Technical Reports Server (NTRS)

Clancey, William J.; Sierhuis, Maarten; Seah, Chin

2009-01-01

During the 1980s, a community of artificial intelligence researchers became interested in formalizing problem solving methods as part of an effort called "second generation expert systems" (2nd GES). How do the motivations and results of this research relate to building tools for the workplace today? We provide an historical review of how the theory of expertise has developed, a progress report on a tool for designing and implementing model-based automation (Brahms), and a concrete example how we apply 2nd GES concepts today in an agent-based system for space flight operations (OCAMS). Brahms incorporates an ontology for modeling work practices, what people are doing in the course of a day, characterized as "activities." OCAMS was developed using a simulation-to-implementation methodology, in which a prototype tool was embedded in a simulation of future work practices. OCAMS uses model-based methods to interactively plan its actions and keep track of the work to be done. The problem solving methods of practice are interactive, employing reasoning for and through action in the real world. Analogously, it is as if a medical expert system were charged not just with interpreting culture results, but actually interacting with a patient. Our perspective shifts from building a "problem solving" (expert) system to building an actor in the world. The reusable components in work system designs include entire "problem solvers" (e.g., a planning subsystem), interoperability frameworks, and workflow agents that use and revise models dynamically in a network of people and tools. Consequently, the research focus shifts so "problem solving methods" include ways of knowing that models do not fit the world, and ways of interacting with other agents and people to gain or verify information and (ultimately) adapt rules and procedures to resolve problematic situations.

A Participatory Agent-Based Simulation for Indoor Evacuation Supported by Google Glass.

PubMed

Sánchez, Jesús M; Carrera, Álvaro; Iglesias, Carlos Á; Serrano, Emilio

2016-08-24

Indoor evacuation systems are needed for rescue and safety management. One of the challenges is to provide users with personalized evacuation routes in real time. To this end, this project aims at exploring the possibilities of Google Glass technology for participatory multiagent indoor evacuation simulations. Participatory multiagent simulation combines scenario-guided agents and humans equipped with Google Glass that coexist in a shared virtual space and jointly perform simulations. The paper proposes an architecture for participatory multiagent simulation in order to combine devices (Google Glass and/or smartphones) with an agent-based social simulator and indoor tracking services.

A Parallel Sliding Region Algorithm to Make Agent-Based Modeling Possible for a Large-Scale Simulation: Modeling Hepatitis C Epidemics in Canada.

PubMed

Wong, William W L; Feng, Zeny Z; Thein, Hla-Hla

2016-11-01

Agent-based models (ABMs) are computer simulation models that define interactions among agents and simulate emergent behaviors that arise from the ensemble of local decisions. ABMs have been increasingly used to examine trends in infectious disease epidemiology. However, the main limitation of ABMs is the high computational cost for a large-scale simulation. To improve the computational efficiency for large-scale ABM simulations, we built a parallelizable sliding region algorithm (SRA) for ABM and compared it to a nonparallelizable ABM. We developed a complex agent network and performed two simulations to model hepatitis C epidemics based on the real demographic data from Saskatchewan, Canada. The first simulation used the SRA that processed on each postal code subregion subsequently. The second simulation processed the entire population simultaneously. It was concluded that the parallelizable SRA showed computational time saving with comparable results in a province-wide simulation. Using the same method, SRA can be generalized for performing a country-wide simulation. Thus, this parallel algorithm enables the possibility of using ABM for large-scale simulation with limited computational resources.

From bricks to buildings: adapting the Medical Research Council framework to develop programs of research in simulation education and training for the health professions.

PubMed

Haji, Faizal A; Da Silva, Celina; Daigle, Delton T; Dubrowski, Adam

2014-08-01

Presently, health care simulation research is largely conducted on a study-by-study basis. Although such "project-based" research generates a plethora of evidence, it can be chaotic and contradictory. A move toward sustained, thematic, theory-based programs of research is necessary to advance knowledge in the field. Recognizing that simulation is a complex intervention, we present a framework for developing research programs in simulation-based education adapted from the Medical Research Council (MRC) guidance. This framework calls for an iterative approach to developing, refining, evaluating, and implementing simulation interventions. The adapted framework guidance emphasizes: (1) identification of theory and existing evidence; (2) modeling and piloting interventions to clarify active ingredients and identify mechanisms linking the context, intervention, and outcomes; and (3) evaluation of intervention processes and outcomes in both the laboratory and real-world setting. The proposed framework will aid simulation researchers in developing more robust interventions that optimize simulation-based education and advance our understanding of simulation pedagogy.

Development and application of a real-time testbed for multiagent system interoperability: A case study on hierarchical microgrid control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cintuglu, Mehmet Hazar; Youssef, Tarek; Mohammed, Osama A.

This article presents the development and application of a real-time testbed for multiagent system interoperability. As utility independent private microgrids are installed constantly, standardized interoperability frameworks are required to define behavioral models of the individual agents for expandability and plug-and-play operation. In this paper, we propose a comprehensive hybrid agent framework combining the foundation for intelligent physical agents (FIPA), IEC 61850, and data distribution service (DDS) standards. The IEC 61850 logical node concept is extended using FIPA based agent communication language (ACL) with application specific attributes and deliberative behavior modeling capability. The DDS middleware is adopted to enable a real-timemore » publisher-subscriber interoperability mechanism between platforms. The proposed multi-agent framework was validated in a laboratory based testbed involving developed intelligent electronic device (IED) prototypes and actual microgrid setups. Experimental results were demonstrated for both decentralized and distributed control approaches. Secondary and tertiary control levels of a microgrid were demonstrated for decentralized hierarchical control case study. A consensus-based economic dispatch case study was demonstrated as a distributed control example. Finally, it was shown that the developed agent platform is industrially applicable for actual smart grid field deployment.« less

Development and application of a real-time testbed for multiagent system interoperability: A case study on hierarchical microgrid control

DOE PAGES

Cintuglu, Mehmet Hazar; Youssef, Tarek; Mohammed, Osama A.

2016-08-10

This article presents the development and application of a real-time testbed for multiagent system interoperability. As utility independent private microgrids are installed constantly, standardized interoperability frameworks are required to define behavioral models of the individual agents for expandability and plug-and-play operation. In this paper, we propose a comprehensive hybrid agent framework combining the foundation for intelligent physical agents (FIPA), IEC 61850, and data distribution service (DDS) standards. The IEC 61850 logical node concept is extended using FIPA based agent communication language (ACL) with application specific attributes and deliberative behavior modeling capability. The DDS middleware is adopted to enable a real-timemore » publisher-subscriber interoperability mechanism between platforms. The proposed multi-agent framework was validated in a laboratory based testbed involving developed intelligent electronic device (IED) prototypes and actual microgrid setups. Experimental results were demonstrated for both decentralized and distributed control approaches. Secondary and tertiary control levels of a microgrid were demonstrated for decentralized hierarchical control case study. A consensus-based economic dispatch case study was demonstrated as a distributed control example. Finally, it was shown that the developed agent platform is industrially applicable for actual smart grid field deployment.« less

Evaluation of Littoral Combat Ships for Open-Ocean Anti-Submarine Warfare

DTIC Science & Technology

2016-03-01

known. Source: R. R. Hill, R. G. Carl, and L. E. Champagne , “Using Agent-Based Simulation to Empirically Examine Search Theory Using a Historical Case...coverage over a small area. Source: R. R. Hill, R. G. Carl, and L. E. Champagne , “Using Agent-Based Simulation to Empirically Examine Search Theory...Defense Tech, May 30. Hill, R R, R G Carl, and L E Champagne . “Using agent-based simulation to empirically examine search theory using a

Pathogen transfer through environment-host contact: an agent-based queueing theoretic framework.

PubMed

Chen, Shi; Lenhart, Suzanne; Day, Judy D; Lee, Chihoon; Dulin, Michael; Lanzas, Cristina

2017-11-02

Queueing theory studies the properties of waiting queues and has been applied to investigate direct host-to-host transmitted disease dynamics, but its potential in modelling environmentally transmitted pathogens has not been fully explored. In this study, we provide a flexible and customizable queueing theory modelling framework with three major subroutines to study the in-hospital contact processes between environments and hosts and potential nosocomial pathogen transfer, where environments are servers and hosts are customers. Two types of servers with different parameters but the same utilization are investigated. We consider various forms of transfer functions that map contact duration to the amount of pathogen transfer based on existing literature. We propose a case study of simulated in-hospital contact processes and apply stochastic queues to analyse the amount of pathogen transfer under different transfer functions, and assume that pathogen amount decreases during the inter-arrival time. Different host behaviour (feedback and non-feedback) as well as initial pathogen distribution (whether in environment and/or in hosts) are also considered and simulated. We assess pathogen transfer and circulation under these various conditions and highlight the importance of the nonlinear interactions among contact processes, transfer functions and pathogen demography during the contact process. Our modelling framework can be readily extended to more complicated queueing networks to simulate more realistic situations by adjusting parameters such as the number and type of servers and customers, and adding extra subroutines. © The authors 2017. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Emergent Societal Effects of Crimino-Social Forces in an Animat Agent Model

NASA Astrophysics Data System (ADS)

Scogings, Chris J.; Hawick, Ken A.

Societal behaviour can be studied at a causal level by perturbing a stable multi-agent model with new microscopic behaviours and observing the statistical response over an ensemble of simulated model systems. We report on the effects of introducing criminal and law-enforcing behaviours into a large scale animat agent model and describe the complex spatial agent patterns and population changes that result. Our well-established predator-prey substrate model provides a background framework against which these new microscopic behaviours can be trialled and investigated. We describe some quantitative results and some surprising conclusions concerning the overall societal health when individually anti-social behaviour is introduced.

Multi-Agent Framework for Virtual Learning Spaces.

ERIC Educational Resources Information Center

Sheremetov, Leonid; Nunez, Gustavo

1999-01-01

Discussion of computer-supported collaborative learning, distributed artificial intelligence, and intelligent tutoring systems focuses on the concept of agents, and describes a virtual learning environment that has a multi-agent system. Describes a model of interactions in collaborative learning and discusses agents for Web-based virtual…

Dual-energy contrast-enhanced digital mammography (DE-CEDM): optimization on digital subtraction with practical x-ray low/high-energy spectra

NASA Astrophysics Data System (ADS)

Chen, Biao; Jing, Zhenxue; Smith, Andrew P.; Parikh, Samir; Parisky, Yuri

2006-03-01

Dual-energy contrast enhanced digital mammography (DE-CEDM), which is based upon the digital subtraction of low/high-energy image pairs acquired before/after the administration of contrast agents, may provide physicians physiologic and morphologic information of breast lesions and help characterize their probability of malignancy. This paper proposes to use only one pair of post-contrast low / high-energy images to obtain digitally subtracted dual-energy contrast-enhanced images with an optimal weighting factor deduced from simulated characteristics of the imaging chain. Based upon our previous CEDM framework, quantitative characteristics of the materials and imaging components in the x-ray imaging chain, including x-ray tube (tungsten) spectrum, filters, breast tissues / lesions, contrast agents (non-ionized iodine solution), and selenium detector, were systemically modeled. Using the base-material (polyethylene-PMMA) decomposition method based on entrance low / high-energy x-ray spectra and breast thickness, the optimal weighting factor was calculated to cancel the contrast between fatty and glandular tissues while enhancing the contrast of iodized lesions. By contrast, previous work determined the optimal weighting factor through either a calibration step or through acquisition of a pre-contrast low/high-energy image pair. Computer simulations were conducted to determine weighting factors, lesions' contrast signal values, and dose levels as functions of x-ray techniques and breast thicknesses. Phantom and clinical feasibility studies were performed on a modified Selenia full field digital mammography system to verify the proposed method and computer-simulated results. The resultant conclusions from the computer simulations and phantom/clinical feasibility studies will be used in the upcoming clinical study.

Developing a Conceptual Architecture for a Generalized Agent-based Modeling Environment (GAME)

DTIC Science & Technology

2008-03-01

4. REPAST (Java, Python , C#, Open Source) ........28 5. MASON: Multi-Agent Modeling Language (Swarm Extension... Python , C#, Open Source) Repast (Recursive Porous Agent Simulation Toolkit) was designed for building agent-based models and simulations in the...Repast makes it easy for inexperienced users to build models by including a built-in simple model and provide interfaces through which menus and Python

The Design of Collectives of Agents to Control Non-Markovian Systems

NASA Technical Reports Server (NTRS)

Lawson, John W.; Wolpert, David H.

2004-01-01

The Collective Intelligence (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided "world" utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional "team games". We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents ability to learn. The implication is that learning is a property only of high-enough dimensional systems.

The Design of Collectives of Agents to Control Non-Markovian Systems

NASA Technical Reports Server (NTRS)

Lawson, John W.; Wolpert, David H.; Clancy, Daniel (Technical Monitor)

2002-01-01

The 'Collective Intelligence' (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided 'world' utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional-'team games'. We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents' ability to learn. The implication is that 'learning' is a property only of high-enough dimensional systems.

Designing Agent Collectives For Systems With Markovian Dynamics

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Lawson, John W.; Clancy, Daniel (Technical Monitor)

2001-01-01

The "Collective Intelligence" (COIN) framework concerns the design of collectives of agents so that as those agents strive to maximize their individual utility functions, their interaction causes a provided "world" utility function concerning the entire collective to be also maximized. Here we show how to extend that framework to scenarios having Markovian dynamics when no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. Our approach transforms the (time-extended) argument of each agent's utility function before evaluating that function. This transformation has benefits in scenarios not involving Markovian dynamics, in particular scenarios where not all of the arguments of an agent's utility function are observable. We investigate this transformation in simulations involving both linear and quadratic (nonlinear) dynamics. In addition, we find that a certain subset of these transformations, which result in utilities that have low "opacity (analogous to having high signal to noise) but are not "factored" (analogous to not being incentive compatible), reliably improve performance over that arising with factored utilities. We also present a Taylor Series method for the fully general nonlinear case.

New approaches in agent-based modeling of complex financial systems

NASA Astrophysics Data System (ADS)

Chen, Ting-Ting; Zheng, Bo; Li, Yan; Jiang, Xiong-Fei

2017-12-01

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.

Collaborative environments for capability-based planning

NASA Astrophysics Data System (ADS)

McQuay, William K.

2005-05-01

Distributed collaboration is an emerging technology for the 21st century that will significantly change how business is conducted in the defense and commercial sectors. Collaboration involves two or more geographically dispersed entities working together to create a "product" by sharing and exchanging data, information, and knowledge. A product is defined broadly to include, for example, writing a report, creating software, designing hardware, or implementing robust systems engineering and capability planning processes in an organization. Collaborative environments provide the framework and integrate models, simulations, domain specific tools, and virtual test beds to facilitate collaboration between the multiple disciplines needed in the enterprise. The Air Force Research Laboratory (AFRL) is conducting a leading edge program in developing distributed collaborative technologies targeted to the Air Force's implementation of systems engineering for a simulation-aided acquisition and capability-based planning. The research is focusing on the open systems agent-based framework, product and process modeling, structural architecture, and the integration technologies - the glue to integrate the software components. In past four years, two live assessment events have been conducted to demonstrate the technology in support of research for the Air Force Agile Acquisition initiatives. The AFRL Collaborative Environment concept will foster a major cultural change in how the acquisition, training, and operational communities conduct business.

Brands as Intentional Agents Framework: How Perceived Intentions and Ability Can Map Brand Perception.

PubMed

Kervyn, Nicolas; Fiske, Susan T; Malone, Chris

2012-04-01

Building on the Stereotype Content Model, this paper introduces and tests the Brands as Intentional Agents Framework. A growing body of research suggests that consumers have relationships with brands that resemble relations between people. We propose that consumers perceive brands in the same way they perceive people. This approach allows us to explore how social perception theories and processes can predict brand purchase interest and loyalty. Brands as Intentional Agents Framework is based on a well-established social perception approach: the Stereotype Content Model. Two studies support the Brands as Intentional Agents Framework prediction that consumers assess a brand's perceived intentions and ability and that these perceptions elicit distinct emotions and drive differential brand behaviors. The research shows that human social interaction relationships translate to consumer-brand interactions in ways that are useful to inform brand positioning and brand communications.

Brands as Intentional Agents Framework: How Perceived Intentions and Ability Can Map Brand Perception

PubMed Central

Kervyn, Nicolas; Fiske, Susan T.; Malone, Chris

2013-01-01

Building on the Stereotype Content Model, this paper introduces and tests the Brands as Intentional Agents Framework. A growing body of research suggests that consumers have relationships with brands that resemble relations between people. We propose that consumers perceive brands in the same way they perceive people. This approach allows us to explore how social perception theories and processes can predict brand purchase interest and loyalty. Brands as Intentional Agents Framework is based on a well-established social perception approach: the Stereotype Content Model. Two studies support the Brands as Intentional Agents Framework prediction that consumers assess a brand’s perceived intentions and ability and that these perceptions elicit distinct emotions and drive differential brand behaviors. The research shows that human social interaction relationships translate to consumer-brand interactions in ways that are useful to inform brand positioning and brand communications. PMID:24403815

Optimal control in microgrid using multi-agent reinforcement learning.

PubMed

Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

2012-11-01

This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Engaging Youth Through Spatial Socio-Technical Storytelling, Participatory GIS, Agent-Based Modeling, Online Geogames and Action Projects

NASA Astrophysics Data System (ADS)

Poplin, A.; Shenk, L.; Krejci, C.; Passe, U.

2017-09-01

The main goal of this paper is to present the conceptual framework for engaging youth in urban planning activities that simultaneously create locally meaningful positive change. The framework for engaging youth interlinks the use of IT tools such as geographic information systems (GIS), agent-based modelling (ABM), online serious games, and mobile participatory geographic information systems with map-based storytelling and action projects. We summarize the elements of our framework and the first results gained in the program Community Growers established in a neighbourhood community of Des Moines, the capital of Iowa, USA. We conclude the paper with a discussion and future research directions.

Design and Simulation of Material-Integrated Distributed Sensor Processing with a Code-Based Agent Platform and Mobile Multi-Agent Systems

PubMed Central

Bosse, Stefan

2015-01-01

Multi-agent systems (MAS) can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG) model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container) and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques. PMID:25690550

Design and simulation of material-integrated distributed sensor processing with a code-based agent platform and mobile multi-agent systems.

PubMed

Bosse, Stefan

2015-02-16

Multi-agent systems (MAS) can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG) model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container) and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goldsmith, Steven Y.; Spires, Shannon V.

There are currently two proposed standards for agent communication languages, namely, KQML (Finin, Lobrou, and Mayfield 1994) and the FIPA ACL. Neither standard has yet achieved primacy, and neither has been evaluated extensively in an open environment such as the Internet. It seems prudent therefore to design a general-purpose agent communications facility for new agent architectures that is flexible yet provides an architecture that accepts many different specializations. In this paper we exhibit the salient features of an agent communications architecture based on distributed metaobjects. This architecture captures design commitments at a metaobject level, leaving the base-level design and implementationmore » up to the agent developer. The scope of the metamodel is broad enough to accommodate many different communication protocols, interaction protocols, and knowledge sharing regimes through extensions to the metaobject framework. We conclude that with a powerful distributed object substrate that supports metaobject communications, a general framework can be developed that will effectively enable different approaches to agent communications in the same agent system. We have implemented a KQML-based communications protocol and have several special-purpose interaction protocols under development.« less

Simulation-based intelligent robotic agent for Space Station Freedom

NASA Technical Reports Server (NTRS)

Biegl, Csaba A.; Springfield, James F.; Cook, George E.; Fernandez, Kenneth R.

1990-01-01

A robot control package is described which utilizes on-line structural simulation of robot manipulators and objects in their workspace. The model-based controller is interfaced with a high level agent-independent planner, which is responsible for the task-level planning of the robot's actions. Commands received from the agent-independent planner are refined and executed in the simulated workspace, and upon successful completion, they are transferred to the real manipulators.

A Participatory Agent-Based Simulation for Indoor Evacuation Supported by Google Glass

PubMed Central

Sánchez, Jesús M.; Carrera, Álvaro; Iglesias, Carlos Á.; Serrano, Emilio

2016-01-01

Indoor evacuation systems are needed for rescue and safety management. One of the challenges is to provide users with personalized evacuation routes in real time. To this end, this project aims at exploring the possibilities of Google Glass technology for participatory multiagent indoor evacuation simulations. Participatory multiagent simulation combines scenario-guided agents and humans equipped with Google Glass that coexist in a shared virtual space and jointly perform simulations. The paper proposes an architecture for participatory multiagent simulation in order to combine devices (Google Glass and/or smartphones) with an agent-based social simulator and indoor tracking services. PMID:27563911

Surgical model-view-controller simulation software framework for local and collaborative applications

PubMed Central

Sankaranarayanan, Ganesh; Halic, Tansel; Arikatla, Venkata Sreekanth; Lu, Zhonghua; De, Suvranu

2010-01-01

Purpose Surgical simulations require haptic interactions and collaboration in a shared virtual environment. A software framework for decoupled surgical simulation based on a multi-controller and multi-viewer model-view-controller (MVC) pattern was developed and tested. Methods A software framework for multimodal virtual environments was designed, supporting both visual interactions and haptic feedback while providing developers with an integration tool for heterogeneous architectures maintaining high performance, simplicity of implementation, and straightforward extension. The framework uses decoupled simulation with updates of over 1,000 Hz for haptics and accommodates networked simulation with delays of over 1,000 ms without performance penalty. Results The simulation software framework was implemented and was used to support the design of virtual reality-based surgery simulation systems. The framework supports the high level of complexity of such applications and the fast response required for interaction with haptics. The efficacy of the framework was tested by implementation of a minimally invasive surgery simulator. Conclusion A decoupled simulation approach can be implemented as a framework to handle simultaneous processes of the system at the various frame rates each process requires. The framework was successfully used to develop collaborative virtual environments (VEs) involving geographically distributed users connected through a network, with the results comparable to VEs for local users. PMID:20714933

Surgical model-view-controller simulation software framework for local and collaborative applications.

PubMed

Maciel, Anderson; Sankaranarayanan, Ganesh; Halic, Tansel; Arikatla, Venkata Sreekanth; Lu, Zhonghua; De, Suvranu

2011-07-01

Surgical simulations require haptic interactions and collaboration in a shared virtual environment. A software framework for decoupled surgical simulation based on a multi-controller and multi-viewer model-view-controller (MVC) pattern was developed and tested. A software framework for multimodal virtual environments was designed, supporting both visual interactions and haptic feedback while providing developers with an integration tool for heterogeneous architectures maintaining high performance, simplicity of implementation, and straightforward extension. The framework uses decoupled simulation with updates of over 1,000 Hz for haptics and accommodates networked simulation with delays of over 1,000 ms without performance penalty. The simulation software framework was implemented and was used to support the design of virtual reality-based surgery simulation systems. The framework supports the high level of complexity of such applications and the fast response required for interaction with haptics. The efficacy of the framework was tested by implementation of a minimally invasive surgery simulator. A decoupled simulation approach can be implemented as a framework to handle simultaneous processes of the system at the various frame rates each process requires. The framework was successfully used to develop collaborative virtual environments (VEs) involving geographically distributed users connected through a network, with the results comparable to VEs for local users.

Distributed adaptive neural network control for a class of heterogeneous nonlinear multi-agent systems subject to actuation failures

NASA Astrophysics Data System (ADS)

Cui, Bing; Zhao, Chunhui; Ma, Tiedong; Feng, Chi

2017-02-01

In this paper, the cooperative adaptive consensus tracking problem for heterogeneous nonlinear multi-agent systems on directed graph is addressed. Each follower is modelled as a general nonlinear system with the unknown and nonidentical nonlinear dynamics, disturbances and actuator failures. Cooperative fault tolerant neural network tracking controllers with online adaptive learning features are proposed to guarantee that all agents synchronise to the trajectory of one leader with bounded adjustable synchronisation errors. With the help of linear quadratic regulator-based optimal design, a graph-dependent Lyapunov proof provides error bounds that depend on the graph topology, one virtual matrix and some design parameters. Of particular interest is that if the control gain is selected appropriately, the proposed control scheme can be implemented in a unified framework no matter whether there are faults or not. Furthermore, the fault detection and isolation are not needed to implement. Finally, a simulation is given to verify the effectiveness of the proposed method.

Destruction of chemical warfare agents using metal-organic frameworks

NASA Astrophysics Data System (ADS)

Mondloch, Joseph E.; Katz, Michael J.; Isley, William C., III; Ghosh, Pritha; Liao, Peilin; Bury, Wojciech; Wagner, George W.; Hall, Morgan G.; Decoste, Jared B.; Peterson, Gregory W.; Snurr, Randall Q.; Cramer, Christopher J.; Hupp, Joseph T.; Farha, Omar K.

2015-05-01

Chemical warfare agents containing phosphonate ester bonds are among the most toxic chemicals known to mankind. Recent global military events, such as the conflict and disarmament in Syria, have brought into focus the need to find effective strategies for the rapid destruction of these banned chemicals. Solutions are needed for immediate personal protection (for example, the filtration and catalytic destruction of airborne versions of agents), bulk destruction of chemical weapon stockpiles, protection (via coating) of clothing, equipment and buildings, and containment of agent spills. Solid heterogeneous materials such as modified activated carbon or metal oxides exhibit many desirable characteristics for the destruction of chemical warfare agents. However, low sorptive capacities, low effective active site loadings, deactivation of the active site, slow degradation kinetics, and/or a lack of tailorability offer significant room for improvement in these materials. Here, we report a carefully chosen metal-organic framework (MOF) material featuring high porosity and exceptional chemical stability that is extraordinarily effective for the degradation of nerve agents and their simulants. Experimental and computational evidence points to Lewis-acidic ZrIV ions as the active sites and to their superb accessibility as a defining element of their efficacy.

Destruction of chemical warfare agents using metal-organic frameworks.

PubMed

Mondloch, Joseph E; Katz, Michael J; Isley, William C; Ghosh, Pritha; Liao, Peilin; Bury, Wojciech; Wagner, George W; Hall, Morgan G; DeCoste, Jared B; Peterson, Gregory W; Snurr, Randall Q; Cramer, Christopher J; Hupp, Joseph T; Farha, Omar K

2015-05-01

Chemical warfare agents containing phosphonate ester bonds are among the most toxic chemicals known to mankind. Recent global military events, such as the conflict and disarmament in Syria, have brought into focus the need to find effective strategies for the rapid destruction of these banned chemicals. Solutions are needed for immediate personal protection (for example, the filtration and catalytic destruction of airborne versions of agents), bulk destruction of chemical weapon stockpiles, protection (via coating) of clothing, equipment and buildings, and containment of agent spills. Solid heterogeneous materials such as modified activated carbon or metal oxides exhibit many desirable characteristics for the destruction of chemical warfare agents. However, low sorptive capacities, low effective active site loadings, deactivation of the active site, slow degradation kinetics, and/or a lack of tailorability offer significant room for improvement in these materials. Here, we report a carefully chosen metal-organic framework (MOF) material featuring high porosity and exceptional chemical stability that is extraordinarily effective for the degradation of nerve agents and their simulants. Experimental and computational evidence points to Lewis-acidic Zr(IV) ions as the active sites and to their superb accessibility as a defining element of their efficacy.

Finite-time and fixed-time leader-following consensus for multi-agent systems with discontinuous inherent dynamics

NASA Astrophysics Data System (ADS)

Ning, Boda; Jin, Jiong; Zheng, Jinchuan; Man, Zhihong

2018-06-01

This paper is concerned with finite-time and fixed-time consensus of multi-agent systems in a leader-following framework. Different from conventional leader-following tracking approaches where inherent dynamics satisfying the Lipschitz continuous condition is required, a more generalised case is investigated: discontinuous inherent dynamics. By nonsmooth techniques, a nonlinear protocol is first proposed to achieve the finite-time leader-following consensus. Then, based on fixed-time stability strategies, the fixed-time leader-following consensus problem is solved. An upper bound of settling time is obtained by using a new protocol, and such a bound is independent of initial states, thereby providing additional options for designers in practical scenarios where initial conditions are unavailable. Finally, numerical simulations are provided to demonstrate the effectiveness of the theoretical results.

Dshell++: A Component Based, Reusable Space System Simulation Framework

NASA Technical Reports Server (NTRS)

Lim, Christopher S.; Jain, Abhinandan

2009-01-01

This paper describes the multi-mission Dshell++ simulation framework for high fidelity, physics-based simulation of spacecraft, robotic manipulation and mobility systems. Dshell++ is a C++/Python library which uses modern script driven object-oriented techniques to allow component reuse and a dynamic run-time interface for complex, high-fidelity simulation of spacecraft and robotic systems. The goal of the Dshell++ architecture is to manage the inherent complexity of physicsbased simulations while supporting component model reuse across missions. The framework provides several features that support a large degree of simulation configurability and usability.

Automated numerical simulation of biological pattern formation based on visual feedback simulation framework

PubMed Central

Sun, Mingzhu; Xu, Hui; Zeng, Xingjuan; Zhao, Xin

2017-01-01

There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation. PMID:28225811

Automated numerical simulation of biological pattern formation based on visual feedback simulation framework.

PubMed

Sun, Mingzhu; Xu, Hui; Zeng, Xingjuan; Zhao, Xin

2017-01-01

There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation.

Final report for the endowment of simulator agents with human-like episodic memory LDRD.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Speed, Ann Elizabeth; Lippitt, Carl Edward; Thomas, Edward Victor

This report documents work undertaken to endow the cognitive framework currently under development at Sandia National Laboratories with a human-like memory for specific life episodes. Capabilities have been demonstrated within the context of three separate problem areas. The first year of the project developed a capability whereby simulated robots were able to utilize a record of shared experience to perform surveillance of a building to detect a source of smoke. The second year focused on simulations of social interactions providing a queriable record of interactions such that a time series of events could be constructed and reconstructed. The third yearmore » addressed tools to promote desktop productivity, creating a capability to query episodic logs in real time allowing the model of a user to build on itself based on observations of the user's behavior.« less

CulSim: A simulator of emergence and resilience of cultural diversity

NASA Astrophysics Data System (ADS)

Ulloa, Roberto

CulSim is an agent-based computer simulation software that allows further exploration of influential and recent models of emergence of cultural groups grounded in sociological theories. CulSim provides a collection of tools to analyze resilience of cultural diversity when events affect agents, institutions or global parameters of the simulations; upon combination, events can be used to approximate historical circumstances. The software provides a graphical and text-based user interface, and so makes this agent-based modeling methodology accessible to a variety of users from different research fields.

Modeling pedestrian shopping behavior using principles of bounded rationality: model comparison and validation

NASA Astrophysics Data System (ADS)

Zhu, Wei; Timmermans, Harry

2011-06-01

Models of geographical choice behavior have been dominantly based on rational choice models, which assume that decision makers are utility-maximizers. Rational choice models may be less appropriate as behavioral models when modeling decisions in complex environments in which decision makers may simplify the decision problem using heuristics. Pedestrian behavior in shopping streets is an example. We therefore propose a modeling framework for pedestrian shopping behavior incorporating principles of bounded rationality. We extend three classical heuristic rules (conjunctive, disjunctive and lexicographic rule) by introducing threshold heterogeneity. The proposed models are implemented using data on pedestrian behavior in Wang Fujing Street, the city center of Beijing, China. The models are estimated and compared with multinomial logit models and mixed logit models. Results show that the heuristic models are the best for all the decisions that are modeled. Validation tests are carried out through multi-agent simulation by comparing simulated spatio-temporal agent behavior with the observed pedestrian behavior. The predictions of heuristic models are slightly better than those of the multinomial logit models.

A Duet for one☆

PubMed Central

Friston, Karl; Frith, Christopher

2015-01-01

This paper considers communication in terms of inference about the behaviour of others (and our own behaviour). It is based on the premise that our sensations are largely generated by other agents like ourselves. This means, we are trying to infer how our sensations are caused by others, while they are trying to infer our behaviour: for example, in the dialogue between two speakers. We suggest that the infinite regress induced by modelling another agent – who is modelling you – can be finessed if you both possess the same model. In other words, the sensations caused by others and oneself are generated by the same process. This leads to a view of communication based upon a narrative that is shared by agents who are exchanging sensory signals. Crucially, this narrative transcends agency – and simply involves intermittently attending to and attenuating sensory input. Attending to sensations enables the shared narrative to predict the sensations generated by another (i.e. to listen), while attenuating sensory input enables one to articulate the narrative (i.e. to speak). This produces a reciprocal exchange of sensory signals that, formally, induces a generalised synchrony between internal (neuronal) brain states generating predictions in both agents. We develop the arguments behind this perspective, using an active (Bayesian) inference framework and offer some simulations (of birdsong) as proof of principle. PMID:25563935

Competency-Based Training and Simulation: Making a "Valid" Argument.

PubMed

Noureldin, Yasser A; Lee, Jason Y; McDougall, Elspeth M; Sweet, Robert M

2018-02-01

The use of simulation as an assessment tool is much more controversial than is its utility as an educational tool. However, without valid simulation-based assessment tools, the ability to objectively assess technical skill competencies in a competency-based medical education framework will remain challenging. The current literature in urologic simulation-based training and assessment uses a definition and framework of validity that is now outdated. This is probably due to the absence of awareness rather than an absence of comprehension. The following review article provides the urologic community an updated taxonomy on validity theory as it relates to simulation-based training and assessments and translates our simulation literature to date into this framework. While the old taxonomy considered validity as distinct subcategories and focused on the simulator itself, the modern taxonomy, for which we translate the literature evidence, considers validity as a unitary construct with a focus on interpretation of simulator data/scores.

A method for evaluating cognitively informed micro-targeted campaign strategies: An agent-based model proof of principle

PubMed Central

Pilditch, Toby D.

2018-01-01

In political campaigns, perceived candidate credibility influences the persuasiveness of messages. In campaigns aiming to influence people’s beliefs, micro-targeted campaigns (MTCs) that target specific voters using their psychological profile have become increasingly prevalent. It remains open how effective MTCs are, notably in comparison to population-targeted campaign strategies. Using an agent-based model, the paper applies recent insights from cognitive models of persuasion, extending them to the societal level in a novel framework for exploring political campaigning. The paper provides an initial treatment of the complex dynamics of population level political campaigning in a psychologically informed manner. Model simulations show that MTCs can take advantage of the psychology of the electorate by targeting voters favourable disposed towards the candidate. Relative to broad campaigning, MTCs allow for efficient and adaptive management of complex campaigns. Findings show that disliked MTC candidates can beat liked population-targeting candidates, pointing to societal questions concerning campaign regulations. PMID:29634722

A method for evaluating cognitively informed micro-targeted campaign strategies: An agent-based model proof of principle.

PubMed

Madsen, Jens Koed; Pilditch, Toby D

2018-01-01

In political campaigns, perceived candidate credibility influences the persuasiveness of messages. In campaigns aiming to influence people's beliefs, micro-targeted campaigns (MTCs) that target specific voters using their psychological profile have become increasingly prevalent. It remains open how effective MTCs are, notably in comparison to population-targeted campaign strategies. Using an agent-based model, the paper applies recent insights from cognitive models of persuasion, extending them to the societal level in a novel framework for exploring political campaigning. The paper provides an initial treatment of the complex dynamics of population level political campaigning in a psychologically informed manner. Model simulations show that MTCs can take advantage of the psychology of the electorate by targeting voters favourable disposed towards the candidate. Relative to broad campaigning, MTCs allow for efficient and adaptive management of complex campaigns. Findings show that disliked MTC candidates can beat liked population-targeting candidates, pointing to societal questions concerning campaign regulations.

Distributed robust finite-time nonlinear consensus protocols for multi-agent systems

NASA Astrophysics Data System (ADS)

Zuo, Zongyu; Tie, Lin

2016-04-01

This paper investigates the robust finite-time consensus problem of multi-agent systems in networks with undirected topology. Global nonlinear consensus protocols augmented with a variable structure are constructed with the aid of Lyapunov functions for each single-integrator agent dynamics in the presence of external disturbances. In particular, it is shown that the finite settling time of the proposed general framework for robust consensus design is upper bounded for any initial condition. This makes it possible for network consensus problems to design and estimate the convergence time offline for a multi-agent team with a given undirected information flow. Finally, simulation results are presented to demonstrate the performance and effectiveness of our finite-time protocols.

Discrete Element Framework for Modelling Extracellular Matrix, Deformable Cells and Subcellular Components

PubMed Central

Gardiner, Bruce S.; Wong, Kelvin K. L.; Joldes, Grand R.; Rich, Addison J.; Tan, Chin Wee; Burgess, Antony W.; Smith, David W.

2015-01-01

This paper presents a framework for modelling biological tissues based on discrete particles. Cell components (e.g. cell membranes, cell cytoskeleton, cell nucleus) and extracellular matrix (e.g. collagen) are represented using collections of particles. Simple particle to particle interaction laws are used to simulate and control complex physical interaction types (e.g. cell-cell adhesion via cadherins, integrin basement membrane attachment, cytoskeletal mechanical properties). Particles may be given the capacity to change their properties and behaviours in response to changes in the cellular microenvironment (e.g., in response to cell-cell signalling or mechanical loadings). Each particle is in effect an ‘agent’, meaning that the agent can sense local environmental information and respond according to pre-determined or stochastic events. The behaviour of the proposed framework is exemplified through several biological problems of ongoing interest. These examples illustrate how the modelling framework allows enormous flexibility for representing the mechanical behaviour of different tissues, and we argue this is a more intuitive approach than perhaps offered by traditional continuum methods. Because of this flexibility, we believe the discrete modelling framework provides an avenue for biologists and bioengineers to explore the behaviour of tissue systems in a computational laboratory. PMID:26452000

Distributed Optimization

NASA Technical Reports Server (NTRS)

Macready, William; Wolpert, David

2005-01-01

We demonstrate a new framework for analyzing and controlling distributed systems, by solving constrained optimization problems with an algorithm based on that framework. The framework is ar. information-theoretic extension of conventional full-rationality game theory to allow bounded rational agents. The associated optimization algorithm is a game in which agents control the variables of the optimization problem. They do this by jointly minimizing a Lagrangian of (the probability distribution of) their joint state. The updating of the Lagrange parameters in that Lagrangian is a form of automated annealing, one that focuses the multi-agent system on the optimal pure strategy. We present computer experiments for the k-sat constraint satisfaction problem and for unconstrained minimization of NK functions.

Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach.

PubMed

Laghari, Samreen; Niazi, Muaz A

2016-01-01

Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.

Portfolio of automated trading systems: complexity and learning set size issues.

PubMed

Raudys, Sarunas

2013-03-01

In this paper, we consider using profit/loss histories of multiple automated trading systems (ATSs) as N input variables in portfolio management. By means of multivariate statistical analysis and simulation studies, we analyze the influences of sample size (L) and input dimensionality on the accuracy of determining the portfolio weights. We find that degradation in portfolio performance due to inexact estimation of N means and N(N - 1)/2 correlations is proportional to N/L; however, estimation of N variances does not worsen the result. To reduce unhelpful sample size/dimensionality effects, we perform a clustering of N time series and split them into a small number of blocks. Each block is composed of mutually correlated ATSs. It generates an expert trading agent based on a nontrainable 1/N portfolio rule. To increase the diversity of the expert agents, we use training sets of different lengths for clustering. In the output of the portfolio management system, the regularized mean-variance framework-based fusion agent is developed in each walk-forward step of an out-of-sample portfolio validation experiment. Experiments with the real financial data (2003-2012) confirm the effectiveness of the suggested approach.

An Agent-Based Simulation for Investigating the Impact of Stereotypes on Task-Oriented Group Formation

NASA Astrophysics Data System (ADS)

Maghami, Mahsa; Sukthankar, Gita

In this paper, we introduce an agent-based simulation for investigating the impact of social factors on the formation and evolution of task-oriented groups. Task-oriented groups are created explicitly to perform a task, and all members derive benefits from task completion. However, even in cases when all group members act in a way that is locally optimal for task completion, social forces that have mild effects on choice of associates can have a measurable impact on task completion performance. In this paper, we show how our simulation can be used to model the impact of stereotypes on group formation. In our simulation, stereotypes are based on observable features, learned from prior experience, and only affect an agent's link formation preferences. Even without assuming stereotypes affect the agents' willingness or ability to complete tasks, the long-term modifications that stereotypes have on the agents' social network impair the agents' ability to form groups with sufficient diversity of skills, as compared to agents who form links randomly. An interesting finding is that this effect holds even in cases where stereotype preference and skill existence are completely uncorrelated.

An event-triggered control approach for the leader-tracking problem with heterogeneous agents

NASA Astrophysics Data System (ADS)

Garcia, Eloy; Cao, Yongcan; Casbeer, David W.

2018-05-01

This paper presents an event-triggered control and communication framework for the cooperative leader-tracking problem with communication constraints. Continuous communication among agents is not assumed in this work and decentralised event-based strategies are proposed for agents with heterogeneous linear dynamics. Also, the leader dynamics are unknown and only intermittent measurements of its states are obtained by a subset of the followers. The event-based method not only represents a way to restrict communication among agents, but it also provides a decentralised scheme for scheduling information broadcasts. Notably, each agent is able to determine its own broadcasting instants independently of any other agent in the network. In an extension, the case where transmission of information is affected by time-varying communication delays is addressed. Finally, positive lower-bounds on the inter-event time intervals are obtained in order to show that Zeno behaviour does not exist and, therefore, continuous exchange of information is never needed in this framework.

Computational modelling of cell chain migration reveals mechanisms that sustain follow-the-leader behaviour

PubMed Central

Wynn, Michelle L.; Kulesa, Paul M.; Schnell, Santiago

2012-01-01

Follow-the-leader chain migration is a striking cell migratory behaviour observed during vertebrate development, adult neurogenesis and cancer metastasis. Although cell–cell contact and extracellular matrix (ECM) cues have been proposed to promote this phenomenon, mechanisms that underlie chain migration persistence remain unclear. Here, we developed a quantitative agent-based modelling framework to test mechanistic hypotheses of chain migration persistence. We defined chain migration and its persistence based on evidence from the highly migratory neural crest model system, where cells within a chain extend and retract filopodia in short-lived cell contacts and move together as a collective. In our agent-based simulations, we began with a set of agents arranged as a chain and systematically probed the influence of model parameters to identify factors critical to the maintenance of the chain migration pattern. We discovered that chain migration persistence requires a high degree of directional bias in both lead and follower cells towards the target. Chain migration persistence was also promoted when lead cells maintained cell contact with followers, but not vice-versa. Finally, providing a path of least resistance in the ECM was not sufficient alone to drive chain persistence. Our results indicate that chain migration persistence depends on the interplay of directional cell movement and biased cell–cell contact. PMID:22219399

A Semantic Grid Oriented to E-Tourism

NASA Astrophysics Data System (ADS)

Zhang, Xiao Ming

With increasing complexity of tourism business models and tasks, there is a clear need of the next generation e-Tourism infrastructure to support flexible automation, integration, computation, storage, and collaboration. Currently several enabling technologies such as semantic Web, Web service, agent and grid computing have been applied in the different e-Tourism applications, however there is no a unified framework to be able to integrate all of them. So this paper presents a promising e-Tourism framework based on emerging semantic grid, in which a number of key design issues are discussed including architecture, ontologies structure, semantic reconciliation, service and resource discovery, role based authorization and intelligent agent. The paper finally provides the implementation of the framework.

Parallel computing in enterprise modeling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goldsby, Michael E.; Armstrong, Robert C.; Shneider, Max S.

2008-08-01

This report presents the results of our efforts to apply high-performance computing to entity-based simulations with a multi-use plugin for parallel computing. We use the term 'Entity-based simulation' to describe a class of simulation which includes both discrete event simulation and agent based simulation. What simulations of this class share, and what differs from more traditional models, is that the result sought is emergent from a large number of contributing entities. Logistic, economic and social simulations are members of this class where things or people are organized or self-organize to produce a solution. Entity-based problems never have an a priorimore » ergodic principle that will greatly simplify calculations. Because the results of entity-based simulations can only be realized at scale, scalable computing is de rigueur for large problems. Having said that, the absence of a spatial organizing principal makes the decomposition of the problem onto processors problematic. In addition, practitioners in this domain commonly use the Java programming language which presents its own problems in a high-performance setting. The plugin we have developed, called the Parallel Particle Data Model, overcomes both of these obstacles and is now being used by two Sandia frameworks: the Decision Analysis Center, and the Seldon social simulation facility. While the ability to engage U.S.-sized problems is now available to the Decision Analysis Center, this plugin is central to the success of Seldon. Because Seldon relies on computationally intensive cognitive sub-models, this work is necessary to achieve the scale necessary for realistic results. With the recent upheavals in the financial markets, and the inscrutability of terrorist activity, this simulation domain will likely need a capability with ever greater fidelity. High-performance computing will play an important part in enabling that greater fidelity.« less

A Primer for Agent-Based Simulation and Modeling in Transportation Applications

DOT National Transportation Integrated Search

2013-11-01

Agent-based modeling and simulation (ABMS) methods have been applied in a spectrum of research domains. This primer focuses on ABMS in the transportation interdisciplinary domain, describes the basic concepts of ABMS and the recent progress of ABMS i...

Agent-Based Simulation of Learning Dissemination in a Project-Based Learning Context Considering the Human Aspects

ERIC Educational Resources Information Center

Seman, Laio Oriel; Hausmann, Romeu; Bezerra, Eduardo Augusto

2018-01-01

Contribution: This paper presents the "PBL classroom model," an agent-based simulation (ABS) that allows testing of several scenarios of a project-based learning (PBL) application by considering different levels of soft-skills, and students' perception of the methodology. Background: While the community has made great advances in…

Physics-based agent to simulant correlations for vapor phase mass transport.

PubMed

Willis, Matthew P; Varady, Mark J; Pearl, Thomas P; Fouse, Janet C; Riley, Patrick C; Mantooth, Brent A; Lalain, Teri A

2013-12-15

Chemical warfare agent simulants are often used as an agent surrogate to perform environmental testing, mitigating exposure hazards. This work specifically addresses the assessment of downwind agent vapor concentration resulting from an evaporating simulant droplet. A previously developed methodology was used to estimate the mass diffusivities of the chemical warfare agent simulants methyl salicylate, 2-chloroethyl ethyl sulfide, di-ethyl malonate, and chloroethyl phenyl sulfide. Along with the diffusivity of the chemical warfare agent bis(2-chloroethyl) sulfide, the simulant diffusivities were used in an advection-diffusion model to predict the vapor concentrations downwind from an evaporating droplet of each chemical at various wind velocities and temperatures. The results demonstrate that the simulant-to-agent concentration ratio and the corresponding vapor pressure ratio are equivalent under certain conditions. Specifically, the relationship is valid within ranges of measurement locations relative to the evaporating droplet and observation times. The valid ranges depend on the relative transport properties of the agent and simulant, and whether vapor transport is diffusion or advection dominant. Published by Elsevier B.V.

Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

NASA Astrophysics Data System (ADS)

Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

2014-09-01

Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

Action understanding as inverse planning.

PubMed

Baker, Chris L; Saxe, Rebecca; Tenenbaum, Joshua B

2009-12-01

Humans are adept at inferring the mental states underlying other agents' actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents' behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent's behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an "intentional stance" [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a "teleological stance" [Gergely, G., Nádasdy, Z., Csibra, G., & Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165-193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.

Ultra-fast consensus of discrete-time multi-agent systems with multi-step predictive output feedback

NASA Astrophysics Data System (ADS)

Zhang, Wenle; Liu, Jianchang

2016-04-01

This article addresses the ultra-fast consensus problem of high-order discrete-time multi-agent systems based on a unified consensus framework. A novel multi-step predictive output mechanism is proposed under a directed communication topology containing a spanning tree. By predicting the outputs of a network several steps ahead and adding this information into the consensus protocol, it is shown that the asymptotic convergence factor is improved by a power of q + 1 compared to the routine consensus. The difficult problem of selecting the optimal control gain is solved well by introducing a variable called convergence step. In addition, the ultra-fast formation achievement is studied on the basis of this new consensus protocol. Finally, the ultra-fast consensus with respect to a reference model and robust consensus is discussed. Some simulations are performed to illustrate the effectiveness of the theoretical results.

Integrated consensus-based frameworks for unmanned vehicle routing and targeting assignment

NASA Astrophysics Data System (ADS)

Barnawi, Waleed T.

Unmanned aerial vehicles (UAVs) are increasingly deployed in complex and dynamic environments to perform multiple tasks cooperatively with other UAVs that contribute to overarching mission effectiveness. Studies by the Department of Defense (DoD) indicate future operations may include anti-access/area-denial (A2AD) environments which limit human teleoperator decision-making and control. This research addresses the problem of decentralized vehicle re-routing and task reassignments through consensus-based UAV decision-making. An Integrated Consensus-Based Framework (ICF) is formulated as a solution to the combined single task assignment problem and vehicle routing problem. The multiple assignment and vehicle routing problem is solved with the Integrated Consensus-Based Bundle Framework (ICBF). The frameworks are hierarchically decomposed into two levels. The bottom layer utilizes the renowned Dijkstra's Algorithm. The top layer addresses task assignment with two methods. The single assignment approach is called the Caravan Auction Algorithm (CarA) Algorithm. This technique extends the Consensus-Based Auction Algorithm (CBAA) to provide awareness for task completion by agents and adopt abandoned tasks. The multiple assignment approach called the Caravan Auction Bundle Algorithm (CarAB) extends the Consensus-Based Bundle Algorithm (CBBA) by providing awareness for lost resources, prioritizing remaining tasks, and adopting abandoned tasks. Research questions are investigated regarding the novelty and performance of the proposed frameworks. Conclusions regarding the research questions will be provided through hypothesis testing. Monte Carlo simulations will provide evidence to support conclusions regarding the research hypotheses for the proposed frameworks. The approach provided in this research addresses current and future military operations for unmanned aerial vehicles. However, the general framework implied by the proposed research is adaptable to any unmanned vehicle. Civil applications that involve missions where human observability would be limited could benefit from the independent UAV task assignment, such as exploration and fire surveillance are also notable uses for this approach.

Operational framework for quantum measurement simulability

NASA Astrophysics Data System (ADS)

Guerini, Leonardo; Bavaresco, Jessica; Terra Cunha, Marcelo; Acín, Antonio

2017-09-01

We introduce a framework for simulating quantum measurements based on classical processing of a set of accessible measurements. Well-known concepts such as joint measurability and projective simulability naturally emerge as particular cases of our framework, but our study also leads to novel results and questions. First, a generalisation of joint measurability is derived, which yields a hierarchy for the incompatibility of sets of measurements. A similar hierarchy is defined based on the number of outcomes necessary to perform a simulation of a given measurement. This general approach also allows us to identify connections between different kinds of simulability and, in particular, we characterise the qubit measurements that are projective-simulable in terms of joint measurability. Finally, we discuss how our framework can be interpreted in the context of resource theories.

A Framework for Simulating Turbine-Based Combined-Cycle Inlet Mode-Transition

NASA Technical Reports Server (NTRS)

Le, Dzu K.; Vrnak, Daniel R.; Slater, John W.; Hessel, Emil O.

2012-01-01

A simulation framework based on the Memory-Mapped-Files technique was created to operate multiple numerical processes in locked time-steps and send I/O data synchronously across to one-another to simulate system-dynamics. This simulation scheme is currently used to study the complex interactions between inlet flow-dynamics, variable-geometry actuation mechanisms, and flow-controls in the transition from the supersonic to hypersonic conditions and vice-versa. A study of Mode-Transition Control for a high-speed inlet wind-tunnel model with this MMF-based framework is presented to illustrate this scheme and demonstrate its usefulness in simulating supersonic and hypersonic inlet dynamics and controls or other types of complex systems.

Agent-based and phylogenetic analyses reveal how HIV-1 moves between risk groups: injecting drug users sustain the heterosexual epidemic in Latvia

PubMed Central

Graw, Frederik; Leitner, Thomas; Ribeiro, Ruy M.

2012-01-01

Injecting drug users (IDU) are a driving force for the spread of HIV-1 in Latvia and other Baltic States, accounting for a majority of cases. However, in recent years, heterosexual cases have increased disproportionately. It is unclear how the changes in incidence patterns in Latvia can be explained, and how important IDU are for the heterosexual sub-epidemic. We introduce a novel epidemic model and use phylogenetic analyses in parallel to examine the spread of HIV-1 in Latvia between 1987 and 2010. Using a hybrid framework with a mean-field description for the susceptible population and an agent-based model for the infecteds, we track infected individuals and follow transmission histories dynamically formed during the simulation. The agent-based simulations and the phylogenetic analysis show that more than half of the heterosexual transmissions in Latvia were caused by IDU, which sustain the heterosexual epidemic. Indeed, we find that heterosexual clusters are characterized by short transmission chains with up to 63% of the chains dying out after the first introduction. In the simulations, the distribution of transmission chain sizes follows a power law distribution, which is confirmed by the phylogenetic data. Our models indicate that frequent introductions reduced the extinction probability of an autonomously spreading heterosexual HIV-1 epidemic, which now has the potential to dominate the spread of the overall epidemic in the future. Furthermore, our model shows that social heterogeneity of the susceptible population can explain the shift in HIV-1 incidence in Latvia over the course of the epidemic. Thus, the decrease in IDU incidence may be due to local heterogeneities in transmission, rather than the implementation of control measures. Increases in susceptibles, through social or geographic movement of IDU, could lead to a boost in HIV-1 infections in this risk group. Targeting individuals that bridge social groups would help prevent further spread of the epidemic. PMID:22664069

Agent-based and phylogenetic analyses reveal how HIV-1 moves between risk groups: injecting drug users sustain the heterosexual epidemic in Latvia.

PubMed

Graw, Frederik; Leitner, Thomas; Ribeiro, Ruy M

2012-06-01

Injecting drug users (IDUs) are a driving force for the spread of HIV-1 in Latvia and other Baltic States, accounting for a majority of cases. However, in recent years, heterosexual cases have increased disproportionately. It is unclear how the changes in incidence patterns in Latvia can be explained, and how important IDUs are for the heterosexual sub-epidemic. We introduce a novel epidemic model and use phylogenetic analyses in parallel to examine the spread of HIV-1 in Latvia between 1987 and 2010. Using a hybrid framework with a mean-field description for the susceptible population and an agent-based model for the infecteds, we track infected individuals and follow transmission histories dynamically formed during the simulation. The agent-based simulations and the phylogenetic analysis show that more than half of the heterosexual transmissions in Latvia were caused by IDU, which sustain the heterosexual epidemic. Indeed, we find that heterosexual clusters are characterized by short transmission chains with up to 63% of the chains dying out after the first introduction. In the simulations, the distribution of transmission chain sizes follows a power law distribution, which is confirmed by the phylogenetic data. Our models indicate that frequent introductions reduced the extinction probability of an autonomously spreading heterosexual HIV-1 epidemic, which now has the potential to dominate the spread of the overall epidemic in the future. Furthermore, our model shows that social heterogeneity of the susceptible population can explain the shift in HIV-1 incidence in Latvia over the course of the epidemic. Thus, the decrease in IDU incidence may be due to local heterogeneities in transmission, rather than the implementation of control measures. Increases in susceptibles, through social or geographic movement of IDU, could lead to a boost in HIV-1 infections in this risk group. Targeting individuals that bridge social groups would help prevent further spread of the epidemic. Copyright © 2012 Elsevier B.V. All rights reserved.

An Agent-Based Modeling Template for a Cohort of Veterans with Diabetic Retinopathy.

PubMed

Day, Theodore Eugene; Ravi, Nathan; Xian, Hong; Brugh, Ann

2013-01-01

Agent-based models are valuable for examining systems where large numbers of discrete individuals interact with each other, or with some environment. Diabetic Veterans seeking eye care at a Veterans Administration hospital represent one such cohort. The objective of this study was to develop an agent-based template to be used as a model for a patient with diabetic retinopathy (DR). This template may be replicated arbitrarily many times in order to generate a large cohort which is representative of a real-world population, upon which in-silico experimentation may be conducted. Agent-based template development was performed in java-based computer simulation suite AnyLogic Professional 6.6. The model was informed by medical data abstracted from 535 patient records representing a retrospective cohort of current patients of the VA St. Louis Healthcare System Eye clinic. Logistic regression was performed to determine the predictors associated with advancing stages of DR. Predicted probabilities obtained from logistic regression were used to generate the stage of DR in the simulated cohort. The simulated cohort of DR patients exhibited no significant deviation from the test population of real-world patients in proportion of stage of DR, duration of diabetes mellitus (DM), or the other abstracted predictors. Simulated patients after 10 years were significantly more likely to exhibit proliferative DR (P<0.001). Agent-based modeling is an emerging platform, capable of simulating large cohorts of individuals based on manageable data abstraction efforts. The modeling method described may be useful in simulating many different conditions where course of disease is described in categorical stages.

Framework to model neutral particle flux in convex high aspect ratio structures using one-dimensional radiosity

NASA Astrophysics Data System (ADS)

Manstetten, Paul; Filipovic, Lado; Hössinger, Andreas; Weinbub, Josef; Selberherr, Siegfried

2017-02-01

We present a computationally efficient framework to compute the neutral flux in high aspect ratio structures during three-dimensional plasma etching simulations. The framework is based on a one-dimensional radiosity approach and is applicable to simulations of convex rotationally symmetric holes and convex symmetric trenches with a constant cross-section. The framework is intended to replace the full three-dimensional simulation step required to calculate the neutral flux during plasma etching simulations. Especially for high aspect ratio structures, the computational effort, required to perform the full three-dimensional simulation of the neutral flux at the desired spatial resolution, conflicts with practical simulation time constraints. Our results are in agreement with those obtained by three-dimensional Monte Carlo based ray tracing simulations for various aspect ratios and convex geometries. With this framework we present a comprehensive analysis of the influence of the geometrical properties of high aspect ratio structures as well as of the particle sticking probability on the neutral particle flux.

An Integrated Framework for Modeling Air Carrier Behavior, Policy, and Impacts in the U.S. Air Transportation System

NASA Technical Reports Server (NTRS)

Horio, Brant M.; Kumar, Vivek; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson M.

2015-01-01

The implementation of the Next Generation Air Transportation System (NextGen) in the United States is an ongoing challenge for policymakers due to the complexity of the air transportation system (ATS) with its broad array of stakeholders and dynamic interdependencies between them. The successful implementation of NextGen has a hard dependency on the active participation of U.S. commercial airlines. To assist policymakers in identifying potential policy designs that facilitate the implementation of NextGen, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework called the Air Transportation System Evolutionary Simulation (ATS-EVOS). This framework integrates large empirical data sets with multiple specialized models to simulate the evolution of the airline response to potential future policies and explore consequential impacts on ATS performance and market dynamics. In the ATS-EVOS configuration presented here, we leverage the Transportation Systems Analysis Model (TSAM), the Airline Evolutionary Simulation (AIRLINE-EVOS), the Airspace Concept Evaluation System (ACES), and the Aviation Environmental Design Tool (AEDT), all of which enable this research to comprehensively represent the complex facets of the ATS and its participants. We validated this baseline configuration of ATS-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments that explored potential implementations of a carbon tax, congestion pricing policy, and the dynamics for equipage of new technology by airlines. These experiments demonstrated ATS-EVOS's capabilities in responding to a wide range of potential NextGen-related policies and utility for decision makers to gain insights for effective policy design.

An agent-based method for simulating porous fluid-saturated structures with indistinguishable components

NASA Astrophysics Data System (ADS)

Kashani, Jamal; Pettet, Graeme John; Gu, YuanTong; Zhang, Lihai; Oloyede, Adekunle

2017-10-01

Single-phase porous materials contain multiple components that intermingle up to the ultramicroscopic level. Although the structures of the porous materials have been simulated with agent-based methods, the results of the available methods continue to provide patterns of distinguishable solid and fluid agents which do not represent materials with indistinguishable phases. This paper introduces a new agent (hybrid agent) and category of rules (intra-agent rule) that can be used to create emergent structures that would more accurately represent single-phase structures and materials. The novel hybrid agent carries the characteristics of system's elements and it is capable of changing within itself, while also responding to its neighbours as they also change. As an example, the hybrid agent under one-dimensional cellular automata formalism in a two-dimensional domain is used to generate patterns that demonstrate the striking morphological and characteristic similarities with the porous saturated single-phase structures where each agent of the ;structure; carries semi-permeability property and consists of both fluid and solid in space and at all times. We conclude that the ability of the hybrid agent to change locally provides an enhanced protocol to simulate complex porous structures such as biological tissues which could facilitate models for agent-based techniques and numerical methods.

Iterated learning and the evolution of language.

PubMed

Kirby, Simon; Griffiths, Tom; Smith, Kenny

2014-10-01

Iterated learning describes the process whereby an individual learns their behaviour by exposure to another individual's behaviour, who themselves learnt it in the same way. It can be seen as a key mechanism of cultural evolution. We review various methods for understanding how behaviour is shaped by the iterated learning process: computational agent-based simulations; mathematical modelling; and laboratory experiments in humans and non-human animals. We show how this framework has been used to explain the origins of structure in language, and argue that cultural evolution must be considered alongside biological evolution in explanations of language origins. Copyright © 2014 Elsevier Ltd. All rights reserved.

An Agent-Based Cockpit Task Management System

NASA Technical Reports Server (NTRS)

Funk, Ken

1997-01-01

An agent-based program to facilitate Cockpit Task Management (CTM) in commercial transport aircraft is developed and evaluated. The agent-based program called the AgendaManager (AMgr) is described and evaluated in a part-task simulator study using airline pilots.

Modeling marine oily wastewater treatment by a probabilistic agent-based approach.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Ye, Xudong

2018-02-01

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Performance Evaluation of a SLA Negotiation Control Protocol for Grid Networks

NASA Astrophysics Data System (ADS)

Cergol, Igor; Mirchandani, Vinod; Verchere, Dominique

A framework for an autonomous negotiation control protocol for service delivery is crucial to enable the support of heterogeneous service level agreements (SLAs) that will exist in distributed environments. We have first given a gist of our augmented service negotiation protocol to support distinct service elements. The augmentations also encompass related composition of the services and negotiation with several service providers simultaneously. All the incorporated augmentations will enable to consolidate the service negotiation operations for telecom networks, which are evolving towards Grid networks. Furthermore, our autonomous negotiation protocol is based on a distributed multi-agent framework to create an open market for Grid services. Second, we have concisely presented key simulation results of our work in progress. The results exhibit the usefulness of our negotiation protocol for realistic scenarios that involves different background traffic loading, message sizes and traffic flow asymmetry between background and negotiation traffics.

A cognitive information processing framework for distributed sensor networks

NASA Astrophysics Data System (ADS)

Wang, Feiyi; Qi, Hairong

2004-09-01

In this paper, we present a cognitive agent framework (CAF) based on swarm intelligence and self-organization principles, and demonstrate it through collaborative processing for target classification in sensor networks. The framework involves integrated designs to provide both cognitive behavior at the organization level to conquer complexity and reactive behavior at the individual agent level to retain simplicity. The design tackles various problems in the current information processing systems, including overly complex systems, maintenance difficulties, increasing vulnerability to attack, lack of capability to tolerate faults, and inability to identify and cope with low-frequency patterns. An important and distinguishing point of the presented work from classical AI research is that the acquired intelligence does not pertain to distinct individuals but to groups. It also deviates from multi-agent systems (MAS) due to sheer quantity of extremely simple agents we are able to accommodate, to the degree that some loss of coordination messages and behavior of faulty/compromised agents will not affect the collective decision made by the group.

Assessment of urban pluvial flood risk and efficiency of adaptation options through simulations - A new generation of urban planning tools

NASA Astrophysics Data System (ADS)

Löwe, Roland; Urich, Christian; Sto. Domingo, Nina; Mark, Ole; Deletic, Ana; Arnbjerg-Nielsen, Karsten

2017-07-01

We present a new framework for flexible testing of flood risk adaptation strategies in a variety of urban development and climate scenarios. This framework couples the 1D-2D hydrodynamic simulation package MIKE FLOOD with the agent-based urban development model DAnCE4Water and provides the possibility to systematically test various flood risk adaptation measures ranging from large infrastructure changes over decentralised water management to urban planning policies. We have tested the framework in a case study in Melbourne, Australia considering 9 scenarios for urban development and climate and 32 potential combinations of flood adaptation measures. We found that the performance of adaptation measures strongly depended on the considered climate and urban development scenario and the other implementation measures implemented, suggesting that adaptive strategies are preferable over one-off investments. Urban planning policies proved to be an efficient means for the reduction of flood risk, while implementing property buyback and pipe increases in a guideline-oriented manner was too costly. Random variations in location and time point of urban development could have significant impact on flood risk and would in some cases outweigh the benefits of less efficient adaptation strategies. The results of our setup can serve as an input for robust decision making frameworks and thus support the identification of flood risk adaptation measures that are economically efficient and robust to variations of climate and urban layout.

Reinforcement Learning Multi-Agent Modeling of Decision-Making Agents for the Study of Transboundary Surface Water Conflicts with Application to the Syr Darya River Basin

NASA Astrophysics Data System (ADS)

Riegels, N.; Siegfried, T.; Pereira Cardenal, S. J.; Jensen, R. A.; Bauer-Gottwein, P.

2008-12-01

In most economics--driven approaches to optimizing water use at the river basin scale, the system is modelled deterministically with the goal of maximizing overall benefits. However, actual operation and allocation decisions must be made under hydrologic and economic uncertainty. In addition, river basins often cross political boundaries, and different states may not be motivated to cooperate so as to maximize basin- scale benefits. Even within states, competing agents such as irrigation districts, municipal water agencies, and large industrial users may not have incentives to cooperate to realize efficiency gains identified in basin- level studies. More traditional simulation--optimization approaches assume pre-commitment by individual agents and stakeholders and unconditional compliance on each side. While this can help determine attainable gains and tradeoffs from efficient management, such hardwired policies do not account for dynamic feedback between agents themselves or between agents and their environments (e.g. due to climate change etc.). In reality however, we are dealing with an out-of-equilibrium multi-agent system, where there is neither global knowledge nor global control, but rather continuous strategic interaction between decision making agents. Based on the theory of stochastic games, we present a computational framework that allows for studying the dynamic feedback between decision--making agents themselves and an inherently uncertain environment in a spatially and temporally distributed manner. Agents with decision-making control over water allocation such as countries, irrigation districts, and municipalities are represented by reinforcement learning agents and coupled to a detailed hydrologic--economic model. This approach emphasizes learning by agents from their continuous interaction with other agents and the environment. It provides a convenient framework for the solution of the problem of dynamic decision-making in a mixed cooperative / non-cooperative environment with which different institutional setups and incentive systems can be studied so to identify reasonable ways to reach desirable, Pareto--optimal allocation outcomes. Preliminary results from an application to the Syr Darya river basin in Central Asia will be presented and discussed. The Syr Darya River is a classic example of a transboundary river basin in which basin-wide efficiency gains identified in optimization studies have not been sufficient to induce cooperative management of the river by the riparian states.

Adding ecosystem function to agent-based land use models

USDA-ARS?s Scientific Manuscript database

The objective of this paper is to examine issues in the inclusion of simulations of ecosystem functions in agent-based models of land use decision-making. The reasons for incorporating these simulations include local interests in land fertility and global interests in carbon sequestration. Biogeoche...

Distributed Simulation as a modelling tool for the development of a simulation-based training programme for cardiovascular specialties.

PubMed

Kelay, Tanika; Chan, Kah Leong; Ako, Emmanuel; Yasin, Mohammad; Costopoulos, Charis; Gold, Matthew; Kneebone, Roger K; Malik, Iqbal S; Bello, Fernando

2017-01-01

Distributed Simulation is the concept of portable, high-fidelity immersive simulation. Here, it is used for the development of a simulation-based training programme for cardiovascular specialities. We present an evidence base for how accessible, portable and self-contained simulated environments can be effectively utilised for the modelling, development and testing of a complex training framework and assessment methodology. Iterative user feedback through mixed-methods evaluation techniques resulted in the implementation of the training programme. Four phases were involved in the development of our immersive simulation-based training programme: ( 1) initial conceptual stage for mapping structural criteria and parameters of the simulation training framework and scenario development ( n  = 16), (2) training facility design using Distributed Simulation , (3) test cases with clinicians ( n  = 8) and collaborative design, where evaluation and user feedback involved a mixed-methods approach featuring (a) quantitative surveys to evaluate the realism and perceived educational relevance of the simulation format and framework for training and (b) qualitative semi-structured interviews to capture detailed feedback including changes and scope for development. Refinements were made iteratively to the simulation framework based on user feedback, resulting in (4) transition towards implementation of the simulation training framework, involving consistent quantitative evaluation techniques for clinicians ( n  = 62). For comparative purposes, clinicians' initial quantitative mean evaluation scores for realism of the simulation training framework, realism of the training facility and relevance for training ( n  = 8) are presented longitudinally, alongside feedback throughout the development stages from concept to delivery, including the implementation stage ( n  = 62). Initially, mean evaluation scores fluctuated from low to average, rising incrementally. This corresponded with the qualitative component, which augmented the quantitative findings; trainees' user feedback was used to perform iterative refinements to the simulation design and components (collaborative design), resulting in higher mean evaluation scores leading up to the implementation phase. Through application of innovative Distributed Simulation techniques, collaborative design, and consistent evaluation techniques from conceptual, development, and implementation stages, fully immersive simulation techniques for cardiovascular specialities are achievable and have the potential to be implemented more broadly.

School-Based Decision Making: A Principal-Agent Perspective.

ERIC Educational Resources Information Center

Ferris, James M.

1992-01-01

A principal-agent framework is used to examine potential gains in educational performance and potential threats to public accountability that school-based decision-making proposals pose. Analysis underscores the need to tailor the design of decentralized decision making to the sources of poor educational performance and threats to school…

Event-Triggered Distributed Average Consensus Over Directed Digital Networks With Limited Communication Bandwidth.

PubMed

Li, Huaqing; Chen, Guo; Huang, Tingwen; Dong, Zhaoyang; Zhu, Wei; Gao, Lan

2016-12-01

In this paper, we consider the event-triggered distributed average-consensus of discrete-time first-order multiagent systems with limited communication data rate and general directed network topology. In the framework of digital communication network, each agent has a real-valued state but can only exchange finite-bit binary symbolic data sequence with its neighborhood agents at each time step due to the digital communication channels with energy constraints. Novel event-triggered dynamic encoder and decoder for each agent are designed, based on which a distributed control algorithm is proposed. A scheme that selects the number of channel quantization level (number of bits) at each time step is developed, under which all the quantizers in the network are never saturated. The convergence rate of consensus is explicitly characterized, which is related to the scale of network, the maximum degree of nodes, the network structure, the scaling function, the quantization interval, the initial states of agents, the control gain and the event gain. It is also found that under the designed event-triggered protocol, by selecting suitable parameters, for any directed digital network containing a spanning tree, the distributed average consensus can be always achieved with an exponential convergence rate based on merely one bit information exchange between each pair of adjacent agents at each time step. Two simulation examples are provided to illustrate the feasibility of presented protocol and the correctness of the theoretical results.

Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach

PubMed Central

2016-01-01

Background Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach. PMID:26812235

Flocking algorithm for autonomous flying robots.

PubMed

Virágh, Csaba; Vásárhelyi, Gábor; Tarcai, Norbert; Szörényi, Tamás; Somorjai, Gergő; Nepusz, Tamás; Vicsek, Tamás

2014-06-01

Animal swarms displaying a variety of typical flocking patterns would not exist without the underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in their control algorithms. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour requires thorough and realistic modeling of the robot and also the environment. In this paper, we first present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of communication, inaccuracy of the on-board sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results on the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters. In our case, bio-inspiration works in two ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from the observation that birds tend to flock to optimize their behaviour as a group. On the other hand, by using a realistic simulation framework and studying the group behaviour of autonomous robots we can learn about the major factors influencing the flight of bird flocks.

Agent based models for testing city evacuation strategies under a flood event as strategy to reduce flood risk

NASA Astrophysics Data System (ADS)

Medina, Neiler; Sanchez, Arlex; Nokolic, Igor; Vojinovic, Zoran

2016-04-01

This research explores the uses of Agent Based Models (ABM) and its potential to test large scale evacuation strategies in coastal cities at risk from flood events due to extreme hydro-meteorological events with the final purpose of disaster risk reduction by decreasing human's exposure to the hazard. The first part of the paper corresponds to the theory used to build the models such as: Complex adaptive systems (CAS) and the principles and uses of ABM in this field. The first section outlines the pros and cons of using AMB to test city evacuation strategies at medium and large scale. The second part of the paper focuses on the central theory used to build the ABM, specifically the psychological and behavioral model as well as the framework used in this research, specifically the PECS reference model is cover in this section. The last part of this section covers the main attributes or characteristics of human beings used to described the agents. The third part of the paper shows the methodology used to build and implement the ABM model using Repast-Symphony as an open source agent-based modelling and simulation platform. The preliminary results for the first implementation in a region of the island of Sint-Maarten a Dutch Caribbean island are presented and discussed in the fourth section of paper. The results obtained so far, are promising for a further development of the model and its implementation and testing in a full scale city

Decoupling Coupled Constraints Through Utility Design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, N; Marden, JR

2014-08-01

Several multiagent systems exemplify the need for establishing distributed control laws that ensure the resulting agents' collective behavior satisfies a given coupled constraint. This technical note focuses on the design of such control laws through a game-theoretic framework. In particular, this technical note provides two systematic methodologies for the design of local agent objective functions that guarantee all resulting Nash equilibria optimize the system level objective while also satisfying a given coupled constraint. Furthermore, the designed local agent objective functions fit into the framework of state based potential games. Consequently, one can appeal to existing results in game-theoretic learning tomore » derive a distributed process that guarantees the agents will reach such an equilibrium.« less

A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation.

PubMed

Parikh, Nidhi; Hayatnagarkar, Harshal G; Beckman, Richard J; Marathe, Madhav V; Swarup, Samarth

2016-11-01

We describe a large-scale simulation of the aftermath of a hypothetical 10kT improvised nuclear detonation at ground level, near the White House in Washington DC. We take a synthetic information approach, where multiple data sets are combined to construct a synthesized representation of the population of the region with accurate demographics, as well as four infrastructures: transportation, healthcare, communication, and power. In this article, we focus on the model of agents and their behavior, which is represented using the options framework. Six different behavioral options are modeled: household reconstitution, evacuation, healthcare-seeking, worry, shelter-seeking, and aiding & assisting others. Agent decision-making takes into account their health status, information about family members, information about the event, and their local environment. We combine these behavioral options into five different behavior models of increasing complexity and do a number of simulations to compare the models.

A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation

PubMed Central

Parikh, Nidhi; Hayatnagarkar, Harshal G.; Beckman, Richard J.; Marathe, Madhav V.; Swarup, Samarth

2016-01-01

We describe a large-scale simulation of the aftermath of a hypothetical 10kT improvised nuclear detonation at ground level, near the White House in Washington DC. We take a synthetic information approach, where multiple data sets are combined to construct a synthesized representation of the population of the region with accurate demographics, as well as four infrastructures: transportation, healthcare, communication, and power. In this article, we focus on the model of agents and their behavior, which is represented using the options framework. Six different behavioral options are modeled: household reconstitution, evacuation, healthcare-seeking, worry, shelter-seeking, and aiding & assisting others. Agent decision-making takes into account their health status, information about family members, information about the event, and their local environment. We combine these behavioral options into five different behavior models of increasing complexity and do a number of simulations to compare the models. PMID:27909393

Agent-based modeling of malaria vectors: the importance of spatial simulation.

PubMed

Bomblies, Arne

2014-07-03

The modeling of malaria vector mosquito populations yields great insight into drivers of malaria transmission at the village scale. Simulation of individual mosquitoes as "agents" in a distributed, dynamic model domain may be greatly beneficial for simulation of spatial relationships of vectors and hosts. In this study, an agent-based model is used to simulate the life cycle and movement of individual malaria vector mosquitoes in a Niger Sahel village, with individual simulated mosquitoes interacting with their physical environment as well as humans. Various processes that are known to be epidemiologically important, such as the dependence of parity on flight distance between developmental habitat and blood meal hosts and therefore spatial relationships of pools and houses, are readily simulated using this modeling paradigm. Impacts of perturbations can be evaluated on the basis of vectorial capacity, because the interactions between individuals that make up the population- scale metric vectorial capacity can be easily tracked for simulated mosquitoes and human blood meal hosts, without the need to estimate vectorial capacity parameters. As expected, model results show pronounced impacts of pool source reduction from larvicide application and draining, but with varying degrees of impact depending on the spatial relationship between pools and human habitation. Results highlight the importance of spatially-explicit simulation that can model individuals such as in an agent-based model. The impacts of perturbations on village scale malaria transmission depend on spatial locations of individual mosquitoes, as well as the tracking of relevant life cycle events and characteristics of individual mosquitoes. This study demonstrates advantages of using an agent-based approach for village-scale mosquito simulation to address questions in which spatial relationships are known to be important.

Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models.

PubMed

Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J

2018-03-26

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.

Towards a genetics-based adaptive agent to support flight testing

NASA Astrophysics Data System (ADS)

Cribbs, Henry Brown, III

Although the benefits of aircraft simulation have been known since the late 1960s, simulation almost always entails interaction with a human test pilot. This "pilot-in-the-loop" simulation process provides useful evaluative information to the aircraft designer and provides a training tool to the pilot. Emulation of a pilot during the early phases of the aircraft design process might provide designers a useful evaluative tool. Machine learning might emulate a pilot in a simulated aircraft/cockpit setting. Preliminary work in the application of machine learning techniques, such as reinforcement learning, to aircraft maneuvering have shown promise. These studies used simplified interfaces between machine learning agent and the aircraft simulation. The simulations employed low order equivalent system models. High-fidelity aircraft simulations exist, such as the simulations developed by NASA at its Dryden Flight Research Center. To expand the applicational domain of reinforcement learning to aircraft designs, this study presents a series of experiments that examine a reinforcement learning agent in the role of test pilot. The NASA X-31 and F-106 high-fidelity simulations provide realistic aircraft for the agent to maneuver. The approach of the study is to examine an agent possessing a genetic-based, artificial neural network to approximate long-term, expected cost (Bellman value) in a basic maneuvering task. The experiments evaluate different learning methods based on a common feedback function and an identical task. The learning methods evaluated are: Q-learning, Q(lambda)-learning, SARSA learning, and SARSA(lambda) learning. Experimental results indicate that, while prediction error remain quite high, similar, repeatable behaviors occur in both aircraft. Similar behavior exhibits portability of the agent between aircraft with different handling qualities (dynamics). Besides the adaptive behavior aspects of the study, the genetic algorithm used in the agent is shown to play an additive role in the shaping of the artificial neural network to the prediction task.

Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

The recent availability of low cost and miniaturized hardware has allowed wireless sensor networks (WSNs) to retrieve audio and video data in real world applications, which has fostered the development of wireless multimedia sensor networks (WMSNs). Resource constraints and challenging multimedia data volume make development of efficient algorithms to perform in-network processing of multimedia contents imperative. This paper proposes solving problems in the domain of WMSNs from the perspective of multi-agent systems. The multi-agent framework enables flexible network configuration and efficient collaborative in-network processing. The focus is placed on target classification in WMSNs where audio information is retrieved by microphones. To deal with the uncertainties related to audio information retrieval, the statistical approaches of power spectral density estimates, principal component analysis and Gaussian process classification are employed. A multi-agent negotiation mechanism is specially developed to efficiently utilize limited resources and simultaneously enhance classification accuracy and reliability. The negotiation is composed of two phases, where an auction based approach is first exploited to allocate the classification task among the agents and then individual agent decisions are combined by the committee decision mechanism. Simulation experiments with real world data are conducted and the results show that the proposed statistical approaches and negotiation mechanism not only reduce memory and computation requirements in WMSNs but also significantly enhance classification accuracy and reliability. PMID:28903223

A Two-Stage Multi-Agent Based Assessment Approach to Enhance Students' Learning Motivation through Negotiated Skills Assessment

ERIC Educational Resources Information Center

Chadli, Abdelhafid; Bendella, Fatima; Tranvouez, Erwan

2015-01-01

In this paper we present an Agent-based evaluation approach in a context of Multi-agent simulation learning systems. Our evaluation model is based on a two stage assessment approach: (1) a Distributed skill evaluation combining agents and fuzzy sets theory; and (2) a Negotiation based evaluation of students' performance during a training…

Combining Monte Carlo methods with coherent wave optics for the simulation of phase-sensitive X-ray imaging

PubMed Central

Peter, Silvia; Modregger, Peter; Fix, Michael K.; Volken, Werner; Frei, Daniel; Manser, Peter; Stampanoni, Marco

2014-01-01

Phase-sensitive X-ray imaging shows a high sensitivity towards electron density variations, making it well suited for imaging of soft tissue matter. However, there are still open questions about the details of the image formation process. Here, a framework for numerical simulations of phase-sensitive X-ray imaging is presented, which takes both particle- and wave-like properties of X-rays into consideration. A split approach is presented where we combine a Monte Carlo method (MC) based sample part with a wave optics simulation based propagation part, leading to a framework that takes both particle- and wave-like properties into account. The framework can be adapted to different phase-sensitive imaging methods and has been validated through comparisons with experiments for grating interferometry and propagation-based imaging. The validation of the framework shows that the combination of wave optics and MC has been successfully implemented and yields good agreement between measurements and simulations. This demonstrates that the physical processes relevant for developing a deeper understanding of scattering in the context of phase-sensitive imaging are modelled in a sufficiently accurate manner. The framework can be used for the simulation of phase-sensitive X-ray imaging, for instance for the simulation of grating interferometry or propagation-based imaging. PMID:24763652

Facilitating the Specification Capture and Transformation Process in the Development of Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Filho, Aluzio Haendehen; Caminada, Numo; Haeusler, Edward Hermann; vonStaa, Arndt

2004-01-01

To support the development of flexible and reusable MAS, we have built a framework designated MAS-CF. MAS-CF is a component framework that implements a layered architecture based on contextual composition. Interaction rules, controlled by architecture mechanisms, ensure very low coupling, making possible the sharing of distributed services in a transparent, dynamic and independent way. These properties propitiate large-scale reuse, since organizational abstractions can be reused and propagated to all instances created from a framework. The objective is to reduce complexity and development time of multi-agent systems through the reuse of generic organizational abstractions.

CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hohimer, Ryan E.; Greitzer, Frank L.; Noonan, Christine F.

2011-11-15

We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.

Pain expressiveness and altruistic behavior: an exploration using agent-based modeling.

PubMed

de C Williams, Amanda C; Gallagher, Elizabeth; Fidalgo, Antonio R; Bentley, Peter J

2016-03-01

Predictions which invoke evolutionary mechanisms are hard to test. Agent-based modeling in artificial life offers a way to simulate behaviors and interactions in specific physical or social environments over many generations. The outcomes have implications for understanding adaptive value of behaviors in context. Pain-related behavior in animals is communicated to other animals that might protect or help, or might exploit or predate. An agent-based model simulated the effects of displaying or not displaying pain (expresser/nonexpresser strategies) when injured and of helping, ignoring, or exploiting another in pain (altruistic/nonaltruistic/selfish strategies). Agents modeled in MATLAB interacted at random while foraging (gaining energy); random injury interrupted foraging for a fixed time unless help from an altruistic agent, who paid an energy cost, speeded recovery. Environmental and social conditions also varied, and each model ran for 10,000 iterations. Findings were meaningful in that, in general, contingencies that evident from experimental work with a variety of mammals, over a few interactions, were replicated in the agent-based model after selection pressure over many generations. More energy-demanding expression of pain reduced its frequency in successive generations, and increasing injury frequency resulted in fewer expressers and altruists. Allowing exploitation of injured agents decreased expression of pain to near zero, but altruists remained. Decreasing costs or increasing benefits of helping hardly changed its frequency, whereas increasing interaction rate between injured agents and helpers diminished the benefits to both. Agent-based modeling allows simulation of complex behaviors and environmental pressures over evolutionary time.

Modeling Peer Assessment as Agent Negotiation in a Computer Supported Collaborative Learning Environment

ERIC Educational Resources Information Center

Lai, K. Robert; Lan, Chung Hsien

2006-01-01

This work presents a novel method for modeling collaborative learning as multi-issue agent negotiation using fuzzy constraints. Agent negotiation is an iterative process, through which, the proposed method aggregates student marks to reduce personal bias. In the framework, students define individual fuzzy membership functions based on their…

Proposal of Classification Method of Time Series Data in International Emissions Trading Market Using Agent-based Simulation

NASA Astrophysics Data System (ADS)

Nakada, Tomohiro; Takadama, Keiki; Watanabe, Shigeyoshi

This paper proposes the classification method using Bayesian analytical method to classify the time series data in the international emissions trading market depend on the agent-based simulation and compares the case with Discrete Fourier transform analytical method. The purpose demonstrates the analytical methods mapping time series data such as market price. These analytical methods have revealed the following results: (1) the classification methods indicate the distance of mapping from the time series data, it is easier the understanding and inference than time series data; (2) these methods can analyze the uncertain time series data using the distance via agent-based simulation including stationary process and non-stationary process; and (3) Bayesian analytical method can show the 1% difference description of the emission reduction targets of agent.

Multi-Level Cultural Models

DTIC Science & Technology

2014-11-05

usable simulations. This procedure was to be tested using real-world data collected from open-source venues. The final system would support rapid...assess social change. Construct is an agent-based dynamic-network simulation system design to allow the user to assess the spread of information and...protest or violence. Technical Challenges Addressed  Re‐use:    Most agent-based simulation ( ABM ) in use today are one-off. In contrast, we

A multi-GPU real-time dose simulation software framework for lung radiotherapy.

PubMed

Santhanam, A P; Min, Y; Neelakkantan, H; Papp, N; Meeks, S L; Kupelian, P A

2012-09-01

Medical simulation frameworks facilitate both the preoperative and postoperative analysis of the patient's pathophysical condition. Of particular importance is the simulation of radiation dose delivery for real-time radiotherapy monitoring and retrospective analyses of the patient's treatment. In this paper, a software framework tailored for the development of simulation-based real-time radiation dose monitoring medical applications is discussed. A multi-GPU-based computational framework coupled with inter-process communication methods is introduced for simulating the radiation dose delivery on a deformable 3D volumetric lung model and its real-time visualization. The model deformation and the corresponding dose calculation are allocated among the GPUs in a task-specific manner and is performed in a pipelined manner. Radiation dose calculations are computed on two different GPU hardware architectures. The integration of this computational framework with a front-end software layer and back-end patient database repository is also discussed. Real-time simulation of the dose delivered is achieved at once every 120 ms using the proposed framework. With a linear increase in the number of GPU cores, the computational time of the simulation was linearly decreased. The inter-process communication time also improved with an increase in the hardware memory. Variations in the delivered dose and computational speedup for variations in the data dimensions are investigated using D70 and D90 as well as gEUD as metrics for a set of 14 patients. Computational speed-up increased with an increase in the beam dimensions when compared with a CPU-based commercial software while the error in the dose calculation was <1%. Our analyses show that the framework applied to deformable lung model-based radiotherapy is an effective tool for performing both real-time and retrospective analyses.

Applications of agent-based modeling to nutrient movement Lake Michigan

EPA Science Inventory

As part of an ongoing project aiming to provide useful information for nearshore management (harmful algal blooms, nutrient loading), we explore the value of agent-based models in Lake Michigan. Agent-based models follow many individual “agents” moving through a simul...

Cognitive Modeling for Agent-Based Simulation of Child Maltreatment

NASA Astrophysics Data System (ADS)

Hu, Xiaolin; Puddy, Richard

This paper extends previous work to develop cognitive modeling for agent-based simulation of child maltreatment (CM). The developed model is inspired from parental efficacy, parenting stress, and the theory of planned behavior. It provides an explanatory, process-oriented model of CM and incorporates causality relationship and feedback loops from different factors in the social ecology in order for simulating the dynamics of CM. We describe the model and present simulation results to demonstrate the features of this model.

An Immune Agent for Web-Based AI Course

ERIC Educational Resources Information Center

Gong, Tao; Cai, Zixing

2006-01-01

To overcome weakness and faults of a web-based e-learning course such as Artificial Intelligence (AI), an immune agent was proposed, simulating a natural immune mechanism against a virus. The immune agent was built on the multi-dimension education agent model and immune algorithm. The web-based AI course was comprised of many files, such as HTML…

A framework for unravelling the complexities of unsustainable water resource use

NASA Astrophysics Data System (ADS)

Dermody, Brian; Bierkens, Marc; Wassen, Martin; Dekker, Stefan

2016-04-01

The majority of unsustainable water resource use is associated with food production, with the agricultural sector accounting for up to 70% of total freshwater use by humans. Water resource use in food production emerges as a result of dynamic interactions between humans and their environment in importing and exporting regions as well as the physical and socioeconomic trade infrastructure linking the two. Thus in order to understand unsustainable water resource use, it is essential to understand the complex socioecological food production and trade system. We present a modelling framework of the food production and trade system that facilitates an understanding of complex socioenvironmental processes that lead to unsustainable water resource use. Our framework is based on a coupling of the global hydrological model PC Raster Global Water Balance (PCR-GLOBWB) with a multi-agent socioeconomic food production and trade network. In our framework, agents perceive environmental conditions. They make food supply decisions based upon those perceptions and the heterogeneous socioeconomic conditions in which they exist. Agent decisions modify land and water resources. Those environmental changes feedback to influence decision making further. The framework presented has the potential to go beyond a diagnosis of the causes of unsustainable water resource and provide pathways towards a sustainable food system in terms of water resources.

Evaluating the effect of human activity patterns on air pollution exposure using an integrated field-based and agent-based modelling framework

NASA Astrophysics Data System (ADS)

Schmitz, Oliver; Beelen, Rob M. J.; de Bakker, Merijn P.; Karssenberg, Derek

2015-04-01

Constructing spatio-temporal numerical models to support risk assessment, such as assessing the exposure of humans to air pollution, often requires the integration of field-based and agent-based modelling approaches. Continuous environmental variables such as air pollution are best represented using the field-based approach which considers phenomena as continuous fields having attribute values at all locations. When calculating human exposure to such pollutants it is, however, preferable to consider the population as a set of individuals each with a particular activity pattern. This would allow to account for the spatio-temporal variation in a pollutant along the space-time paths travelled by individuals, determined, for example, by home and work locations, road network, and travel times. Modelling this activity pattern requires an agent-based or individual based modelling approach. In general, field- and agent-based models are constructed with the help of separate software tools, while both approaches should play together in an interacting way and preferably should be combined into one modelling framework, which would allow for efficient and effective implementation of models by domain specialists. To overcome this lack in integrated modelling frameworks, we aim at the development of concepts and software for an integrated field-based and agent-based modelling framework. Concepts merging field- and agent-based modelling were implemented by extending PCRaster (http://www.pcraster.eu), a field-based modelling library implemented in C++, with components for 1) representation of discrete, mobile, agents, 2) spatial networks and algorithms by integrating the NetworkX library (http://networkx.github.io), allowing therefore to calculate e.g. shortest routes or total transport costs between locations, and 3) functions for field-network interactions, allowing to assign field-based attribute values to networks (i.e. as edge weights), such as aggregated or averaged concentration values. We demonstrate the approach by using six land use regression (LUR) models developed in the ESCAPE (European Study of Cohorts for Air Pollution Effects) project. These models calculate several air pollutants (e.g. NO2, NOx, PM2.5) for the entire Netherlands at a high (5 m) resolution. Using these air pollution maps, we compare exposure of individuals calculated at their x, y location of their home, their work place, and aggregated over the close surroundings of these locations. In addition, total exposure is accumulated over daily activity patterns, summing exposure at home, at the work place, and while travelling between home and workplace, by routing individuals over the Dutch road network, using the shortest route. Finally, we illustrate how routes can be calculated with the minimum total exposure (instead of shortest distance).

Projective simulation for artificial intelligence

NASA Astrophysics Data System (ADS)

Briegel, Hans J.; de Las Cuevas, Gemma

2012-05-01

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.

Projective simulation for artificial intelligence

PubMed Central

Briegel, Hans J.; De las Cuevas, Gemma

2012-01-01

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation. PMID:22590690

Agent-Based Scientific Workflow Composition

NASA Astrophysics Data System (ADS)

Barker, A.; Mann, B.

2006-07-01

Agents are active autonomous entities that interact with one another to achieve their objectives. This paper addresses how these active agents are a natural fit to consume the passive Service Oriented Architecture which is found in Internet and Grid Systems, in order to compose, coordinate and execute e-Science experiments. A framework is introduced which allows an e-Science experiment to be described as a MultiAgent System.

Formalization, implementation, and modeling of institutional controllers for distributed robotic systems.

PubMed

Pereira, José N; Silva, Porfírio; Lima, Pedro U; Martinoli, Alcherio

2014-01-01

The work described is part of a long term program of introducing institutional robotics, a novel framework for the coordination of robot teams that stems from institutional economics concepts. Under the framework, institutions are cumulative sets of persistent artificial modifications made to the environment or to the internal mechanisms of a subset of agents, thought to be functional for the collective order. In this article we introduce a formal model of institutional controllers based on Petri nets. We define executable Petri nets-an extension of Petri nets that takes into account robot actions and sensing-to design, program, and execute institutional controllers. We use a generalized stochastic Petri net view of the robot team controlled by the institutional controllers to model and analyze the stochastic performance of the resulting distributed robotic system. The ability of our formalism to replicate results obtained using other approaches is assessed through realistic simulations of up to 40 e-puck robots. In particular, we model a robot swarm and its institutional controller with the goal of maintaining wireless connectivity, and successfully compare our model predictions and simulation results with previously reported results, obtained by using finite state automaton models and controllers.

An Agent-Based Model for Studying Child Maltreatment and Child Maltreatment Prevention

NASA Astrophysics Data System (ADS)

Hu, Xiaolin; Puddy, Richard W.

This paper presents an agent-based model that simulates the dynamics of child maltreatment and child maltreatment prevention. The developed model follows the principles of complex systems science and explicitly models a community and its families with multi-level factors and interconnections across the social ecology. This makes it possible to experiment how different factors and prevention strategies can affect the rate of child maltreatment. We present the background of this work and give an overview of the agent-based model and show some simulation results.

Learn, see, practice, prove, do, maintain: an evidence-based pedagogical framework for procedural skill training in medicine.

PubMed

Sawyer, Taylor; White, Marjorie; Zaveri, Pavan; Chang, Todd; Ades, Anne; French, Heather; Anderson, JoDee; Auerbach, Marc; Johnston, Lindsay; Kessler, David

2015-08-01

Acquisition of competency in procedural skills is a fundamental goal of medical training. In this Perspective, the authors propose an evidence-based pedagogical framework for procedural skill training. The framework was developed based on a review of the literature using a critical synthesis approach and builds on earlier models of procedural skill training in medicine. The authors begin by describing the fundamentals of procedural skill development. Then, a six-step pedagogical framework for procedural skills training is presented: Learn, See, Practice, Prove, Do, and Maintain. In this framework, procedural skill training begins with the learner acquiring requisite cognitive knowledge through didactic education (Learn) and observation of the procedure (See). The learner then progresses to the stage of psychomotor skill acquisition and is allowed to deliberately practice the procedure on a simulator (Practice). Simulation-based mastery learning is employed to allow the trainee to prove competency prior to performing the procedure on a patient (Prove). Once competency is demonstrated on a simulator, the trainee is allowed to perform the procedure on patients with direct supervision, until he or she can be entrusted to perform the procedure independently (Do). Maintenance of the skill is ensured through continued clinical practice, supplemented by simulation-based training as needed (Maintain). Evidence in support of each component of the framework is presented. Implementation of the proposed framework presents a paradigm shift in procedural skill training. However, the authors believe that adoption of the framework will improve procedural skill training and patient safety.

DAMS: A Model to Assess Domino Effects by Using Agent-Based Modeling and Simulation.

PubMed

Zhang, Laobing; Landucci, Gabriele; Reniers, Genserik; Khakzad, Nima; Zhou, Jianfeng

2017-12-19

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases. © 2017 Society for Risk Analysis.

Memoryless cooperative graph search based on the simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Hou, Jian; Yan, Gang-Feng; Fan, Zhen

2011-04-01

We have studied the problem of reaching a globally optimal segment for a graph-like environment with a single or a group of autonomous mobile agents. Firstly, two efficient simulated-annealing-like algorithms are given for a single agent to solve the problem in a partially known environment and an unknown environment, respectively. It shows that under both proposed control strategies, the agent will eventually converge to a globally optimal segment with probability 1. Secondly, we use multi-agent searching to simultaneously reduce the computation complexity and accelerate convergence based on the algorithms we have given for a single agent. By exploiting graph partition, a gossip-consensus method based scheme is presented to update the key parameter—radius of the graph, ensuring that the agents spend much less time finding a globally optimal segment.

Driving-forces model on individual behavior in scenarios considering moving threat agents

NASA Astrophysics Data System (ADS)

Li, Shuying; Zhuang, Jun; Shen, Shifei; Wang, Jia

2017-09-01

The individual behavior model is a contributory factor to improve the accuracy of agent-based simulation in different scenarios. However, few studies have considered moving threat agents, which often occur in terrorist attacks caused by attackers with close-range weapons (e.g., sword, stick). At the same time, many existing behavior models lack validation from cases or experiments. This paper builds a new individual behavior model based on seven behavioral hypotheses. The driving-forces model is an extension of the classical social force model considering scenarios including moving threat agents. An experiment was conducted to validate the key components of the model. Then the model is compared with an advanced Elliptical Specification II social force model, by calculating the fitting errors between the simulated and experimental trajectories, and being applied to simulate a specific circumstance. Our results show that the driving-forces model reduced the fitting error by an average of 33.9% and the standard deviation by an average of 44.5%, which indicates the accuracy and stability of the model in the studied situation. The new driving-forces model could be used to simulate individual behavior when analyzing the risk of specific scenarios using agent-based simulation methods, such as risk analysis of close-range terrorist attacks in public places.

Decision Manifold Approximation for Physics-Based Simulations

NASA Technical Reports Server (NTRS)

Wong, Jay Ming; Samareh, Jamshid A.

2016-01-01

With the recent surge of success in big-data driven deep learning problems, many of these frameworks focus on the notion of architecture design and utilizing massive databases. However, in some scenarios massive sets of data may be difficult, and in some cases infeasible, to acquire. In this paper we discuss a trajectory-based framework that quickly learns the underlying decision manifold of binary simulation classifications while judiciously selecting exploratory target states to minimize the number of required simulations. Furthermore, we draw particular attention to the simulation prediction application idealized to the case where failures in simulations can be predicted and avoided, providing machine intelligence to novice analysts. We demonstrate this framework in various forms of simulations and discuss its efficacy.

Physically Based Modeling and Simulation with Dynamic Spherical Volumetric Simplex Splines

PubMed Central

Tan, Yunhao; Hua, Jing; Qin, Hong

2009-01-01

In this paper, we present a novel computational modeling and simulation framework based on dynamic spherical volumetric simplex splines. The framework can handle the modeling and simulation of genus-zero objects with real physical properties. In this framework, we first develop an accurate and efficient algorithm to reconstruct the high-fidelity digital model of a real-world object with spherical volumetric simplex splines which can represent with accuracy geometric, material, and other properties of the object simultaneously. With the tight coupling of Lagrangian mechanics, the dynamic volumetric simplex splines representing the object can accurately simulate its physical behavior because it can unify the geometric and material properties in the simulation. The visualization can be directly computed from the object’s geometric or physical representation based on the dynamic spherical volumetric simplex splines during simulation without interpolation or resampling. We have applied the framework for biomechanic simulation of brain deformations, such as brain shifting during the surgery and brain injury under blunt impact. We have compared our simulation results with the ground truth obtained through intra-operative magnetic resonance imaging and the real biomechanic experiments. The evaluations demonstrate the excellent performance of our new technique. PMID:20161636

Web-based health care agents; the case of reminders and todos, too (R2Do2).

PubMed

Silverman, B G; Andonyadis, C; Morales, A

1998-11-01

This paper describes efforts to develop and field an agent-based, healthcare middleware framework that securely connects practice rule sets to patient records to anticipate health todo items and to remind and alert users about these items over the web. Reminders and todos, too (R2Do2) is an example of merging data- and document-centric architectures, and of integrating agents into patient-provider collaboration environments. A test of this capability verifies that R2Do2 is progressing toward its two goals: (1) an open standards framework for middleware in the healthcare field; and (2) an implementation of the 'principle of optimality' to derive the best possible health plans for each user. This paper concludes with lessons learned to date.

Mechanism for Collective Cell Alignment in Myxococcus xanthus Bacteria

PubMed Central

Balagam, Rajesh; Igoshin, Oleg A.

2015-01-01

Myxococcus xanthus cells self-organize into aligned groups, clusters, at various stages of their lifecycle. Formation of these clusters is crucial for the complex dynamic multi-cellular behavior of these bacteria. However, the mechanism underlying the cell alignment and clustering is not fully understood. Motivated by studies of clustering in self-propelled rods, we hypothesized that M. xanthus cells can align and form clusters through pure mechanical interactions among cells and between cells and substrate. We test this hypothesis using an agent-based simulation framework in which each agent is based on the biophysical model of an individual M. xanthus cell. We show that model agents, under realistic cell flexibility values, can align and form cell clusters but only when periodic reversals of cell directions are suppressed. However, by extending our model to introduce the observed ability of cells to deposit and follow slime trails, we show that effective trail-following leads to clusters in reversing cells. Furthermore, we conclude that mechanical cell alignment combined with slime-trail-following is sufficient to explain the distinct clustering behaviors observed for wild-type and non-reversing M. xanthus mutants in recent experiments. Our results are robust to variation in model parameters, match the experimentally observed trends and can be applied to understand surface motility patterns of other bacterial species. PMID:26308508

Information of Complex Systems and Applications in Agent Based Modeling.

PubMed

Bao, Lei; Fritchman, Joseph C

2018-04-18

Information about a system's internal interactions is important to modeling the system's dynamics. This study examines the finer categories of the information definition and explores the features of a type of local information that describes the internal interactions of a system. Based on the results, a dual-space agent and information modeling framework (AIM) is developed by explicitly distinguishing an information space from the material space. The two spaces can evolve both independently and interactively. The dual-space framework can provide new analytic methods for agent based models (ABMs). Three examples are presented including money distribution, individual's economic evolution, and artificial stock market. The results are analyzed in the dual-space, which more clearly shows the interactions and evolutions within and between the information and material spaces. The outcomes demonstrate the wide-ranging applicability of using the dual-space AIMs to model and analyze a broad range of interactive and intelligent systems.

Integrated Agent-Based and Production Cost Modeling Framework for Renewable Energy Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gallo, Giulia

The agent-based framework for renewable energy studies (ARES) is an integrated approach that adds an agent-based model of industry actors to PLEXOS and combines the strengths of the two to overcome their individual shortcomings. It can examine existing and novel wholesale electricity markets under high penetrations of renewables. ARES is demonstrated by studying how increasing levels of wind will impact the operations and the exercise of market power of generation companies that exploit an economic withholding strategy. The analysis is carried out on a test system that represents the Electric Reliability Council of Texas energy-only market in the year 2020.more » The results more realistically reproduce the operations of an energy market under different and increasing penetrations of wind, and ARES can be extended to address pressing issues in current and future wholesale electricity markets.« less

Integrated Agent-Based and Production Cost Modeling Framework for Renewable Energy Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gallo, Giulia

The agent-based framework for renewable energy studies (ARES) is an integrated approach that adds an agent-based model of industry actors to PLEXOS and combines the strengths of the two to overcome their individual shortcomings. It can examine existing and novel wholesale electricity markets under high penetrations of renewables. ARES is demonstrated by studying how increasing levels of wind will impact the operations and the exercise of market power of generation companies that exploit an economic withholding strategy. The analysis is carried out on a test system that represents the Electric Reliability Council of Texas energy-only market in the year 2020.more » The results more realistically reproduce the operations of an energy market under different and increasing penetrations of wind, and ARES can be extended to address pressing issues in current and future wholesale electricity markets.« less

Method for distributed agent-based non-expert simulation of manufacturing process behavior

DOEpatents

Ivezic, Nenad; Potok, Thomas E.

2004-11-30

A method for distributed agent based non-expert simulation of manufacturing process behavior on a single-processor computer comprises the steps of: object modeling a manufacturing technique having a plurality of processes; associating a distributed agent with each the process; and, programming each the agent to respond to discrete events corresponding to the manufacturing technique, wherein each discrete event triggers a programmed response. The method can further comprise the step of transmitting the discrete events to each agent in a message loop. In addition, the programming step comprises the step of conditioning each agent to respond to a discrete event selected from the group consisting of a clock tick message, a resources received message, and a request for output production message.

A Simulation and Modeling Framework for Space Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olivier, S S

This paper describes the development and initial demonstration of a new, integrated modeling and simulation framework, encompassing the space situational awareness enterprise, for quantitatively assessing the benefit of specific sensor systems, technologies and data analysis techniques. The framework is based on a flexible, scalable architecture to enable efficient, physics-based simulation of the current SSA enterprise, and to accommodate future advancements in SSA systems. In particular, the code is designed to take advantage of massively parallel computer systems available, for example, at Lawrence Livermore National Laboratory. The details of the modeling and simulation framework are described, including hydrodynamic models of satellitemore » intercept and debris generation, orbital propagation algorithms, radar cross section calculations, optical brightness calculations, generic radar system models, generic optical system models, specific Space Surveillance Network models, object detection algorithms, orbit determination algorithms, and visualization tools. The use of this integrated simulation and modeling framework on a specific scenario involving space debris is demonstrated.« less

Simulating the decentralized processes of the human immune system in a virtual anatomy model.

PubMed

Sarpe, Vladimir; Jacob, Christian

2013-01-01

Many physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spatial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation. Using agent-based methodology and a 3-dimensional modeling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model - such as immune cells, viruses and cytokines - interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. Ultimately, this system integration across scales is our goal for the LINDSAY Virtual Human project. Our current immune system simulations extend our previous work on agent-based simulations by introducing advanced visualizations within the context of a virtual human anatomy model. We also demonstrate how to distribute a collection of connected simulations over a network of computers. As a future endeavour, we plan to use parameter tuning techniques on our model to further enhance its biological credibility. We consider these in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics.

CyberMedVPS: visual programming for development of simulators.

PubMed

Morais, Aline M; Machado, Liliane S

2011-01-01

Computer applications based on Virtual Reality (VR) has been outstanding in training and teaching in the medical filed due to their ability to simulate realistic in which users can practice skills and decision making in different situations. But was realized in these frameworks a hard interaction of non-programmers users. Based on this problematic will be shown the CyberMedVPS, a graphical module which implement Visual Programming concepts to solve an interaction trouble. Frameworks to develop such simulators are available but their use demands knowledge of programming. Based on this problematic will be shown the CyberMedVPS, a graphical module for the CyberMed framework, which implements Visual Programming concepts to allow the development of simulators by non-programmers professionals of the medical field.

A Conceptual Framework for Representing Human Behavior Characteristics in a System of Systems Agent-Based Survivability Simulation

DTIC Science & Technology

2010-11-22

fuzzy matrix converges to a “zero-one” matrix. The values of “0” and “1” simply means that two edges of the network with “1” have a crisp ...fuzzy matrix converges to a “zero-one” matrix. The values of “0” and “1” simply means that two edges of the network with “1” have a crisp connectivity...converges to a “zero-one” matrix. The values of “0” and “1” simply means that two edges of the network with “1” have a crisp connectivity (and

Modeling the Impact of Motivation, Personality, and Emotion on Social Behavior

NASA Astrophysics Data System (ADS)

Miller, Lynn C.; Read, Stephen J.; Zachary, Wayne; Rosoff, Andrew

Models seeking to predict human social behavior must contend with multiple sources of individual and group variability that underlie social behavior. One set of interrelated factors that strongly contribute to that variability - motivations, personality, and emotions - has been only minimally incorporated in previous computational models of social behavior. The Personality, Affect, Culture (PAC) framework is a theory-based computational model that addresses this gap. PAC is used to simulate social agents whose social behavior varies according to their personalities and emotions, which, in turn, vary according to their motivations and underlying motive control parameters. Examples involving disease spread and counter-insurgency operations show how PAC can be used to study behavioral variability in different social contexts.

Simulated Environments with Animated Agents: Effects on Visual Attention, Emotion, Performance, and Perception

ERIC Educational Resources Information Center

Romero-Hall, E.; Watson, G. S.; Adcock, A.; Bliss, J.; Adams Tufts, K.

2016-01-01

This research assessed how emotive animated agents in a simulation-based training affect the performance outcomes and perceptions of the individuals interacting in real time with the training application. A total of 56 participants consented to complete the study. The material for this investigation included a nursing simulation in which…

TANDEM: A Trust-Based Agent Framework for Networked Decision Making

DTIC Science & Technology

2015-09-10

selective (20–80 %), while the rest are good citizens, trust acts as a method to isolate misbehaving agents. If the majority of the agents have high...competence and low selectivity, then they can use trust to isolate route information around the misbehaving agents, improving Comm and Steps. The impact is...more dramatic when only 20–40 % of the agents are misbehaving . However, using trust results in reduced SA as the information available at the

Evacuation Simulation in Kalayaan Residence Hall, up Diliman Using Gama Simulation Software

NASA Astrophysics Data System (ADS)

Claridades, A. R. C.; Villanueva, J. K. S.; Macatulad, E. G.

2016-09-01

Agent-Based Modeling (ABM) has recently been adopted in some studies for the modelling of events as a dynamic system given a set of events and parameters. In principle, ABM employs individual agents with assigned attributes and behaviors and simulates their behavior around their environment and interaction with other agents. This can be a useful tool in both micro and macroscale-applications. In this study, a model initially created and applied to an academic building was implemented in a dormitory. In particular, this research integrates three-dimensional Geographic Information System (GIS) with GAMA as the multi-agent based evacuation simulation and is implemented in Kalayaan Residence Hall. A three-dimensional GIS model is created based on the floor plans and demographic data of the dorm, including respective pathways as networks, rooms, floors, exits and appropriate attributes. This model is then re-implemented in GAMA. Different states of the agents and their effect on their evacuation time were then observed. GAMA simulation with varying path width was also implemented. It has been found out that compared to their original states, panic, eating and studying will hasten evacuation, and on the other hand, sleeping and being on the bathrooms will be impedances. It is also concluded that evacuation time will be halved when path widths are doubled, however it is recommended for further studies for pathways to be modeled as spaces instead of lines. A more scientific basis for predicting agent behavior in these states is also recommended for more realistic results.

Integrating an agent-based model into a large-scale hydrological model for evaluating drought management in California

NASA Astrophysics Data System (ADS)

Sheffield, J.; He, X.; Wada, Y.; Burek, P.; Kahil, M.; Wood, E. F.; Oppenheimer, M.

2017-12-01

California has endured record-breaking drought since winter 2011 and will likely experience more severe and persistent drought in the coming decades under changing climate. At the same time, human water management practices can also affect drought frequency and intensity, which underscores the importance of human behaviour in effective drought adaptation and mitigation. Currently, although a few large-scale hydrological and water resources models (e.g., PCR-GLOBWB) consider human water use and management practices (e.g., irrigation, reservoir operation, groundwater pumping), none of them includes the dynamic feedback between local human behaviors/decisions and the natural hydrological system. It is, therefore, vital to integrate social and behavioral dimensions into current hydrological modeling frameworks. This study applies the agent-based modeling (ABM) approach and couples it with a large-scale hydrological model (i.e., Community Water Model, CWatM) in order to have a balanced representation of social, environmental and economic factors and a more realistic representation of the bi-directional interactions and feedbacks in coupled human and natural systems. In this study, we focus on drought management in California and considers two types of agents, which are (groups of) farmers and state management authorities, and assumed that their corresponding objectives are to maximize the net crop profit and to maintain sufficient water supply, respectively. Farmers' behaviors are linked with local agricultural practices such as cropping patterns and deficit irrigation. More precisely, farmers' decisions are incorporated into CWatM across different time scales in terms of daily irrigation amount, seasonal/annual decisions on crop types and irrigated area as well as the long-term investment of irrigation infrastructure. This simulation-based optimization framework is further applied by performing different sets of scenarios to investigate and evaluate the effectiveness of different water management strategies and how policy interventions will facilitate drought adaptation in California.

Conformity-driven agents support ordered phases in the spatial public goods game

NASA Astrophysics Data System (ADS)

Javarone, Marco Alberto; Antonioni, Alberto; Caravelli, Francesco

2016-05-01

We investigate the spatial Public Goods Game in the presence of fitness-driven and conformity-driven agents. This framework usually considers only the former type of agents, i.e., agents that tend to imitate the strategy of their fittest neighbors. However, whenever we study social systems, the evolution of a population might be affected also by social behaviors as conformism, stubbornness, altruism, and selfishness. Although the term evolution can assume different meanings depending on the considered domain, here it corresponds to the set of processes that lead a system towards an equilibrium or a steady state. We map fitness to the agents' payoff so that richer agents are those most imitated by fitness-driven agents, while conformity-driven agents tend to imitate the strategy assumed by the majority of their neighbors. Numerical simulations aim to identify the nature of the transition, on varying the amount of the relative density of conformity-driven agents in the population, and to study the nature of related equilibria. Remarkably, we find that conformism generally fosters ordered cooperative phases and may also lead to bistable behaviors.

Trust Management Considerations For the Cooperative Infrastructure Defense Framework: Trust Relationships, Evidence, and Decisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maiden, Wendy M.

Cooperative Infrastructure Defense (CID) is a hierarchical, agent-based, adaptive, cyber-security framework designed to collaboratively protect multiple enclaves or organizations participating in a complex infrastructure. CID employs a swarm of lightweight, mobile agents called Sensors designed to roam hosts throughout a security enclave to find indications of anomalies and report them to host-based Sentinels. The Sensors’ findings become pieces of a larger puzzle, which the Sentinel puts together to determine the problem and respond per policy as given by the enclave-level Sergeant agent. Horizontally across multiple enclaves and vertically within each enclave, authentication and access control technologies are necessary but insufficientmore » authorization mechanisms to ensure that CID agents continue to fulfill their roles in a trustworthy manner. Trust management fills the gap, providing mechanisms to detect malicious agents and offering more robust mechanisms for authorization. This paper identifies the trust relationships throughout the CID hierarchy, the types of trust evidence that could be gathered, and the actions that the CID system could take if an entity is determined to be untrustworthy.« less

Simulating Cancer Growth with Multiscale Agent-Based Modeling

PubMed Central

Wang, Zhihui; Butner, Joseph D.; Kerketta, Romica; Cristini, Vittorio; Deisboeck, Thomas S.

2014-01-01

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models. PMID:24793698

A Computational Framework for Efficient Low Temperature Plasma Simulations

NASA Astrophysics Data System (ADS)

Verma, Abhishek Kumar; Venkattraman, Ayyaswamy

2016-10-01

Over the past years, scientific computing has emerged as an essential tool for the investigation and prediction of low temperature plasmas (LTP) applications which includes electronics, nanomaterial synthesis, metamaterials etc. To further explore the LTP behavior with greater fidelity, we present a computational toolbox developed to perform LTP simulations. This framework will allow us to enhance our understanding of multiscale plasma phenomenon using high performance computing tools mainly based on OpenFOAM FVM distribution. Although aimed at microplasma simulations, the modular framework is able to perform multiscale, multiphysics simulations of physical systems comprises of LTP. Some salient introductory features are capability to perform parallel, 3D simulations of LTP applications on unstructured meshes. Performance of the solver is tested based on numerical results assessing accuracy and efficiency of benchmarks for problems in microdischarge devices. Numerical simulation of microplasma reactor at atmospheric pressure with hemispherical dielectric coated electrodes will be discussed and hence, provide an overview of applicability and future scope of this framework.

Modelling Tradeoffs Evolution in Multipurpose Water Systems Operation in Response to Extreme Events

NASA Astrophysics Data System (ADS)

Mason, E.; Gazzotti, P.; Amigoni, F.; Giuliani, M.; Castelletti, A.

2015-12-01

Multipurpose water resource systems are usually operated on a tradeoff of the operating objectives, which - under steady state climatic and socio-economic boundary conditions - is supposed to ensure a fair and/or efficient balance among the conflicting interests. Extreme variability in the system's drivers might affect operators' risk aversion and force a change in the tradeoff. Properly accounting for these shifts is key to any rigorous retrospective assessment of operators' behavior and the associated system's performance. In this study, we explore how the selection of different optimal tradeoffs among the operating objectives is linked to the variations of the boundary conditions, such as, for example, drifting rainfall season or remarkable changes in crop and energy prices. We argue that tradeoff selection is driven by recent, extreme variations in system performance: underperforming on one of the operating objective target value should push the tradeoff toward the disadvantaged objective. To test this assumption, we developed a rational procedure to simulate the operators' tradeoff selection process. We map the selection onto a multi lateral negotiation process, where different multiple, virtual agents optimize different operating objectives. The agents periodically negotiate a compromise on the operating policy. The agent's rigidity in each negotiation round is determined by the recent system performances according to the specific objective it represents. The negotiation follows a set-based egocentric monotonic concession protocol: at each negotiation step an agent incrementally adds some options to the set of its acceptable compromises and (possibly) accepts lower and lower satisfying policies until an agreement is achieved. We apply this reiterated negotiation framework on the regulated Lake Como, Italy, simulating the lake dam operation and its recurrent updates over the last 50 years. The operation aims to balance shoreline flood prevention and irrigation deficit control in the downstream irrigated areas. The results of our simulated negotiations are able to accurately capture the operator's risk aversion changes as driven by extreme wet and dry situations, and to well reproduce the observational release data.

Development of a Dynamically Configurable, Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation

NASA Technical Reports Server (NTRS)

Afjeh, Abdollah A.; Reed, John A.

2003-01-01

The following reports are presented on this project:A first year progress report on: Development of a Dynamically Configurable,Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation; A second year progress report on: Development of a Dynamically Configurable, Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation; An Extensible, Interchangeable and Sharable Database Model for Improving Multidisciplinary Aircraft Design; Interactive, Secure Web-enabled Aircraft Engine Simulation Using XML Databinding Integration; and Improving the Aircraft Design Process Using Web-based Modeling and Simulation.

Comparison of an Agent-based Model of Disease Propagation with the Generalised SIR Epidemic Model

DTIC Science & Technology

2009-08-01

has become a practical method for conducting Epidemiological Modelling. In the agent- based approach the whole township can be modelled as a system of...SIR system was initially developed based on a very simplified model of social interaction. For instance an assumption of uniform population mixing was...simulating the progress of a disease within a host and of transmission between hosts is based upon Transportation Analysis and Simulation System

Integration agent-based models and GIS as a virtual urban dynamic laboratory

NASA Astrophysics Data System (ADS)

Chen, Peng; Liu, Miaolong

2007-06-01

Based on the Agent-based Model and spatial data model, a tight-coupling integrating method of GIS and Agent-based Model (ABM) is to be discussed in this paper. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena such as urban dynamic. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, the agent-based model and spatial data model are discussed, and then the relationships affecting spatial data model and agent-based process models interaction. After that, a realistic crowd flow simulation experiment is presented. Using some tools provided by general GIS systems and a few specific programming languages, a new software system integrating GIS and MAS as a virtual laboratory applicable for simulating pedestrian flows in a crowd activity centre has been developed successfully. Under the environment supported by the software system, as an applicable case, a dynamic evolution process of the pedestrian's flows (dispersed process for the spectators) in a crowds' activity center - The Shanghai Stadium has been simulated successfully. At the end of the paper, some new research problems have been pointed out for the future.

Constructing Agent Model for Virtual Training Systems

NASA Astrophysics Data System (ADS)

Murakami, Yohei; Sugimoto, Yuki; Ishida, Toru

Constructing highly realistic agents is essential if agents are to be employed in virtual training systems. In training for collaboration based on face-to-face interaction, the generation of emotional expressions is one key. In training for guidance based on one-to-many interaction such as direction giving for evacuations, emotional expressions must be supplemented by diverse agent behaviors to make the training realistic. To reproduce diverse behavior, we characterize agents by using a various combinations of operation rules instantiated by the user operating the agent. To accomplish this goal, we introduce a user modeling method based on participatory simulations. These simulations enable us to acquire information observed by each user in the simulation and the operating history. Using these data and the domain knowledge including known operation rules, we can generate an explanation for each behavior. Moreover, the application of hypothetical reasoning, which offers consistent selection of hypotheses, to the generation of explanations allows us to use otherwise incompatible operation rules as domain knowledge. In order to validate the proposed modeling method, we apply it to the acquisition of an evacuee's model in a fire-drill experiment. We successfully acquire a subject's model corresponding to the results of an interview with the subject.

Evolving effective behaviours to interact with tag-based populations

NASA Astrophysics Data System (ADS)

Yucel, Osman; Crawford, Chad; Sen, Sandip

2015-07-01

Tags and other characteristics, externally perceptible features that are consistent among groups of animals or humans, can be used by others to determine appropriate response strategies in societies. This usage of tags can be extended to artificial environments, where agents can significantly reduce cognitive effort spent on appropriate strategy choice and behaviour selection by reusing strategies for interacting with new partners based on their tags. Strategy selection mechanisms developed based on this idea have successfully evolved stable cooperation in games such as the Prisoner's Dilemma game but relies upon payoff sharing and matching methods that limit the applicability of the tag framework. Our goal is to develop a general classification and behaviour selection approach based on the tag framework. We propose and evaluate alternative tag matching and adaptation schemes for a new, incoming individual to select appropriate behaviour against any population member of an existing, stable society. Our proposed approach allows agents to evolve both the optimal tag for the environment as well as appropriate strategies for existing agent groups. We show that these mechanisms will allow for robust selection of optimal strategies by agents entering a stable society and analyse the various environments where this approach is effective.

An Agent-Based Epidemic Simulation of Social Behaviors Affecting HIV Transmission among Taiwanese Homosexuals

PubMed Central

2015-01-01

Computational simulations are currently used to identify epidemic dynamics, to test potential prevention and intervention strategies, and to study the effects of social behaviors on HIV transmission. The author describes an agent-based epidemic simulation model of a network of individuals who participate in high-risk sexual practices, using number of partners, condom usage, and relationship length to distinguish between high- and low-risk populations. Two new concepts—free links and fixed links—are used to indicate tendencies among individuals who either have large numbers of short-term partners or stay in long-term monogamous relationships. An attempt was made to reproduce epidemic curves of reported HIV cases among male homosexuals in Taiwan prior to using the agent-based model to determine the effects of various policies on epidemic dynamics. Results suggest that when suitable adjustments are made based on available social survey statistics, the model accurately simulates real-world behaviors on a large scale. PMID:25815047

Optimization of space system development resources

NASA Astrophysics Data System (ADS)

Kosmann, William J.; Sarkani, Shahram; Mazzuchi, Thomas

2013-06-01

NASA has had a decades-long problem with cost growth during the development of space science missions. Numerous agency-sponsored studies have produced average mission level cost growths ranging from 23% to 77%. A new study of 26 historical NASA Science instrument set developments using expert judgment to reallocate key development resources has an average cost growth of 73.77%. Twice in history, a barter-based mechanism has been used to reallocate key development resources during instrument development. The mean instrument set development cost growth was -1.55%. Performing a bivariate inference on the means of these two distributions, there is statistical evidence to support the claim that using a barter-based mechanism to reallocate key instrument development resources will result in a lower expected cost growth than using the expert judgment approach. Agent-based discrete event simulation is the natural way to model a trade environment. A NetLogo agent-based barter-based simulation of science instrument development was created. The agent-based model was validated against the Cassini historical example, as the starting and ending instrument development conditions are available. The resulting validated agent-based barter-based science instrument resource reallocation simulation was used to perform 300 instrument development simulations, using barter to reallocate development resources. The mean cost growth was -3.365%. A bivariate inference on the means was performed to determine that additional significant statistical evidence exists to support a claim that using barter-based resource reallocation will result in lower expected cost growth, with respect to the historical expert judgment approach. Barter-based key development resource reallocation should work on spacecraft development as well as it has worked on instrument development. A new study of 28 historical NASA science spacecraft developments has an average cost growth of 46.04%. As barter-based key development resource reallocation has never been tried in a spacecraft development, no historical results exist, and a simulation of using that approach must be developed. The instrument development simulation should be modified to account for spacecraft development market participant differences. The resulting agent-based barter-based spacecraft resource reallocation simulation would then be used to determine if significant statistical evidence exists to prove a claim that using barter-based resource reallocation will result in lower expected cost growth.

Agent-Based Simulation to Support the Effectiveness, Procurement, and Employment of Non-Lethal Weapon Systems

DTIC Science & Technology

2017-06-01

cases have the most significant impact on reducing the number of lethal shots fired in the simulation. Table 10 shows the reduction in the average...Figure ES-2 was developed to show the results of the focused study on maximum effective range. After analyzing the results of the 1,700 simulated...toward other agents based on whose side they are on at that time. This attribute is critical to this study as the sidedness of the local population is

The Key Events Dose-Response Framework: a cross-disciplinary mode-of-action based approach to examining dose-response and thresholds.

PubMed

Julien, Elizabeth; Boobis, Alan R; Olin, Stephen S

2009-09-01

The ILSI Research Foundation convened a cross-disciplinary working group to examine current approaches for assessing dose-response and identifying safe levels of intake or exposure for four categories of bioactive agents-food allergens, nutrients, pathogenic microorganisms, and environmental chemicals. This effort generated a common analytical framework-the Key Events Dose-Response Framework (KEDRF)-for systematically examining key events that occur between the initial dose of a bioactive agent and the effect of concern. Individual key events are considered with regard to factors that influence the dose-response relationship and factors that underlie variability in that relationship. This approach illuminates the connection between the processes occurring at the level of fundamental biology and the outcomes observed at the individual and population levels. Thus, it promotes an evidence-based approach for using mechanistic data to reduce reliance on default assumptions, to quantify variability, and to better characterize biological thresholds. This paper provides an overview of the KEDRF and introduces a series of four companion papers that illustrate initial application of the approach to a range of bioactive agents.

Towards a Theoretical Framework for Educational Simulations.

ERIC Educational Resources Information Center

Winer, Laura R.; Vazquez-Abad, Jesus

1981-01-01

Discusses the need for a sustained and systematic effort toward establishing a theoretical framework for educational simulations, proposes the adaptation of models borrowed from the natural and applied sciences, and describes three simulations based on such a model adapted using Brunerian learning theory. Sixteen references are listed. (LLS)

UiO-66-NH2 Metal-Organic Framework (MOF) Nucleation on TiO2, ZnO, and Al2O3 Atomic Layer Deposition-Treated Polymer Fibers: Role of Metal Oxide on MOF Growth and Catalytic Hydrolysis of Chemical Warfare Agent Simulants.

PubMed

Lee, Dennis T; Zhao, Junjie; Oldham, Christopher J; Peterson, Gregory W; Parsons, Gregory N

2017-12-27

Metal-organic frameworks (MOFs) chemically bound to polymeric microfibrous textiles show promising performance for many future applications. In particular, Zr-based UiO-66-family MOF-textiles have been shown to catalytically degrade highly toxic chemical warfare agents (CWAs), where favorable MOF/polymer bonding and adhesion are attained by placing a nanoscale metal-oxide layer on the polymer fiber preceding MOF growth. To date, however, the nucleation mechanism of Zr-based MOFs on different metal oxides and how product performance is affected are not well understood. Herein, we provide new insight into how different inorganic nucleation films (i.e., Al 2 O 3 , ZnO, or TiO 2 ) conformally coated on polypropylene (PP) nonwoven textiles via atomic layer deposition (ALD) influence the quality, overall surface area, and the fractional yield of UiO-66-NH 2 MOF crystals solvothermally grown on fiber substrates. Of the materials explored, we find that TiO 2 ALD layers lead to the most effective overall MOF/fiber adhesion, uniformity, and a rapid catalytic degradation rate for a CWA simulant, dimethyl p-nitrophenyl phosphate (DMNP) with t 1/2 = 15 min, 580-fold faster than the catalytic performance of untreated PP textiles. Interestingly, compared to ALD TiO 2 and Al 2 O 3 , ALD ZnO induces a larger MOF yield in solution and mass loading on PP fibrous mats. However, this larger MOF yield is ascribed to chemical instability of the ZnO layer under MOF formation condition, leading to Zn 2+ ions that promote further homogeneous MOF growth. Insights presented here improve understanding of compatibility between active MOF materials and substrate surfaces, which we believe will help advanced MOF composite materials for a variety of useful functions.

A Collective Case Study of Secondary Students' Model-Based Inquiry on Natural Selection through Programming in an Agent-Based Modeling Environment

ERIC Educational Resources Information Center

Xiang, Lin

2011-01-01

This is a collective case study seeking to develop detailed descriptions of how programming an agent-based simulation influences a group of 8th grade students' model-based inquiry (MBI) by examining students' agent-based programmable modeling (ABPM) processes and the learning outcomes. The context of the present study was a biology unit on…

Interactive, Secure Web-enabled Aircraft Engine Simulation Using XML Databinding Integration

NASA Technical Reports Server (NTRS)

Lin, Risheng; Afjeh, Abdollah A.

2003-01-01

This paper discusses the detailed design of an XML databinding framework for aircraft engine simulation. The framework provides an object interface to access and use engine data. while at the same time preserving the meaning of the original data. The Language independent representation of engine component data enables users to move around XML data using HTTP through disparate networks. The application of this framework is demonstrated via a web-based turbofan propulsion system simulation using the World Wide Web (WWW). A Java Servlet based web component architecture is used for rendering XML engine data into HTML format and dealing with input events from the user, which allows users to interact with simulation data from a web browser. The simulation data can also be saved to a local disk for archiving or to restart the simulation at a later time.

Study of photo-oxidative reactivity of sunscreening agents based on photo-oxidation of uric acid by kinetic Monte Carlo simulation.

PubMed

Moradmand Jalali, Hamed; Bashiri, Hadis; Rasa, Hossein

2015-05-01

In the present study, the mechanism of free radical production by light-reflective agents in sunscreens (TiO2, ZnO and ZrO2) was obtained by applying kinetic Monte Carlo simulation. The values of the rate constants for each step of the suggested mechanism have been obtained by simulation. The effect of the initial concentration of mineral oxides and uric acid on the rate of uric acid photo-oxidation by irradiation of some sun care agents has been studied. The kinetic Monte Carlo simulation results agree qualitatively with the existing experimental data for the production of free radicals by sun care agents. Copyright © 2015 Elsevier B.V. All rights reserved.

Metal organic frameworks for the catalytic detoxification of chemical warfare nerve agents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hupp, Joseph T.; Farha, Omar K.; Katz, Michael J.

A method of using a metal organic framework (MOF) comprising a metal ion and an at least bidendate organic ligand to catalytically detoxify chemical warfare nerve agents including exposing the metal-organic-framework (MOF) to the chemical warfare nerve agent and catalytically decomposing the nerve agent with the MOF.

A Generic Simulation Framework for Non-Entangled based Experimental Quantum Cryptography and Communication: Quantum Cryptography and Communication Simulator (QuCCs)

NASA Astrophysics Data System (ADS)

Buhari, Abudhahir; Zukarnain, Zuriati Ahmad; Khalid, Roszelinda; Zakir Dato', Wira Jaafar Ahmad

2016-11-01

The applications of quantum information science move towards bigger and better heights for the next generation technology. Especially, in the field of quantum cryptography and quantum computation, the world already witnessed various ground-breaking tangible product and promising results. Quantum cryptography is one of the mature field from quantum mechanics and already available in the markets. The current state of quantum cryptography is still under various researches in order to reach the heights of digital cryptography. The complexity of quantum cryptography is higher due to combination of hardware and software. The lack of effective simulation tool to design and analyze the quantum cryptography experiments delays the reaching distance of the success. In this paper, we propose a framework to achieve an effective non-entanglement based quantum cryptography simulation tool. We applied hybrid simulation technique i.e. discrete event, continuous event and system dynamics. We also highlight the limitations of a commercial photonic simulation tool based experiments. Finally, we discuss ideas for achieving one-stop simulation package for quantum based secure key distribution experiments. All the modules of simulation framework are viewed from the computer science perspective.

TSI-Enhanced Pedagogical Agents to Engage Learners in Virtual Worlds

ERIC Educational Resources Information Center

Leung, Steve; Virwaney, Sandeep; Lin, Fuhua; Armstrong, AJ; Dubbelboer, Adien

2013-01-01

Building pedagogical applications in virtual worlds is a multi-disciplinary endeavor that involves learning theories, application development framework, and mediated communication theories. This paper presents a project that integrates game-based learning, multi-agent system architecture (MAS), and the theory of Transformed Social Interaction…

Agent planning in AgScala

NASA Astrophysics Data System (ADS)

Tošić, Saša; Mitrović, Dejan; Ivanović, Mirjana

2013-10-01

Agent-oriented programming languages are designed to simplify the development of software agents, especially those that exhibit complex, intelligent behavior. This paper presents recent improvements of AgScala, an agent-oriented programming language based on Scala. AgScala includes declarative constructs for managing beliefs, actions and goals of intelligent agents. Combined with object-oriented and functional programming paradigms offered by Scala, it aims to be an efficient framework for developing both purely reactive, and more complex, deliberate agents. Instead of the Prolog back-end used initially, the new version of AgScala relies on Agent Planning Package, a more advanced system for automated planning and reasoning.

Ultra-Fast Degradation of Chemical Warfare Agents Using MOF-Nanofiber Kebabs.

PubMed

Zhao, Junjie; Lee, Dennis T; Yaga, Robert W; Hall, Morgan G; Barton, Heather F; Woodward, Ian R; Oldham, Christopher J; Walls, Howard J; Peterson, Gregory W; Parsons, Gregory N

2016-10-10

The threat associated with chemical warfare agents (CWAs) motivates the development of new materials to provide enhanced protection with a reduced burden. Metal-organic frame-works (MOFs) have recently been shown as highly effective catalysts for detoxifying CWAs, but challenges still remain for integrating MOFs into functional filter media and/or protective garments. Herein, we report a series of MOF-nanofiber kebab structures for fast degradation of CWAs. We found TiO 2 coatings deposited via atomic layer deposition (ALD) onto polyamide-6 nanofibers enable the formation of conformal Zr-based MOF thin films including UiO-66, UiO-66-NH 2 , and UiO-67. Cross-sectional TEM images show that these MOF crystals nucleate and grow directly on and around the nanofibers, with strong attachment to the substrates. These MOF-functionalized nanofibers exhibit excellent reactivity for detoxifying CWAs. The half-lives of a CWA simulant compound and nerve agent soman (GD) are as short as 7.3 min and 2.3 min, respectively. These results therefore provide the earliest report of MOF-nanofiber textile composites capable of ultra-fast degradation of CWAs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bioprocess scale-up/down as integrative enabling technology: from fluid mechanics to systems biology and beyond.

PubMed

Delvigne, Frank; Takors, Ralf; Mudde, Rob; van Gulik, Walter; Noorman, Henk

2017-09-01

Efficient optimization of microbial processes is a critical issue for achieving a number of sustainable development goals, considering the impact of microbial biotechnology in agrofood, environment, biopharmaceutical and chemical industries. Many of these applications require scale-up after proof of concept. However, the behaviour of microbial systems remains unpredictable (at least partially) when shifting from laboratory-scale to industrial conditions. The need for robust microbial systems is thus highly needed in this context, as well as a better understanding of the interactions between fluid mechanics and cell physiology. For that purpose, a full scale-up/down computational framework is already available. This framework links computational fluid dynamics (CFD), metabolic flux analysis and agent-based modelling (ABM) for a better understanding of the cell lifelines in a heterogeneous environment. Ultimately, this framework can be used for the design of scale-down simulators and/or metabolically engineered cells able to cope with environmental fluctuations typically found in large-scale bioreactors. However, this framework still needs some refinements, such as a better integration of gas-liquid flows in CFD, and taking into account intrinsic biological noise in ABM. © 2017 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases

NASA Astrophysics Data System (ADS)

Yu, Bin; Wang, Jijun; McGowan, Michael; Vaidyanathan, Ganesh; Younger, Kristofer

In this paper we present Gryphon, a hybrid agent-based stochastic modeling and simulation platform developed for characterizing the geographic spread of infectious diseases and the effects of interventions. We study both local and non-local transmission dynamics of stochastic simulations based on the published parameters and data for SARS. The results suggest that the expected numbers of infections and the timeline of control strategies predicted by our stochastic model are in reasonably good agreement with previous studies. These preliminary results indicate that Gryphon is able to characterize other future infectious diseases and identify endangered regions in advance.

Extending the FairRoot framework to allow for simulation and reconstruction of free streaming data

NASA Astrophysics Data System (ADS)

Al-Turany, M.; Klein, D.; Manafov, A.; Rybalchenko, A.; Uhlig, F.

2014-06-01

The FairRoot framework is the standard framework for simulation, reconstruction and data analysis for the FAIR experiments. The framework is designed to optimise the accessibility for beginners and developers, to be flexible and to cope with future developments. FairRoot enhances the synergy between the different physics experiments. As a first step toward simulation of free streaming data, the time based simulation was introduced to the framework. The next step is the event source simulation. This is achieved via a client server system. After digitization the so called "samplers" can be started, where sampler can read the data of the corresponding detector from the simulation files and make it available for the reconstruction clients. The system makes it possible to develop and validate the online reconstruction algorithms. In this work, the design and implementation of the new architecture and the communication layer will be described.

A Framework of Multi Objectives Negotiation for Dynamic Supply Chain Model

NASA Astrophysics Data System (ADS)

Chai, Jia Yee; Sakaguchi, Tatsuhiko; Shirase, Keiichi

Trends of globalization and advances in Information Technology (IT) have created opportunity in collaborative manufacturing across national borders. A dynamic supply chain utilizes these advances to enable more flexibility in business cooperation. This research proposes a concurrent decision making framework for a three echelons dynamic supply chain model. The dynamic supply chain is formed by autonomous negotiation among agents based on multi agents approach. Instead of generating negotiation aspects (such as amount, price and due date) arbitrary, this framework proposes to utilize the information available at operational level of an organization in order to generate realistic negotiation aspect. The effectiveness of the proposed model is demonstrated by various case studies.

A Framework for Determining the Return on Investment of Simulation-Based Training in Health Care

PubMed Central

Bukhari, Hatim; Andreatta, Pamela; Goldiez, Brian; Rabelo, Luis

2017-01-01

This article describes a framework that has been developed to monetize the real value of simulation-based training in health care. A significant consideration has been given to the incorporation of the intangible and qualitative benefits, not only the tangible and quantitative benefits of simulation-based training in health care. The framework builds from three works: the value measurement methodology (VMM) used by several departments of the US Government, a methodology documented in several books by Dr Jack Phillips to monetize various training approaches, and a traditional return on investment methodology put forth by Frost and Sullivan, and Immersion Medical. All 3 source materials were adapted to create an integrated methodology that can be readily implemented. This article presents details on each of these methods and how they can be integrated and presents a framework that integrates the previous methods. In addition to that, it describes the concept and the application of the developed framework. As a test of the applicability of the framework, a real case study has been used to demonstrate the application of the framework. This case study provides real data related to the correlation between the pediatric patient cardiopulmonary arrest (CPA) survival rates and a simulation-based mock codes at the University of Michigan tertiary care academic medical center. It is important to point out that the proposed framework offers the capability to consider a wide range of benefits and values, but on the other hand, there are several limitations that has been discussed and need to be taken in consideration. PMID:28133988

A Framework for Determining the Return on Investment of Simulation-Based Training in Health Care.

PubMed

Bukhari, Hatim; Andreatta, Pamela; Goldiez, Brian; Rabelo, Luis

2017-01-01

This article describes a framework that has been developed to monetize the real value of simulation-based training in health care. A significant consideration has been given to the incorporation of the intangible and qualitative benefits, not only the tangible and quantitative benefits of simulation-based training in health care. The framework builds from three works: the value measurement methodology (VMM) used by several departments of the US Government, a methodology documented in several books by Dr Jack Phillips to monetize various training approaches, and a traditional return on investment methodology put forth by Frost and Sullivan, and Immersion Medical. All 3 source materials were adapted to create an integrated methodology that can be readily implemented. This article presents details on each of these methods and how they can be integrated and presents a framework that integrates the previous methods. In addition to that, it describes the concept and the application of the developed framework. As a test of the applicability of the framework, a real case study has been used to demonstrate the application of the framework. This case study provides real data related to the correlation between the pediatric patient cardiopulmonary arrest (CPA) survival rates and a simulation-based mock codes at the University of Michigan tertiary care academic medical center. It is important to point out that the proposed framework offers the capability to consider a wide range of benefits and values, but on the other hand, there are several limitations that has been discussed and need to be taken in consideration.

Modeling and simulation of dynamic ant colony's labor division for task allocation of UAV swarm

NASA Astrophysics Data System (ADS)

Wu, Husheng; Li, Hao; Xiao, Renbin; Liu, Jie

2018-02-01

The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of "problem centered + evolutionary solution" and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony's labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs' real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.

Human Behavior & Low Energy Architecture: Linking Environmental Adaptation, Personal Comfort, & Energy Use in the Built Environment

NASA Astrophysics Data System (ADS)

Langevin, Jared

Truly sustainable buildings serve to enrich the daily sensory experience of their human inhabitants while consuming the least amount of energy possible; yet, building occupants and their environmentally adaptive behaviors remain a poorly characterized variable in even the most "green" building design and operation approaches. This deficiency has been linked to gaps between predicted and actual energy use, as well as to eventual problems with occupant discomfort, productivity losses, and health issues. Going forward, better tools are needed for considering the human-building interaction as a key part of energy efficiency strategies that promote good Indoor Environmental Quality (IEQ) in buildings. This dissertation presents the development and implementation of a Human and Building Interaction Toolkit (HABIT), a framework for the integrated simulation of office occupants' thermally adaptive behaviors, IEQ, and building energy use as part of sustainable building design and operation. Development of HABIT begins with an effort to devise more reliable methods for predicting individual occupants' thermal comfort, considered the driving force behind the behaviors of focus for this project. A long-term field study of thermal comfort and behavior is then presented, and the data it generates are used to develop and validate an agent-based behavior simulation model. Key aspects of the agent-based behavior model are described, and its predictive abilities are shown to compare favorably to those of multiple other behavior modeling options. Finally, the agent-based behavior model is linked with whole building energy simulation in EnergyPlus, forming the full HABIT program. The program is used to evaluate the energy and IEQ impacts of several occupant behavior scenarios in the simulation of a case study office building for the Philadelphia climate. Results indicate that more efficient local heating/cooling options may be paired with wider set point ranges to yield up to 24/28% HVAC energy savings in the winter/summer while also reducing thermal unacceptability; however, it is shown that the source of energy being saved must be considered in each case, as local heating options end up replacing cheaper, more carbon-friendly gas heating with expensive, emissions-heavy plug load electricity. The dissertation concludes with a summary of key outcomes and suggests how HABIT may be further developed in the future.

A Metascalable Computing Framework for Large Spatiotemporal-Scale Atomistic Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nomura, K; Seymour, R; Wang, W

2009-02-17

A metascalable (or 'design once, scale on new architectures') parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials based on spatiotemporal data locality principles, which is expected to scale on emerging multipetaflops architectures. The framework consists of: (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms for high complexity problems; (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, while introducing multiple parallelization axes; and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these O(N) algorithms onto a multicore cluster based onmore » hybrid implementation combining message passing and critical section-free multithreading. The EDC-STEP-HCD framework exposes maximal concurrency and data locality, thereby achieving: (1) inter-node parallel efficiency well over 0.95 for 218 billion-atom molecular-dynamics and 1.68 trillion electronic-degrees-of-freedom quantum-mechanical simulations on 212,992 IBM BlueGene/L processors (superscalability); (2) high intra-node, multithreading parallel efficiency (nanoscalability); and (3) nearly perfect time/ensemble parallel efficiency (eon-scalability). The spatiotemporal scale covered by MD simulation on a sustained petaflops computer per day (i.e. petaflops {center_dot} day of computing) is estimated as NT = 2.14 (e.g. N = 2.14 million atoms for T = 1 microseconds).« less

Interaction and Communication of Agents in Networks and Language Complexity Estimates

NASA Technical Reports Server (NTRS)

Smid, Jan; Obitko, Marek; Fisher, David; Truszkowski, Walt

2004-01-01

Knowledge acquisition and sharing are arguably the most critical activities of communicating agents. We report about our on-going project featuring knowledge acquisition and sharing among communicating agents embedded in a network. The applications we target range from hardware robots to virtual entities such as internet agents. Agent experiments can be simulated using a convenient simulation language. We analyzed the complexity of communicating agent simulations using Java and Easel. Scenarios we have studied are listed below. The communication among agents can range from declarative queries to sub-natural language queries. 1) A set of agents monitoring an object are asked to build activity profiles based on exchanging elementary observations; 2) A set of car drivers form a line, where every car is following its predecessor. An unsafe distance cm create a strong wave in the line. Individual agents are asked to incorporate and apply directions how to avoid the wave. 3) A set of micro-vehicles form a grid and are asked to propagate information and concepts to a central server.

The highly intelligent virtual agents for modeling financial markets

NASA Astrophysics Data System (ADS)

Yang, G.; Chen, Y.; Huang, J. P.

2016-02-01

Researchers have borrowed many theories from statistical physics, like ensemble, Ising model, etc., to study complex adaptive systems through agent-based modeling. However, one fundamental difference between entities (such as spins) in physics and micro-units in complex adaptive systems is that the latter are usually with high intelligence, such as investors in financial markets. Although highly intelligent virtual agents are essential for agent-based modeling to play a full role in the study of complex adaptive systems, how to create such agents is still an open question. Hence, we propose three principles for designing high artificial intelligence in financial markets and then build a specific class of agents called iAgents based on these three principles. Finally, we evaluate the intelligence of iAgents through virtual index trading in two different stock markets. For comparison, we also include three other types of agents in this contest, namely, random traders, agents from the wealth game (modified on the famous minority game), and agents from an upgraded wealth game. As a result, iAgents perform the best, which gives a well support for the three principles. This work offers a general framework for the further development of agent-based modeling for various kinds of complex adaptive systems.

Using an agent-based model to examine forest management outcomes in a fire-prone landscape in Oregon, USA

Treesearch

Thomas A. Spies; Eric White; Alan Ager; Jeffrey D. Kline; John P. Bolte; Emily K. Platt; Keith A. Olsen; Robert J. Pabst; Ana M. G. Barros; John D. Bailey; Susan Charnley; Anita T. Morzillo; Jennifer Koch; Michelle M. Steen-Adams; Peter H. Singleton; James Sulzman; Cynthia Schwartz; Blair Csuti

2017-01-01

Fire-prone landscapes present many challenges for both managers and policy makers in developing adaptive behaviors and institutions. We used a coupled human and natural systems framework and an agent-based landscape model to examine how alternative management scenarios affect fire and ecosystem services metrics in a fire-prone multiownership landscape in the eastern...

Mutual and asynchronous anticipation and action in sports as globally competitive and locally coordinative dynamics

PubMed Central

Fujii, Keisuke; Isaka, Tadao; Kouzaki, Motoki; Yamamoto, Yuji

2015-01-01

Humans interact by changing their actions, perceiving other’s actions and executing solutions in conflicting situations. Using oscillator models, nonlinear dynamics have been considered for describing these complex human movements as an emergence of self-organisation. However, these frameworks cannot explain the hierarchical structures of complex behaviours between conflicting inter-agent and adapting intra-agent systems, especially in sport competitions wherein mutually quick decision making and execution are required. Here we adopt a hybrid multiscale approach to model an attack-and-defend game during which both players predict the opponent’s movement and move with a delay. From both simulated and measured data, one synchronous outcome between two-agent (i.e. successful defence) can be described as one attractor. In contrast, the other coordination-breaking outcome (i.e. successful attack) cannot be explained using gradient dynamics because the asymmetric interaction cannot always assume a conserved physical quantity. Instead, we provide the asymmetric and asynchronous hierarchical dynamical models to discuss two-agent competition. Our framework suggests that possessing information about an opponent and oneself in local-coordinative and global-competitive scale enables us to gain a deeper understanding of sports competitions. We anticipate developments in the scientific fields of complex movement adapting to such uncontrolled environments. PMID:26538452

Mutual and asynchronous anticipation and action in sports as globally competitive and locally coordinative dynamics

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Isaka, Tadao; Kouzaki, Motoki; Yamamoto, Yuji

2015-11-01

Humans interact by changing their actions, perceiving other’s actions and executing solutions in conflicting situations. Using oscillator models, nonlinear dynamics have been considered for describing these complex human movements as an emergence of self-organisation. However, these frameworks cannot explain the hierarchical structures of complex behaviours between conflicting inter-agent and adapting intra-agent systems, especially in sport competitions wherein mutually quick decision making and execution are required. Here we adopt a hybrid multiscale approach to model an attack-and-defend game during which both players predict the opponent’s movement and move with a delay. From both simulated and measured data, one synchronous outcome between two-agent (i.e. successful defence) can be described as one attractor. In contrast, the other coordination-breaking outcome (i.e. successful attack) cannot be explained using gradient dynamics because the asymmetric interaction cannot always assume a conserved physical quantity. Instead, we provide the asymmetric and asynchronous hierarchical dynamical models to discuss two-agent competition. Our framework suggests that possessing information about an opponent and oneself in local-coordinative and global-competitive scale enables us to gain a deeper understanding of sports competitions. We anticipate developments in the scientific fields of complex movement adapting to such uncontrolled environments.

Re-Examining of Moffitt’s Theory of Delinquency through Agent Based Modeling

PubMed Central

Leaw, Jia Ning; Ang, Rebecca P.; Huan, Vivien S.; Chan, Wei Teng; Cheong, Siew Ann

2015-01-01

Moffitt’s theory of delinquency suggests that at-risk youths can be divided into two groups, the adolescence- limited group and the life-course-persistent group, predetermined at a young age, and social interactions between these two groups become important during the adolescent years. We built an agent-based model based on the microscopic interactions Moffitt described: (i) a maturity gap that dictates (ii) the cost and reward of antisocial behavior, and (iii) agents imitating the antisocial behaviors of others more successful than themselves, to find indeed the two groups emerging in our simulations. Moreover, through an intervention simulation where we moved selected agents from one social network to another, we also found that the social network plays an important role in shaping the life course outcome. PMID:26062022

Disturbance of DNA conformation by the binding of testosterone-based platinum drugs via groove-face and intercalative interactions: a molecular dynamics simulation study

PubMed Central

2013-01-01

Background To explore novel platinum-based anticancer agents that are distinct from the structure and interaction mode of the traditional cisplatin by forming the bifunctional intrastrand 1,2 GpG adduct, the monofunctional platinum + DNA adducts with extensive non-covalent interactions had been studied. It was reported that the monofunctional testosterone-based platinum(II) agents present the high anticancer activity. Moreover, it was also found that the testosterone-based platinum agents could cause the DNA helix to undergo significant unwinding and bending over the non-testosterone-based platinum agents. However, the interaction mechanisms of these platinum agents with DNA at the atomic level are not yet clear so far. Results In the present work, we used molecular dynamics (MD) simulations and DNA conformational dynamics calculations to study the DNA distortion properties of the testosterone-based platinum + DNA, the improved testosterone-based platinum + DNA and the non-testosterone-based platinum + DNA adducts. The results show that the intercalative interaction of the improved flexible testosterone-based platinum agent with DNA molecule could cause larger DNA conformational distortion than the groove-face interaction of the rigid testosterone-based platinum agent with DNA molecule. Further investigations for the non-testosterone-based platinum agent reveal the occurrence of insignificant change of DNA conformation due to the absence of testosterone ligand in such agent. Based on the DNA dynamics analysis, the DNA base motions relating to DNA groove parameter changes and hydrogen bond destruction of DNA base pairs were also discussed in this work. Conclusions The flexible linker in the improved testosterone-based platinum agent causes an intercalative interaction with DNA in the improved testosterone-based platinum + DNA adduct, which is different from the groove-face interaction caused by a rigid linker in the testosterone-based platinum agent. The present investigations provide useful information of DNA conformation affected by a testosterone-based platinum complex at the atomic level. PMID:23517640

Transition to parenthood: the role of social interaction and endogenous networks.

PubMed

Diaz, Belinda Aparicio; Fent, Thomas; Prskawetz, Alexia; Bernardi, Laura

2011-05-01

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdependencies across individuals' transition to parenthood and its timing, we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics: age, intended education, and parity. Agents endogenously form their network based on social closeness. Network members may then influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social interactions can explain the shift of first-birth probabilities in Austria during the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

An immunity-based anomaly detection system with sensor agents.

PubMed

Okamoto, Takeshi; Ishida, Yoshiteru

2009-01-01

This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

Mission planning and simulation via intelligent agents

NASA Technical Reports Server (NTRS)

Gargan, Robert A., Jr.; Tilley, Randall W.

1987-01-01

A system that can operate from a flight manifest to plan and simulate payload preparation and transport via Shuttle flights is described. The design alternatives and the prototype implementation of the payload hardware and inventory tracking system are discussed. It is shown how intelligent agents can be used to generate mission schedules, and how, through the use of these intelligent agents, knowledge becomes separated into small manageable knowledge bases.

Task-based image quality assessment in radiation therapy: initial characterization and demonstration with CT simulation images

NASA Astrophysics Data System (ADS)

Dolly, Steven R.; Anastasio, Mark A.; Yu, Lifeng; Li, Hua

2017-03-01

In current radiation therapy practice, image quality is still assessed subjectively or by utilizing physically-based metrics. Recently, a methodology for objective task-based image quality (IQ) assessment in radiation therapy was proposed by Barrett et al.1 In this work, we present a comprehensive implementation and evaluation of this new IQ assessment methodology. A modular simulation framework was designed to perform an automated, computer-simulated end-to-end radiation therapy treatment. A fully simulated framework was created that utilizes new learning-based stochastic object models (SOM) to obtain known organ boundaries, generates a set of images directly from the numerical phantoms created with the SOM, and automates the image segmentation and treatment planning steps of a radiation therapy work ow. By use of this computational framework, therapeutic operating characteristic (TOC) curves can be computed and the area under the TOC curve (AUTOC) can be employed as a figure-of-merit to guide optimization of different components of the treatment planning process. The developed computational framework is employed to optimize X-ray CT pre-treatment imaging. We demonstrate that use of the radiation therapy-based-based IQ measures lead to different imaging parameters than obtained by use of physical-based measures.

Sampled-data consensus in switching networks of integrators based on edge events

NASA Astrophysics Data System (ADS)

Xiao, Feng; Meng, Xiangyu; Chen, Tongwen

2015-02-01

This paper investigates the event-driven sampled-data consensus in switching networks of multiple integrators and studies both the bidirectional interaction and leader-following passive reaction topologies in a unified framework. In these topologies, each information link is modelled by an edge of the information graph and assigned a sequence of edge events, which activate the mutual data sampling and controller updates of the two linked agents. Two kinds of edge-event-detecting rules are proposed for the general asynchronous data-sampling case and the synchronous periodic event-detecting case. They are implemented in a distributed fashion, and their effectiveness in reducing communication costs and solving consensus problems under a jointly connected topology condition is shown by both theoretical analysis and simulation examples.

Agent-based simulation of a financial market

NASA Astrophysics Data System (ADS)

Raberto, Marco; Cincotti, Silvano; Focardi, Sergio M.; Marchesi, Michele

2001-10-01

This paper introduces an agent-based artificial financial market in which heterogeneous agents trade one single asset through a realistic trading mechanism for price formation. Agents are initially endowed with a finite amount of cash and a given finite portfolio of assets. There is no money-creation process; the total available cash is conserved in time. In each period, agents make random buy and sell decisions that are constrained by available resources, subject to clustering, and dependent on the volatility of previous periods. The model proposed herein is able to reproduce the leptokurtic shape of the probability density of log price returns and the clustering of volatility. Implemented using extreme programming and object-oriented technology, the simulator is a flexible computational experimental facility that can find applications in both academic and industrial research projects.

Component Framework for Loosely Coupled High Performance Integrated Plasma Simulations

NASA Astrophysics Data System (ADS)

Elwasif, W. R.; Bernholdt, D. E.; Shet, A. G.; Batchelor, D. B.; Foley, S.

2010-11-01

We present the design and implementation of a component-based simulation framework for the execution of coupled time-dependent plasma modeling codes. The Integrated Plasma Simulator (IPS) provides a flexible lightweight component model that streamlines the integration of stand alone codes into coupled simulations. Standalone codes are adapted to the IPS component interface specification using a thin wrapping layer implemented in the Python programming language. The framework provides services for inter-component method invocation, configuration, task, and data management, asynchronous event management, simulation monitoring, and checkpoint/restart capabilities. Services are invoked, as needed, by the computational components to coordinate the execution of different aspects of coupled simulations on Massive parallel Processing (MPP) machines. A common plasma state layer serves as the foundation for inter-component, file-based data exchange. The IPS design principles, implementation details, and execution model will be presented, along with an overview of several use cases.

A Multi-Paradigm Modeling Framework to Simulate Dynamic Reciprocity in a Bioreactor

PubMed Central

Kaul, Himanshu; Cui, Zhanfeng; Ventikos, Yiannis

2013-01-01

Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications. PMID:23555740

Simulating cancer growth with multiscale agent-based modeling.

PubMed

Wang, Zhihui; Butner, Joseph D; Kerketta, Romica; Cristini, Vittorio; Deisboeck, Thomas S

2015-02-01

There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models. Copyright © 2014 Elsevier Ltd. All rights reserved.

Model reduction for agent-based social simulation: coarse-graining a civil violence model.

PubMed

Zou, Yu; Fonoberov, Vladimir A; Fonoberova, Maria; Mezic, Igor; Kevrekidis, Ioannis G

2012-06-01

Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20).

Model reduction for agent-based social simulation: Coarse-graining a civil violence model

NASA Astrophysics Data System (ADS)

Zou, Yu; Fonoberov, Vladimir A.; Fonoberova, Maria; Mezic, Igor; Kevrekidis, Ioannis G.

2012-06-01

Agent-based modeling (ABM) constitutes a powerful computational tool for the exploration of phenomena involving emergent dynamic behavior in the social sciences. This paper demonstrates a computer-assisted approach that bridges the significant gap between the single-agent microscopic level and the macroscopic (coarse-grained population) level, where fundamental questions must be rationally answered and policies guiding the emergent dynamics devised. Our approach will be illustrated through an agent-based model of civil violence. This spatiotemporally varying ABM incorporates interactions between a heterogeneous population of citizens [active (insurgent), inactive, or jailed] and a population of police officers. Detailed simulations exhibit an equilibrium punctuated by periods of social upheavals. We show how to effectively reduce the agent-based dynamics to a stochastic model with only two coarse-grained degrees of freedom: the number of jailed citizens and the number of active ones. The coarse-grained model captures the ABM dynamics while drastically reducing the computation time (by a factor of approximately 20).

Wealth distribution, Pareto law, and stretched exponential decay of money: Computer simulations analysis of agent-based models

NASA Astrophysics Data System (ADS)

Aydiner, Ekrem; Cherstvy, Andrey G.; Metzler, Ralf

2018-01-01

We study by Monte Carlo simulations a kinetic exchange trading model for both fixed and distributed saving propensities of the agents and rationalize the person and wealth distributions. We show that the newly introduced wealth distribution - that may be more amenable in certain situations - features a different power-law exponent, particularly for distributed saving propensities of the agents. For open agent-based systems, we analyze the person and wealth distributions and find that the presence of trap agents alters their amplitude, leaving however the scaling exponents nearly unaffected. For an open system, we show that the total wealth - for different trap agent densities and saving propensities of the agents - decreases in time according to the classical Kohlrausch-Williams-Watts stretched exponential law. Interestingly, this decay does not depend on the trap agent density, but rather on saving propensities. The system relaxation for fixed and distributed saving schemes are found to be different.

Binding of chemical warfare agent simulants as guests in a coordination cage: contributions to binding and a fluorescence-based response.

PubMed

Taylor, Christopher G P; Piper, Jerico R; Ward, Michael D

2016-05-07

Cubic coordination cages act as competent hosts for several alkyl phosphonates used as chemical warfare agent simulants; a range of cage/guest structures have been determined, contributions to guest binding analysed, and a fluorescent response to guest binding demonstrated.

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.

PubMed

Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

2017-01-01

Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

PubMed Central

Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

2017-01-01

Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation. PMID:28596730

Efficient Allocation of Resources for Defense of Spatially Distributed Networks Using Agent-Based Simulation.

PubMed

Kroshl, William M; Sarkani, Shahram; Mazzuchi, Thomas A

2015-09-01

This article presents ongoing research that focuses on efficient allocation of defense resources to minimize the damage inflicted on a spatially distributed physical network such as a pipeline, water system, or power distribution system from an attack by an active adversary, recognizing the fundamental difference between preparing for natural disasters such as hurricanes, earthquakes, or even accidental systems failures and the problem of allocating resources to defend against an opponent who is aware of, and anticipating, the defender's efforts to mitigate the threat. Our approach is to utilize a combination of integer programming and agent-based modeling to allocate the defensive resources. We conceptualize the problem as a Stackelberg "leader follower" game where the defender first places his assets to defend key areas of the network, and the attacker then seeks to inflict the maximum damage possible within the constraints of resources and network structure. The criticality of arcs in the network is estimated by a deterministic network interdiction formulation, which then informs an evolutionary agent-based simulation. The evolutionary agent-based simulation is used to determine the allocation of resources for attackers and defenders that results in evolutionary stable strategies, where actions by either side alone cannot increase its share of victories. We demonstrate these techniques on an example network, comparing the evolutionary agent-based results to a more traditional, probabilistic risk analysis (PRA) approach. Our results show that the agent-based approach results in a greater percentage of defender victories than does the PRA-based approach. © 2015 Society for Risk Analysis.

Anticipating surprise: Using agent-based alternative futures simulation modeling to identify and map surprising fires in the Willamette Valley, Oregon USA

Treesearch

David Hulse; Allan Branscomb; Chris Enright; Bart Johnson; Cody Evers; John Bolte; Alan Ager

2016-01-01

This article offers a literature-supported conception and empirically grounded analysis of surprise by exploring the capacity of scenario-driven, agent-based simulation models to better anticipate it. Building on literature-derived definitions and typologies of surprise, and using results from a modeled 81,000 ha study area in a wildland-urban interface of western...

Agent-Based Simulation and Analysis of a Defensive UAV Swarm Against an Enemy UAV Swarm

DTIC Science & Technology

2011-06-01

de Investigacion, Programas y Desarrollo de la Armada Armada de Chile CHILE 10. CAPT Jeffrey Kline, USN(ret.) Naval Postgraduate School Monterey, California 91 ...this de - fensive swarm system, an agent-based simulation model is developed, and appropriate designs of experiments and statistical analyses are... de - velopment and implementation of counter UAV technology from readily-available commercial products. The organization leverages the “largest

Shared Activity Coordination

NASA Technical Reports Server (NTRS)

Clement, Bradley J.; Barrett, Anthony C.

2003-01-01

Interacting agents that interleave planning and execution must reach consensus on their commitments to each other. In domains where agents have varying degrees of interaction and different constraints on communication and computation, agents will require different coordination protocols in order to efficiently reach consensus in real time. We briefly describe a largely unexplored class of real-time, distributed planning problems (inspired by interacting spacecraft missions), new challenges they pose, and a general approach to solving the problems. These problems involve self-interested agents that have infrequent communication but collaborate on joint activities. We describe a Shared Activity Coordination (SHAC) framework that provides a decentralized algorithm for negotiating the scheduling of shared activities in a dynamic environment, a soft, real-time approach to reaching consensus during execution with limited communication, and a foundation for customizing protocols for negotiating planner interactions. We apply SHAC to a realistic simulation of interacting Mars missions and illustrate the simplicity of protocol development.

Continual coordination through shared activities

NASA Technical Reports Server (NTRS)

Clement, Bradley J.; Barrett, Anthony C.

2003-01-01

Interacting agents that interleave planning and execution must reach consensus on their commitments to each other. In domains where agents have varying degrees of interaction and different constraints on communication and computation, agents will require different coordination protocols in order to efficiently reach consensus in real time. We briefly describe a largely unexplored class of realtime, distributed planning problems (inspired by interacting spacecraft missions), new challenges they pose, and a general approach to solving the problems. These problems involve self-interested agents that have infrequent communication but collaborate on joint activities. We describe a Shared Activity Coordination (SHAC) framework that provides a decentralized algorithm for negotiating the scheduling of shared activities over the lifetimes of separate missions, a soft, real-time approach to reaching consensus during execution with limited communication, and a foundation for customizing protocols for negotiating planner interactions. We apply SHAC to a realistic simulation of interacting Mars missions and illustrate the simplicity of protocol development.

Using the Integration of Discrete Event and Agent-Based Simulation to Enhance Outpatient Service Quality in an Orthopedic Department.

PubMed

Kittipittayakorn, Cholada; Ying, Kuo-Ching

2016-01-01

Many hospitals are currently paying more attention to patient satisfaction since it is an important service quality index. Many Asian countries' healthcare systems have a mixed-type registration, accepting both walk-in patients and scheduled patients. This complex registration system causes a long patient waiting time in outpatient clinics. Different approaches have been proposed to reduce the waiting time. This study uses the integration of discrete event simulation (DES) and agent-based simulation (ABS) to improve patient waiting time and is the first attempt to apply this approach to solve this key problem faced by orthopedic departments. From the data collected, patient behaviors are modeled and incorporated into a massive agent-based simulation. The proposed approach is an aid for analyzing and modifying orthopedic department processes, allows us to consider far more details, and provides more reliable results. After applying the proposed approach, the total waiting time of the orthopedic department fell from 1246.39 minutes to 847.21 minutes. Thus, using the correct simulation model significantly reduces patient waiting time in an orthopedic department.

Using the Integration of Discrete Event and Agent-Based Simulation to Enhance Outpatient Service Quality in an Orthopedic Department

PubMed Central

Kittipittayakorn, Cholada

2016-01-01

Many hospitals are currently paying more attention to patient satisfaction since it is an important service quality index. Many Asian countries' healthcare systems have a mixed-type registration, accepting both walk-in patients and scheduled patients. This complex registration system causes a long patient waiting time in outpatient clinics. Different approaches have been proposed to reduce the waiting time. This study uses the integration of discrete event simulation (DES) and agent-based simulation (ABS) to improve patient waiting time and is the first attempt to apply this approach to solve this key problem faced by orthopedic departments. From the data collected, patient behaviors are modeled and incorporated into a massive agent-based simulation. The proposed approach is an aid for analyzing and modifying orthopedic department processes, allows us to consider far more details, and provides more reliable results. After applying the proposed approach, the total waiting time of the orthopedic department fell from 1246.39 minutes to 847.21 minutes. Thus, using the correct simulation model significantly reduces patient waiting time in an orthopedic department. PMID:27195606

On System Engineering a Barter-Based Re-allocation of Space System Key Development Resources

NASA Astrophysics Data System (ADS)

Kosmann, William J.

NASA has had a decades-long problem with cost growth during the development of space science missions. Numerous agency-sponsored studies have produced average mission level development cost growths ranging from 23 to 77%. A new study of 26 historical NASA science instrument set developments using expert judgment to re-allocate key development resources has an average cost growth of 73.77%. Twice in history, during the Cassini and EOS-Terra science instrument developments, a barter-based mechanism has been used to re-allocate key development resources. The mean instrument set development cost growth was -1.55%. Performing a bivariate inference on the means of these two distributions, there is statistical evidence to support the claim that using a barter-based mechanism to re-allocate key instrument development resources will result in a lower expected cost growth than using the expert judgment approach. Agent-based discrete event simulation is the natural way to model a trade environment. A NetLogo agent-based barter-based simulation of science instrument development was created. The agent-based model was validated against the Cassini historical example, as the starting and ending instrument development conditions are available. The resulting validated agent-based barter-based science instrument resource re-allocation simulation was used to perform 300 instrument development simulations, using barter to re-allocate development resources. The mean cost growth was -3.365%. A bivariate inference on the means was performed to determine that additional significant statistical evidence exists to support a claim that using barter-based resource re-allocation will result in lower expected cost growth, with respect to the historical expert judgment approach. Barter-based key development resource re-allocation should work on science spacecraft development as well as it has worked on science instrument development. A new study of 28 historical NASA science spacecraft developments has an average cost growth of 46.04%. As barter-based key development resource re-allocation has never been tried in a spacecraft development, no historical results exist, and an inference on the means test is not possible. A simulation of using barter-based resource re-allocation should be developed. The NetLogo instrument development simulation should be modified to account for spacecraft development market participant differences. The resulting agent-based barter-based spacecraft resource re-allocation simulation would then be used to determine if significant statistical evidence exists to prove a claim that using barter-based resource re-allocation will result in lower expected cost growth.

Bee++: An Object-Oriented, Agent-Based Simulator for Honey Bee Colonies

PubMed Central

Betti, Matthew; LeClair, Josh; Wahl, Lindi M.; Zamir, Mair

2017-01-01

We present a model and associated simulation package (www.beeplusplus.ca) to capture the natural dynamics of a honey bee colony in a spatially-explicit landscape, with temporally-variable, weather-dependent parameters. The simulation tracks bees of different ages and castes, food stores within the colony, pollen and nectar sources and the spatial position of individual foragers outside the hive. We track explicitly the intake of pesticides in individual bees and their ability to metabolize these toxins, such that the impact of sub-lethal doses of pesticides can be explored. Moreover, pathogen populations (in particular, Nosema apis, Nosema cerenae and Varroa mites) have been included in the model and may be introduced at any time or location. The ability to study interactions among pesticides, climate, biodiversity and pathogens in this predictive framework should prove useful to a wide range of researchers studying honey bee populations. To this end, the simulation package is written in open source, object-oriented code (C++) and can be easily modified by the user. Here, we demonstrate the use of the model by exploring the effects of sub-lethal pesticide exposure on the flight behaviour of foragers. PMID:28287445

Niobium(V) saponite clay for the catalytic oxidative abatement of chemical warfare agents.

PubMed

Carniato, Fabio; Bisio, Chiara; Psaro, Rinaldo; Marchese, Leonardo; Guidotti, Matteo

2014-09-15

A Nb(V)-containing saponite clay was designed to selectively transform toxic organosulfur chemical warfare agents (CWAs) under extremely mild conditions into nontoxic products with reduced environmental impact. Thanks to the insertion of Nb(V) sites within the saponite framework, a bifunctional catalyst with strong oxidizing and acid properties was obtained. Remarkable activity and high selectivity were observed for the oxidative abatement of (2-chloroethyl)ethyl sulfide (CEES), a simulant of sulfur mustard, at room temperature with aqueous hydrogen peroxide. This performance was significantly better compared to a conventional commercial decontamination powder. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

PISA — Pooling Information from Several Agents: Multiplayer Argumentation from Experience

NASA Astrophysics Data System (ADS)

Wardeh, Maya; Bench-Capon, Trevor; Coenen, Frans

In this paper a framework, PISA (Pooling Information from Several Agents), to facilitate multiplayer (three or more protagonists), "argumentation from experience" is described. Multiplayer argumentation is a form of dialogue game involving three or more players. The PISA framework is founded on a two player argumentation framework, PADUA (Protocol for Argumentation Dialogue Using Association Rules), also developed by the authors. One of the main advantages of both PISA and PADUA is that they avoid the resource intensive need to predefine a knowledge base, instead data mining techniques are used to facilitate the provision of "just in time" information. Many of the issues associated with multiplayer dialogue games do not present a significant challenge in the two player game. The main original contributions of this paper are the mechanisms whereby the PISA framework addresses these challenges.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

Evaluation of water security in Jordan using a multi-agent, hydroeconomic model: Initial model results from the Jordan Water Project

NASA Astrophysics Data System (ADS)

Yoon, J.; Klassert, C. J. A.; Lachaut, T.; Selby, P. D.; Knox, S.; Gorelick, S.; Rajsekhar, D.; Tilmant, A.; Avisse, N.; Harou, J. J.; Medellin-Azuara, J.; Gawel, E.; Klauer, B.; Mustafa, D.; Talozi, S.; Sigel, K.; Zhang, H.

2016-12-01

Our work focuses on development of a multi-agent, hydroeconomic model for water policy evaluation in Jordan. Jordan ranks among the most water-scarce countries in the world, a situation exacerbated due to a recent influx of refugees escaping the ongoing civil war in neighboring Syria. The modular, multi-agent model is used to evaluate interventions for enhancing Jordan's water security, integrating biophysical modules that simulate natural and engineered phenomena with human modules that represent behavior at multiple levels of decision making. The hydrologic modules are developed using spatially-distributed groundwater and surface water models, which are translated into compact simulators for efficient integration into the multi-agent model. For the multi-agent model, we explicitly account for human agency at multiple levels of decision making, with agents representing riparian, management, supplier, and water user groups. Human agents are implemented as autonomous entities in the model that make decisions in relation to one another and in response to hydrologic and socioeconomic conditions. The integrated model is programmed in Python using Pynsim, a generalizable, open-source object-oriented software framework for modeling network-based water resource systems. The modeling time periods include historical (2006-2014) and future (present-2050) time spans. For the historical runs, the model performance is validated against historical data for several observations that reflect the interacting dynamics of both the hydrologic and human components of the system. A historical counterfactual scenario is also constructed to isolate and identify the impacts of the recent Syrian civil war and refugee crisis on Jordan's water system. For the future period, model runs are conducted to evaluate potential supply, demand, and institutional interventions over a wide range of plausible climate and socioeconomic scenarios. In addition, model sensitivity analysis is conducted revealing the hydrologic and human aspects of the system that most strongly influence water security outcomes, providing insight into coupled human-water system dynamics as well as priority areas of focus for continued model improvement.

Fuzzy Hybrid Deliberative/Reactive Paradigm (FHDRP)

NASA Technical Reports Server (NTRS)

Sarmadi, Hengameth

2004-01-01

This work aims to introduce a new concept for incorporating fuzzy sets in hybrid deliberative/reactive paradigm. After a brief review on basic issues of hybrid paradigm the definition of agent-based fuzzy hybrid paradigm, which enables the agents to proceed and extract their behavior through quantitative numerical and qualitative knowledge and to impose their decision making procedure via fuzzy rule bank, is discussed. Next an example performs a more applied platform for the developed approach and finally an overview of the corresponding agents architecture enhances agents logical framework.

Impact of different policies on unhealthy dietary behaviors in an urban adult population: an agent-based simulation model.

PubMed

Zhang, Donglan; Giabbanelli, Philippe J; Arah, Onyebuchi A; Zimmerman, Frederick J

2014-07-01

Unhealthy eating is a complex-system problem. We used agent-based modeling to examine the effects of different policies on unhealthy eating behaviors. We developed an agent-based simulation model to represent a synthetic population of adults in Pasadena, CA, and how they make dietary decisions. Data from the 2007 Food Attitudes and Behaviors Survey and other empirical studies were used to calibrate the parameters of the model. Simulations were performed to contrast the potential effects of various policies on the evolution of dietary decisions. Our model showed that a 20% increase in taxes on fast foods would lower the probability of fast-food consumption by 3 percentage points, whereas improving the visibility of positive social norms by 10%, either through community-based or mass-media campaigns, could improve the consumption of fruits and vegetables by 7 percentage points and lower fast-food consumption by 6 percentage points. Zoning policies had no significant impact. Interventions emphasizing healthy eating norms may be more effective than directly targeting food prices or regulating local food outlets. Agent-based modeling may be a useful tool for testing the population-level effects of various policies within complex systems.

OntoTrader: An Ontological Web Trading Agent Approach for Environmental Information Retrieval

PubMed Central

Iribarne, Luis; Padilla, Nicolás; Ayala, Rosa; Asensio, José A.; Criado, Javier

2014-01-01

Modern Web-based Information Systems (WIS) are becoming increasingly necessary to provide support for users who are in different places with different types of information, by facilitating their access to the information, decision making, workgroups, and so forth. Design of these systems requires the use of standardized methods and techniques that enable a common vocabulary to be defined to represent the underlying knowledge. Thus, mediation elements such as traders enrich the interoperability of web components in open distributed systems. These traders must operate with other third-party traders and/or agents in the system, which must also use a common vocabulary for communication between them. This paper presents the OntoTrader architecture, an Ontological Web Trading agent based on the OMG ODP trading standard. It also presents the ontology needed by some system agents to communicate with the trading agent and the behavioral framework for the SOLERES OntoTrader agent, an Environmental Management Information System (EMIS). This framework implements a “Query-Searching/Recovering-Response” information retrieval model using a trading service, SPARQL notation, and the JADE platform. The paper also presents reflection, delegation and, federation mediation models and describes formalization, an experimental testing environment in three scenarios, and a tool which allows our proposal to be evaluated and validated. PMID:24977211

OntoTrader: an ontological Web trading agent approach for environmental information retrieval.

PubMed

Iribarne, Luis; Padilla, Nicolás; Ayala, Rosa; Asensio, José A; Criado, Javier

2014-01-01

Modern Web-based Information Systems (WIS) are becoming increasingly necessary to provide support for users who are in different places with different types of information, by facilitating their access to the information, decision making, workgroups, and so forth. Design of these systems requires the use of standardized methods and techniques that enable a common vocabulary to be defined to represent the underlying knowledge. Thus, mediation elements such as traders enrich the interoperability of web components in open distributed systems. These traders must operate with other third-party traders and/or agents in the system, which must also use a common vocabulary for communication between them. This paper presents the OntoTrader architecture, an Ontological Web Trading agent based on the OMG ODP trading standard. It also presents the ontology needed by some system agents to communicate with the trading agent and the behavioral framework for the SOLERES OntoTrader agent, an Environmental Management Information System (EMIS). This framework implements a "Query-Searching/Recovering-Response" information retrieval model using a trading service, SPARQL notation, and the JADE platform. The paper also presents reflection, delegation and, federation mediation models and describes formalization, an experimental testing environment in three scenarios, and a tool which allows our proposal to be evaluated and validated.

Computational Investigations of Potential Energy Function Development for Metal--Organic Framework Simulations, Metal Carbenes, and Chemical Warfare Agents

NASA Astrophysics Data System (ADS)

Cioce, Christian R.

Metal-Organic Frameworks (MOFs) are three-dimensional porous nanomaterials with a variety of applications, including catalysis, gas storage and separation, and sustainable energy. Their potential as air filtration systems is of interest for designer carbon capture materials. The chemical constituents (i.e. organic ligands) can be functionalized to create rationally designed CO2 sequestration platforms, for example. Hardware and software alike at the bleeding edge of supercomputing are utilized for designing first principles-based molecular models for the simulation of gas sorption in these frameworks. The classical potentials developed herein are named PHAST --- Potentials with High Accuracy, Speed, and Transferability, and thus are designed via a "bottom-up" approach. Specifically, models for N2 and CH4 are constructed and presented. Extensive verification and validation leads to insights and range of applicability. Through this experience, the PHAST models are improved upon further to be more applicable in heterogeneous environments. Given this, the models are applied to reproducing high level ab initio energies for gas sorption trajectories of helium atoms in a variety of rare-gas clusters, the geometries of which being representative of sorption-like environments commonly encountered in a porous nanomaterial. This work seeks to push forward the state of classical and first principles materials modeling. Additionally, the characterization of a new type of tunable radical metal---carbene is presented. Here, a cobalt(II)---porphyrin complex, [Co(Por)], was investigated to understand its role as an effective catalyst in stereoselective cyclopropanation of a diazoacetate reagent. Density functional theory along with natural bond order analysis and charge decomposition analysis gave insight into the electronics of the catalytic intermediate. The bonding pattern unveiled a new class of radical metal---carbene complex, with a doublet cobalt into which a triplet carbene sigma donates, and subsequent back-bonding occurs into a pi* antibonding orbital. This is a different type of interaction not seen in the three existing classes of metal-carbene complexes, namely Fischer, Schrock, and Grubbs. Finally, the virtual engineering of enhanced chemical warfare agent (CWA) detection systems is discussed. As part of a U.S. Department of Defense supported research project, in silico chemical modifications to a previously synthesized zinc-porphyrin, ZnCS1, were made to attempt to achieve preferential binding of the nerve agent sarin versus its simulant, DIMP (diisopropyl methylphosphonate). Upon modification, a combination of steric effects and induced hydrogen bonding allowed for the selective binding of sarin. The success of this work demonstrates the role that high performance computing can play in national security research, without the associated costs and high security required for experimentation.

Mitigating randomness of consumer preferences under certain conditional choices

NASA Astrophysics Data System (ADS)

Bothos, John M. A.; Thanos, Konstantinos-Georgios; Papadopoulou, Eirini; Daveas, Stelios; Thomopoulos, Stelios C. A.

2017-05-01

Agent-based crowd behaviour consists a significant field of research that has drawn a lot of attention in recent years. Agent-based crowd simulation techniques have been used excessively to forecast the behaviour of larger or smaller crowds in terms of certain given conditions influenced by specific cognition models and behavioural rules and norms, imposed from the beginning. Our research employs conditional event algebra, statistical methodology and agent-based crowd simulation techniques in developing a behavioural econometric model about the selection of certain economic behaviour by a consumer that faces a spectre of potential choices when moving and acting in a multiplex mall. More specifically we try to analyse the influence of demographic, economic, social and cultural factors on the economic behaviour of a certain individual and then we try to link its behaviour with the general behaviour of the crowds of consumers in multiplex malls using agent-based crowd simulation techniques. We then run our model using Generalized Least Squares and Maximum Likelihood methods to come up with the most probable forecast estimations, regarding the agent's behaviour. Our model is indicative about the formation of consumers' spectre of choices in multiplex malls under the condition of predefined preferences and can be used as a guide for further research in this area.

Exits in order: How crowding affects particle lifetimes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Penington, Catherine J.; Simpson, Matthew J.; Baker, Ruth E.

2016-06-28

Diffusive processes are often represented using stochastic random walk frameworks. The amount of time taken for an individual in a random walk to intersect with an absorbing boundary is a fundamental property that is often referred to as the particle lifetime, or the first passage time. The mean lifetime of particles in a random walk model of diffusion is related to the amount of time required for the diffusive process to reach a steady state. Mathematical analysis describing the mean lifetime of particles in a standard model of diffusion without crowding is well known. However, the lifetime of agents inmore » a random walk with crowding has received much less attention. Since many applications of diffusion in biology and biophysics include crowding effects, here we study a discrete model of diffusion that incorporates crowding. Using simulations, we show that crowding has a dramatic effect on agent lifetimes, and we derive an approximate expression for the mean agent lifetime that includes crowding effects. Our expression matches simulation results very well, and highlights the importance of crowding effects that are sometimes overlooked.« less

Quantitative Agent Based Model of User Behavior in an Internet Discussion Forum

PubMed Central

Sobkowicz, Pawel

2013-01-01

The paper presents an agent based simulation of opinion evolution, based on a nonlinear emotion/information/opinion (E/I/O) individual dynamics, to an actual Internet discussion forum. The goal is to reproduce the results of two-year long observations and analyses of the user communication behavior and of the expressed opinions and emotions, via simulations using an agent based model. The model allowed to derive various characteristics of the forum, including the distribution of user activity and popularity (outdegree and indegree), the distribution of length of dialogs between the participants, their political sympathies and the emotional content and purpose of the comments. The parameters used in the model have intuitive meanings, and can be translated into psychological observables. PMID:24324606

Using an agent-based model to analyze the dynamic communication network of the immune response

PubMed Central

2011-01-01

Background The immune system behaves like a complex, dynamic network with interacting elements including leukocytes, cytokines, and chemokines. While the immune system is broadly distributed, leukocytes must communicate effectively to respond to a pathological challenge. The Basic Immune Simulator 2010 contains agents representing leukocytes and tissue cells, signals representing cytokines, chemokines, and pathogens, and virtual spaces representing organ tissue, lymphoid tissue, and blood. Agents interact dynamically in the compartments in response to infection of the virtual tissue. Agent behavior is imposed by logical rules derived from the scientific literature. The model captured the agent-to-agent contact history, and from this the network topology and the interactions resulting in successful versus failed viral clearance were identified. This model served to integrate existing knowledge and allowed us to examine the immune response from a novel perspective directed at exploiting complex dynamics, ultimately for the design of therapeutic interventions. Results Analyzing the evolution of agent-agent interactions at incremental time points from identical initial conditions revealed novel features of immune communication associated with successful and failed outcomes. There were fewer contacts between agents for simulations ending in viral elimination (win) versus persistent infection (loss), due to the removal of infected agents. However, early cellular interactions preceded successful clearance of infection. Specifically, more Dendritic Agent interactions with TCell and BCell Agents, and more BCell Agent interactions with TCell Agents early in the simulation were associated with the immune win outcome. The Dendritic Agents greatly influenced the outcome, confirming them as hub agents of the immune network. In addition, unexpectedly high frequencies of Dendritic Agent-self interactions occurred in the lymphoid compartment late in the loss outcomes. Conclusions An agent-based model capturing several key aspects of complex system dynamics was used to study the emergent properties of the immune response to viral infection. Specific patterns of interactions between leukocyte agents occurring early in the response significantly improved outcome. More interactions at later stages correlated with persistent inflammation and infection. These simulation experiments highlight the importance of commonly overlooked aspects of the immune response and provide insight into these processes at a resolution level exceeding the capabilities of current laboratory technologies. PMID:21247471

Multiagent distributed watershed management

NASA Astrophysics Data System (ADS)

Giuliani, M.; Castelletti, A.; Amigoni, F.; Cai, X.

2012-04-01

Deregulation and democratization of water along with increasing environmental awareness are challenging integrated water resources planning and management worldwide. The traditional centralized approach to water management, as described in much of water resources literature, is often unfeasible in most of the modern social and institutional contexts. Thus it should be reconsidered from a more realistic and distributed perspective, in order to account for the presence of multiple and often independent Decision Makers (DMs) and many conflicting stakeholders. Game theory based approaches are often used to study these situations of conflict (Madani, 2010), but they are limited to a descriptive perspective. Multiagent systems (see Wooldridge, 2009), instead, seem to be a more suitable paradigm because they naturally allow to represent a set of self-interested agents (DMs and/or stakeholders) acting in a distributed decision process at the agent level, resulting in a promising compromise alternative between the ideal centralized solution and the actual uncoordinated practices. Casting a water management problem in a multiagent framework allows to exploit the techniques and methods that are already available in this field for solving distributed optimization problems. In particular, in Distributed Constraint Satisfaction Problems (DCSP, see Yokoo et al., 2000), each agent controls some variables according to his own utility function but has to satisfy inter-agent constraints; while in Distributed Constraint Optimization Problems (DCOP, see Modi et al., 2005), the problem is generalized by introducing a global objective function to be optimized that requires a coordination mechanism between the agents. In this work, we apply a DCSP-DCOP based approach to model a steady state hypothetical watershed management problem (Yang et al., 2009), involving several active human agents (i.e. agents who make decisions) and reactive ecological agents (i.e. agents representing environmental interests). Different scenarios of distributed management are simulated, i.e. a situation where all the agents act independently, a situation in which a global coordination takes place and in-between solutions. The solutions are compared with the ones presented in Yang et al. (2009), aiming to present more general multiagent approaches to solve distributed management problems.

Agent-based model for rural-urban migration: A dynamic consideration

NASA Astrophysics Data System (ADS)

Cai, Ning; Ma, Hai-Ying; Khan, M. Junaid

2015-10-01

This paper develops a dynamic agent-based model for rural-urban migration, based on the previous relevant works. The model conforms to the typical dynamic linear multi-agent systems model concerned extensively in systems science, in which the communication network is formulated as a digraph. Simulations reveal that consensus of certain variable could be harmful to the overall stability and should be avoided.

Aviation Safety: Modeling and Analyzing Complex Interactions between Humans and Automated Systems

NASA Technical Reports Server (NTRS)

Rungta, Neha; Brat, Guillaume; Clancey, William J.; Linde, Charlotte; Raimondi, Franco; Seah, Chin; Shafto, Michael

2013-01-01

The on-going transformation from the current US Air Traffic System (ATS) to the Next Generation Air Traffic System (NextGen) will force the introduction of new automated systems and most likely will cause automation to migrate from ground to air. This will yield new function allocations between humans and automation and therefore change the roles and responsibilities in the ATS. Yet, safety in NextGen is required to be at least as good as in the current system. We therefore need techniques to evaluate the safety of the interactions between humans and automation. We think that current human factor studies and simulation-based techniques will fall short in front of the ATS complexity, and that we need to add more automated techniques to simulations, such as model checking, which offers exhaustive coverage of the non-deterministic behaviors in nominal and off-nominal scenarios. In this work, we present a verification approach based both on simulations and on model checking for evaluating the roles and responsibilities of humans and automation. Models are created using Brahms (a multi-agent framework) and we show that the traditional Brahms simulations can be integrated with automated exploration techniques based on model checking, thus offering a complete exploration of the behavioral space of the scenario. Our formal analysis supports the notion of beliefs and probabilities to reason about human behavior. We demonstrate the technique with the Ueberligen accident since it exemplifies authority problems when receiving conflicting advices from human and automated systems.

Understanding the Dynamics of Socio-Hydrological Environment: a Conceptual Framework

NASA Astrophysics Data System (ADS)

Woyessa, Y.; Welderufael, W.; Edossa, D.

2011-12-01

Human actions affect ecological systems and the services they provide through various activities, such as land use, water use, pollution and climate change. Climate change is perhaps one of the most important sustainable development challenges that threaten to undo many of the development efforts being made to reach the targets set for the Millennium Development Goals. Understanding the change of ecosystems under different scenarios of climate and biophysical conditions could assist in bringing the issue of ecosystem services into decision making process. Similarly, the impacts of land use change on ecosystems and biodiversity have received considerable attention from ecologists and hydrologists alike. Land use change in a catchment can impact on water supply by altering hydrological processes, such as infiltration, groundwater recharge, base flow and direct runoff. In the past a variety of models were used for predicting land-use changes. Recently the focus has shifted away from using mathematically oriented models to agent-based modelling (ABM) approach to simulate land use scenarios. A conceptual framework is being developed which integrates climate change scenarios and the human dimension of land use decision into a hydrological model in order to assess its impacts on the socio-hydrological dynamics of a river basin. The following figures present the framework for the analysis and modelling of the socio-hydrological dynamics. Keywords: climate change, land use, river basin

An Example-Based Brain MRI Simulation Framework.

PubMed

He, Qing; Roy, Snehashis; Jog, Amod; Pham, Dzung L

2015-02-21

The simulation of magnetic resonance (MR) images plays an important role in the validation of image analysis algorithms such as image segmentation, due to lack of sufficient ground truth in real MR images. Previous work on MRI simulation has focused on explicitly modeling the MR image formation process. However, because of the overwhelming complexity of MR acquisition these simulations must involve simplifications and approximations that can result in visually unrealistic simulated images. In this work, we describe an example-based simulation framework, which uses an "atlas" consisting of an MR image and its anatomical models derived from the hard segmentation. The relationships between the MR image intensities and its anatomical models are learned using a patch-based regression that implicitly models the physics of the MR image formation. Given the anatomical models of a new brain, a new MR image can be simulated using the learned regression. This approach has been extended to also simulate intensity inhomogeneity artifacts based on the statistical model of training data. Results show that the example based MRI simulation method is capable of simulating different image contrasts and is robust to different choices of atlas. The simulated images resemble real MR images more than simulations produced by a physics-based model.

N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering

PubMed Central

Ullah, Farman; Lee, Sungchang

2014-01-01

This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements. PMID:25152921

Assessment of mass detection performance in contrast enhanced digital mammography

NASA Astrophysics Data System (ADS)

Carton, Ann-Katherine; de Carvalho, Pablo M.; Li, Zhijin; Dromain, Clarisse; Muller, Serge

2015-03-01

We address the detectability of contrast-agent enhancing masses for contrast-agent enhanced spectral mammography (CESM), a dual-energy technique providing functional projection images of breast tissue perfusion and vascularity using simulated CESM images. First, the realism of simulated CESM images from anthropomorphic breast software phantoms generated with a software X-ray imaging platform was validated. Breast texture was characterized by power-law coefficients calculated in data sets of real clinical and simulated images. We also performed a 2-alternative forced choice (2-AFC) psychophysical experiment whereby simulated and real images were presented side-by-side to an experienced radiologist to test if real images could be distinguished from the simulated images. It was found that texture in our simulated CESM images has a fairly realistic appearance. Next, the relative performance of human readers and previously developed mathematical observers was assessed for the detection of iodine-enhancing mass lesions containing different contrast agent concentrations. A four alternative-forced-choice (4 AFC) task was designed; the task for the model and human observer was to detect which one of the four simulated DE recombined images contained an iodineenhancing mass. Our results showed that the NPW and NPWE models largely outperform human performance. After introduction of an internal noise component, both observers approached human performance. The CHO observer performs slightly worse than the average human observer. There is still work to be done in improving model observers as predictors of human-observer performance. Larger trials could also improve our test statistics. We hope that in the future, this framework of software breast phantoms, virtual image acquisition and processing, and mathematical observers can be beneficial to optimize CESM imaging techniques.

Knowledge Management in Role Based Agents

NASA Astrophysics Data System (ADS)

Kır, Hüseyin; Ekinci, Erdem Eser; Dikenelli, Oguz

In multi-agent system literature, the role concept is getting increasingly researched to provide an abstraction to scope beliefs, norms, goals of agents and to shape relationships of the agents in the organization. In this research, we propose a knowledgebase architecture to increase applicability of roles in MAS domain by drawing inspiration from the self concept in the role theory of sociology. The proposed knowledgebase architecture has granulated structure that is dynamically organized according to the agent's identification in a social environment. Thanks to this dynamic structure, agents are enabled to work on consistent knowledge in spite of inevitable conflicts between roles and the agent. The knowledgebase architecture is also implemented and incorporated into the SEAGENT multi-agent system development framework.

A complex systems approach to evaluate HIV prevention in metropolitan areas: preliminary implications for combination intervention strategies.

PubMed

Marshall, Brandon D L; Paczkowski, Magdalena M; Seemann, Lars; Tempalski, Barbara; Pouget, Enrique R; Galea, Sandro; Friedman, Samuel R

2012-01-01

HIV transmission among injecting and non-injecting drug users (IDU, NIDU) is a significant public health problem. Continuing propagation in endemic settings and emerging regional outbreaks have indicated the need for comprehensive and coordinated HIV prevention. We describe the development of a conceptual framework and calibration of an agent-based model (ABM) to examine how combinations of interventions may reduce and potentially eliminate HIV transmission among drug-using populations. A multidisciplinary team of researchers from epidemiology, sociology, geography, and mathematics developed a conceptual framework based on prior ethnographic and epidemiologic research. An ABM was constructed and calibrated through an iterative design and verification process. In the model, "agents" represent IDU, NIDU, and non-drug users who interact with each other and within risk networks, engaging in sexual and, for IDUs, injection-related risk behavior over time. Agents also interact with simulated HIV prevention interventions (e.g., syringe exchange programs, substance abuse treatment, HIV testing) and initiate antiretroviral treatment (ART) in a stochastic manner. The model was constructed to represent the New York metropolitan statistical area (MSA) population, and calibrated by comparing output trajectories for various outcomes (e.g., IDU/NIDU prevalence, HIV prevalence and incidence) against previously validated MSA-level data. The model closely approximated HIV trajectories in IDU and NIDU observed in New York City between 1992 and 2002, including a linear decrease in HIV prevalence among IDUs. Exploratory results are consistent with empirical studies demonstrating that the effectiveness of a combination of interventions, including syringe exchange expansion and ART provision, dramatically reduced HIV prevalence among IDUs during this time period. Complex systems models of adaptive HIV transmission dynamics can be used to identify potential collective benefits of hypothetical combination prevention interventions. Future work will seek to inform novel strategies that may lead to more effective and equitable HIV prevention strategies for drug-using populations.

An agent-based modelling framework to explore the role of social media and stubborn people on evacuation rates during flooding events

NASA Astrophysics Data System (ADS)

Du, E.; Cai, X.; Minsker, B. S.; Sun, Z.

2017-12-01

Flood warnings from various information sources are important for individuals to make evacuation decisions during a flood event. In this study, we develop a general opinion dynamics model to simulate how individuals update their flood hazard awareness when exposed to multiple information sources, including global broadcast, social media, and observations of neighbors' actions. The opinion dynamics model is coupled with a traffic model to simulate the evacuation processes of a residential community with a given transportation network. Through various scenarios, we investigate how social media affect the opinion dynamics and evacuation processes. We find that stronger social media can make evacuation processes more sensitive to the change of global broadcast and neighbor observations, and thus, impose larger uncertainty on evacuation rates (i.e., a large range of evacuation rates corresponding to sources of information). For instance, evacuation rates are lower when social media become more influential and individuals have less trust in global broadcast. Stubborn individuals can significantly affect the opinion dynamics and reduce evacuation rates. In addition, evacuation rates respond to the percentage of stubborn agents in a non-linear manner, i.e., above a threshold, the impact of stubborn agents will be intensified by stronger social media. These results highlight the role of social media in flood evacuation processes and the need to monitor social media so that misinformation can be corrected in a timely manner. The joint impacts of social media, quality of flood warnings and transportation capacity on evacuation rates are also discussed.

A systems approach for tumor pharmacokinetics.

PubMed

Thurber, Greg Michael; Weissleder, Ralph

2011-01-01

Recent advances in genome inspired target discovery, small molecule screens, development of biological and nanotechnology have led to the introduction of a myriad of new differently sized agents into the clinic. The differences in small and large molecule delivery are becoming increasingly important in combination therapies as well as the use of drugs that modify the physiology of tumors such as anti-angiogenic treatment. The complexity of targeting has led to the development of mathematical models to facilitate understanding, but unfortunately, these studies are often only applicable to a particular molecule, making pharmacokinetic comparisons difficult. Here we develop and describe a framework for categorizing primary pharmacokinetics of drugs in tumors. For modeling purposes, we define drugs not by their mechanism of action but rather their rate-limiting step of delivery. Our simulations account for variations in perfusion, vascularization, interstitial transport, and non-linear local binding and metabolism. Based on a comparison of the fundamental rates determining uptake, drugs were classified into four categories depending on whether uptake is limited by blood flow, extravasation, interstitial diffusion, or local binding and metabolism. Simulations comparing small molecule versus macromolecular drugs show a sharp difference in distribution, which has implications for multi-drug therapies. The tissue-level distribution differs widely in tumors for small molecules versus macromolecular biologic drugs, and this should be considered in the design of agents and treatments. An example using antibodies in mouse xenografts illustrates the different in vivo behavior. This type of transport analysis can be used to aid in model development, experimental data analysis, and imaging and therapeutic agent design.

Game theoretic sensor management for target tracking

NASA Astrophysics Data System (ADS)

Shen, Dan; Chen, Genshe; Blasch, Erik; Pham, Khanh; Douville, Philip; Yang, Chun; Kadar, Ivan

2010-04-01

This paper develops and evaluates a game-theoretic approach to distributed sensor-network management for target tracking via sensor-based negotiation. We present a distributed sensor-based negotiation game model for sensor management for multi-sensor multi-target tacking situations. In our negotiation framework, each negotiation agent represents a sensor and each sensor maximizes their utility using a game approach. The greediness of each sensor is limited by the fact that the sensor-to-target assignment efficiency will decrease if too many sensor resources are assigned to a same target. It is similar to the market concept in real world, such as agreements between buyers and sellers in an auction market. Sensors are willing to switch targets so that they can obtain their highest utility and the most efficient way of applying their resources. Our sub-game perfect equilibrium-based negotiation strategies dynamically and distributedly assign sensors to targets. Numerical simulations are performed to demonstrate our sensor-based negotiation approach for distributed sensor management.

Environmental potentials of policy instruments to mitigate nutrient emissions in Chinese livestock production.

PubMed

Zheng, Chaohui; Liu, Yi; Bluemling, Bettina; Mol, Arthur P J; Chen, Jining

2015-01-01

To minimize negative environmental impact of livestock production, policy-makers face a challenge to design and implement more effective policy instruments for livestock farmers at different scales. This research builds an assessment framework on the basis of an agent-based model, named ANEM, to explore nutrient mitigation potentials of five policy instruments, using pig production in Zhongjiang county, southwest China, as the empirical filling. The effects of different policy scenarios are simulated and compared using four indicators and differentiating between small, medium and large scale pig farms. Technology standards, biogas subsidies and information provisioning prove to be the most effective policies, while pollution fees and manure markets fail to environmentally improve manure management in pig livestock farming. Medium-scale farms are the more relevant scale category for a more environmentally sound development of Chinese livestock production. A number of policy recommendations are formulated as conclusion, as well as some limitations and prospects of the simulations are discussed. Copyright © 2014 Elsevier B.V. All rights reserved.

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

PubMed Central

Walpole, J.; Chappell, J.C.; Cluceru, J.G.; Mac Gabhann, F.; Bautch, V.L.; Peirce, S. M.

2015-01-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods. PMID:26158406

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.

PubMed

Walpole, J; Chappell, J C; Cluceru, J G; Mac Gabhann, F; Bautch, V L; Peirce, S M

2015-09-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.

A water market simulator considering pair-wise trades between agents

NASA Astrophysics Data System (ADS)

Huskova, I.; Erfani, T.; Harou, J. J.

2012-04-01

In many basins in England no further water abstraction licences are available. Trading water between water rights holders has been recognized as a potentially effective and economically efficient strategy to mitigate increasing scarcity. A screening tool that could assess the potential for trade through realistic simulation of individual water rights holders would help assess the solution's potential contribution to local water management. We propose an optimisation-driven water market simulator that predicts pair-wise trade in a catchment and represents its interaction with natural hydrology and engineered infrastructure. A model is used to emulate licence-holders' willingness to engage in short-term trade transactions. In their simplest form agents are represented using an economic benefit function. The working hypothesis is that trading behaviour can be partially predicted based on differences in marginal values of water over space and time and estimates of transaction costs on pair-wise trades. We discuss the further possibility of embedding rules, norms and preferences of the different water user sectors to more realistically represent the behaviours, motives and constraints of individual licence holders. The potential benefits and limitations of such a social simulation (agent-based) approach is contrasted with our simulator where agents are driven by economic optimization. A case study based on the Dove River Basin (UK) demonstrates model inputs and outputs. The ability of the model to suggest impacts of water rights policy reforms on trading is discussed.

Metascalable molecular dynamics simulation of nano-mechano-chemistry

NASA Astrophysics Data System (ADS)

Shimojo, F.; Kalia, R. K.; Nakano, A.; Nomura, K.; Vashishta, P.

2008-07-01

We have developed a metascalable (or 'design once, scale on new architectures') parallel application-development framework for first-principles based simulations of nano-mechano-chemical processes on emerging petaflops architectures based on spatiotemporal data locality principles. The framework consists of (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms, (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these scalable algorithms onto hardware. The EDC-STEP-HCD framework exposes and expresses maximal concurrency and data locality, thereby achieving parallel efficiency as high as 0.99 for 1.59-billion-atom reactive force field molecular dynamics (MD) and 17.7-million-atom (1.56 trillion electronic degrees of freedom) quantum mechanical (QM) MD in the framework of the density functional theory (DFT) on adaptive multigrids, in addition to 201-billion-atom nonreactive MD, on 196 608 IBM BlueGene/L processors. We have also used the framework for automated execution of adaptive hybrid DFT/MD simulation on a grid of six supercomputers in the US and Japan, in which the number of processors changed dynamically on demand and tasks were migrated according to unexpected faults. The paper presents the application of the framework to the study of nanoenergetic materials: (1) combustion of an Al/Fe2O3 thermite and (2) shock initiation and reactive nanojets at a void in an energetic crystal.

A framework for modeling scenario-based barrier island storm impacts

USGS Publications Warehouse

Mickey, Rangley; Long, Joseph W.; Dalyander, P. Soupy; Plant, Nathaniel G.; Thompson, David M.

2018-01-01

Methods for investigating the vulnerability of existing or proposed coastal features to storm impacts often rely on simplified parametric models or one-dimensional process-based modeling studies that focus on changes to a profile across a dune or barrier island. These simple studies tend to neglect the impacts to curvilinear or alongshore varying island planforms, influence of non-uniform nearshore hydrodynamics and sediment transport, irregular morphology of the offshore bathymetry, and impacts from low magnitude wave events (e.g. cold fronts). Presented here is a framework for simulating regionally specific, low and high magnitude scenario-based storm impacts to assess the alongshore variable vulnerabilities of a coastal feature. Storm scenarios based on historic hydrodynamic conditions were derived and simulated using the process-based morphologic evolution model XBeach. Model results show that the scenarios predicted similar patterns of erosion and overwash when compared to observed qualitative morphologic changes from recent storm events that were not included in the dataset used to build the scenarios. The framework model simulations were capable of predicting specific areas of vulnerability in the existing feature and the results illustrate how this storm vulnerability simulation framework could be used as a tool to help inform the decision-making process for scientists, engineers, and stakeholders involved in coastal zone management or restoration projects.

Understanding Air Transportation Market Dynamics Using a Search Algorithm for Calibrating Travel Demand and Price

NASA Technical Reports Server (NTRS)

Kumar, Vivek; Horio, Brant M.; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson M.

2015-01-01

This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.

Real-Time Agent-Based Modeling Simulation with in-situ Visualization of Complex Biological Systems: A Case Study on Vocal Fold Inflammation and Healing.

PubMed

Seekhao, Nuttiiya; Shung, Caroline; JaJa, Joseph; Mongeau, Luc; Li-Jessen, Nicole Y K

2016-05-01

We present an efficient and scalable scheme for implementing agent-based modeling (ABM) simulation with In Situ visualization of large complex systems on heterogeneous computing platforms. The scheme is designed to make optimal use of the resources available on a heterogeneous platform consisting of a multicore CPU and a GPU, resulting in minimal to no resource idle time. Furthermore, the scheme was implemented under a client-server paradigm that enables remote users to visualize and analyze simulation data as it is being generated at each time step of the model. Performance of a simulation case study of vocal fold inflammation and wound healing with 3.8 million agents shows 35× and 7× speedup in execution time over single-core and multi-core CPU respectively. Each iteration of the model took less than 200 ms to simulate, visualize and send the results to the client. This enables users to monitor the simulation in real-time and modify its course as needed.

Who messed up my lake?

EPA Science Inventory

Initial results from a lake-wide agent based simulation releasing virtual drifters from multiple tributaries over time. We examine the use of agent based modeling to break down the sources contributing to the composition of nearshore waters. Knowing that flow is highly biased in ...

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

PubMed

Bennett, Casey C; Hauser, Kris

2013-01-01

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine. Copyright © 2012 Elsevier B.V. All rights reserved.

Distributed environmental control

NASA Technical Reports Server (NTRS)

Cleveland, Gary A.

1992-01-01

We present an architecture of distributed, independent control agents designed to work with the Computer Aided System Engineering and Analysis (CASE/A) simulation tool. CASE/A simulates behavior of Environmental Control and Life Support Systems (ECLSS). We describe a lattice of agents capable of distributed sensing and overcoming certain sensor and effector failures. We address how the architecture can achieve the coordinating functions of a hierarchical command structure while maintaining the robustness and flexibility of independent agents. These agents work between the time steps of the CASE/A simulation tool to arrive at command decisions based on the state variables maintained by CASE/A. Control is evaluated according to both effectiveness (e.g., how well temperature was maintained) and resource utilization (the amount of power and materials used).

Gathering Validity Evidence for Surgical Simulation: A Systematic Review.

PubMed

Borgersen, Nanna Jo; Naur, Therese M H; Sørensen, Stine M D; Bjerrum, Flemming; Konge, Lars; Subhi, Yousif; Thomsen, Ann Sofia S

2018-06-01

To identify current trends in the use of validity frameworks in surgical simulation, to provide an overview of the evidence behind the assessment of technical skills in all surgical specialties, and to present recommendations and guidelines for future validity studies. Validity evidence for assessment tools used in the evaluation of surgical performance is of paramount importance to ensure valid and reliable assessment of skills. We systematically reviewed the literature by searching 5 databases (PubMed, EMBASE, Web of Science, PsycINFO, and the Cochrane Library) for studies published from January 1, 2008, to July 10, 2017. We included original studies evaluating simulation-based assessments of health professionals in surgical specialties and extracted data on surgical specialty, simulator modality, participant characteristics, and the validity framework used. Data were synthesized qualitatively. We identified 498 studies with a total of 18,312 participants. Publications involving validity assessments in surgical simulation more than doubled from 2008 to 2010 (∼30 studies/year) to 2014 to 2016 (∼70 to 90 studies/year). Only 6.6% of the studies used the recommended contemporary validity framework (Messick). The majority of studies used outdated frameworks such as face validity. Significant differences were identified across surgical specialties. The evaluated assessment tools were mostly inanimate or virtual reality simulation models. An increasing number of studies have gathered validity evidence for simulation-based assessments in surgical specialties, but the use of outdated frameworks remains common. To address the current practice, this paper presents guidelines on how to use the contemporary validity framework when designing validity studies.

A framework using cluster-based hybrid network architecture for collaborative virtual surgery.

PubMed

Qin, Jing; Choi, Kup-Sze; Poon, Wai-Sang; Heng, Pheng-Ann

2009-12-01

Research on collaborative virtual environments (CVEs) opens the opportunity for simulating the cooperative work in surgical operations. It is however a challenging task to implement a high performance collaborative surgical simulation system because of the difficulty in maintaining state consistency with minimum network latencies, especially when sophisticated deformable models and haptics are involved. In this paper, an integrated framework using cluster-based hybrid network architecture is proposed to support collaborative virtual surgery. Multicast transmission is employed to transmit updated information among participants in order to reduce network latencies, while system consistency is maintained by an administrative server. Reliable multicast is implemented using distributed message acknowledgment based on cluster cooperation and sliding window technique. The robustness of the framework is guaranteed by the failure detection chain which enables smooth transition when participants join and leave the collaboration, including normal and involuntary leaving. Communication overhead is further reduced by implementing a number of management approaches such as computational policies and collaborative mechanisms. The feasibility of the proposed framework is demonstrated by successfully extending an existing standalone orthopedic surgery trainer into a collaborative simulation system. A series of experiments have been conducted to evaluate the system performance. The results demonstrate that the proposed framework is capable of supporting collaborative surgical simulation.

Modelling biological behaviours with the unified modelling language: an immunological case study and critique.

PubMed

Read, Mark; Andrews, Paul S; Timmis, Jon; Kumar, Vipin

2014-10-06

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.

Modelling biological behaviours with the unified modelling language: an immunological case study and critique

PubMed Central

Read, Mark; Andrews, Paul S.; Timmis, Jon; Kumar, Vipin

2014-01-01

We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology. PMID:25142524

Next Generation Simulation Framework for Robotic and Human Space Missions

NASA Technical Reports Server (NTRS)

Cameron, Jonathan M.; Balaram, J.; Jain, Abhinandan; Kuo, Calvin; Lim, Christopher; Myint, Steven

2012-01-01

The Dartslab team at NASA's Jet Propulsion Laboratory (JPL) has a long history of developing physics-based simulations based on the Darts/Dshell simulation framework that have been used to simulate many planetary robotic missions, such as the Cassini spacecraft and the rovers that are currently driving on Mars. Recent collaboration efforts between the Dartslab team at JPL and the Mission Operations Directorate (MOD) at NASA Johnson Space Center (JSC) have led to significant enhancements to the Dartslab DSENDS (Dynamics Simulator for Entry, Descent and Surface landing) software framework. The new version of DSENDS is now being used for new planetary mission simulations at JPL. JSC is using DSENDS as the foundation for a suite of software known as COMPASS (Core Operations, Mission Planning, and Analysis Spacecraft Simulation) that is the basis for their new human space mission simulations and analysis. In this paper, we will describe the collaborative process with the JPL Dartslab and the JSC MOD team that resulted in the redesign and enhancement of the DSENDS software. We will outline the improvements in DSENDS that simplify creation of new high-fidelity robotic/spacecraft simulations. We will illustrate how DSENDS simulations are assembled and show results from several mission simulations.

A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission

PubMed Central

Parker, Jon; Epstein, Joshua M.

2013-01-01

The Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of several billion agents. It is unprecedented in its scale, its speed, and its use of Java. Solutions to multiple challenges inherent in distributing massive agent-based models are presented. Communication, synchronization, and memory usage are among the topics covered in detail. The memory usage discussion is Java specific. However, the communication and synchronization discussions apply broadly. We provide benchmarks illustrating the GSAM’s speed and scalability. PMID:24465120

Perception Modelling of Visitors in Vargas Museum Using Agent-Based Simulation and Visibility Analysis

NASA Astrophysics Data System (ADS)

Carcellar, B. G., III

2017-10-01

Museum exhibit management is one of the usual undertakings of museum facilitators. Art works must be strategically placed to achieve maximum viewing from the visitors. The positioning of the artworks also highly influences the quality of experience of the visitors. One solution in such problems is to utilize GIS and Agent-Based Modelling (ABM). In ABM, persistent interacting objects are modelled as agents. These agents are given attributes and behaviors that describe their properties as well as their motion. In this study, ABM approach that incorporates GIS is utilized to perform analyticcal assessment on the placement of the artworks in the Vargas Museum. GIS serves as the backbone for the spatial aspect of the simulation such as the placement of the artwork exhibits, as well as possible obstructions to perception such as the columns, walls, and panel boards. Visibility Analysis is also done to the model in GIS to assess the overall visibility of the artworks. The ABM is done using the initial GIS outputs and GAMA, an open source ABM software. Visitors are modelled as agents, moving inside the museum following a specific decision tree. The simulation is done in three use cases: the 10 %, 20 %, and 30 % chance of having a visitor in the next minute. For the case of the said museum, the 10 % chance is determined to be the closest simulation case to the actual and the recommended minimum time to achieve a maximum artwork perception is 1 hour and 40 minutes. Initial assessment of the results shows that even after 3 hours of simulation, small parts of the exhibit show lack of viewers, due to its distance from the entrance. A more detailed decision tree for the visitor agents can be incorporated to have a more realistic simulation.

Resolving Multi-Stakeholder Robustness Asymmetries in Coupled Agricultural and Urban Systems

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea; Reed, Patrick

2016-04-01

The evolving pressures from a changing climate and society are increasingly motivating decision support frameworks that consider the robustness of management actions across many possible futures. Focusing on robustness is helpful for investigating key vulnerabilities within current water systems and for identifying potential tradeoffs across candidate adaptation responses. To date, most robustness studies assume a social planner perspective by evaluating highly aggregated measures of system performance. This aggregate treatment of stakeholders does not explore the equity or intrinsic multi-stakeholder conflicts implicit to the system-wide measures of performance benefits and costs. The commonly present heterogeneity across complex management interests, however, may produce strong asymmetries for alternative adaptation options, designed to satisfy system-level targets. In this work, we advance traditional robustness decision frameworks by replacing the centralized social planner with a bottom-up, agent-based approach, where stakeholders are modeled as individuals, and represented as potentially self-interested agents. This agent-based model enables a more explicit exploration of the potential inequities and asymmetries in the distribution of the system-wide benefit. The approach is demonstrated by exploring the potential conflicts between urban flooding and agricultural production in the Lake Como system (Italy). Lake Como is a regulated lake that is operated to supply water to the downstream agricultural district (Muzza as the pilot study area in this work) composed of a set of farmers with heterogeneous characteristics in terms of water allocation, cropping patterns, and land properties. Supplying water to farmers increases the risk of floods along the lakeshore and therefore the system is operated based on the tradeoff between these two objectives. We generated an ensemble of co-varying climate and socio-economic conditions and evaluated the robustness of the current Lake Como system management as well as of possible adaptation options (e.g., improved irrigation efficiency or changes in the dam operating rules). Numerical results show that crops prices and costs are the main drivers of the simulated system failures when evaluated in terms of system-level expected profitability. Analysis conducted at the farmer-agent scale highlights alternatively that temperature and inflows are the critical drivers leading to failures. Finally, we show that the robustness of the considered adaptation options varies spatially, strongly influenced by stakeholders' context, the metrics used to define success, and the assumed preferences for reservoir operations in balancing urban flooding and agricultural productivity.

Sensitivity Analysis in Agent-Based Models of Socio-Ecological Systems: An Example in Agricultural Land Conservation for Lake Water Quality Improvement

NASA Astrophysics Data System (ADS)

Ligmann-Zielinska, A.; Kramer, D. B.; Spence Cheruvelil, K.; Soranno, P.

2012-12-01

Socio-ecological systems are dynamic and nonlinear. To account for this complexity, we employ agent-based models (ABMs) to study macro-scale phenomena resulting from micro-scale interactions among system components. Because ABMs typically have many parameters, it is challenging to identify which parameters contribute to the emerging macro-scale patterns. In this paper, we address the following question: What is the extent of participation in agricultural land conservation programs given heterogeneous landscape, economic, social, and individual decision making criteria in complex lakesheds? To answer this question, we: [1] built an ABM for our model system; [2] simulated land use change resulting from agent decision making, [3] estimated the uncertainty of the model output, decomposed it and apportioned it to each of the parameters in the model. Our model system is a freshwater socio-ecological system - that of farmland and lake water quality within a region containing a large number of lakes and high proportions of agricultural lands. Our study focuses on examining how agricultural land conversion from active to fallow reduces freshwater nutrient loading and improves water quality. Consequently, our ABM is composed of farmer agents who make decisions related to participation in a government-sponsored Conservation Reserve Program (CRP) managed by the Farm Service Agency (FSA). We also include an FSA agent, who selects enrollment offers made by farmers and announces the signup results leading to land use change. The model is executed in a Monte Carlo simulation framework to generate a distribution of maps of fallow lands that are used for calculating nutrient loading to lakes. What follows is a variance-based sensitivity analysis of the results. We compute sensitivity indices for individual parameters and their combinations, allowing for identification of the most influential as well as the insignificant inputs. In the case study, we observe that farmland conservation is first and foremost driven by the FSA signup choices. Environmental criteria used in FSA offer selection play a secondary role in farmland-to-fallow-land conversion. Farmer decision making is mainly influenced by the willingness to reduce the potential annual rental payments. As the case study demonstrates, our approach leads to ABM simplification without the loss of outcome variability. It also shows how to represent the magnitude of ABM complexity and isolate the effects of the interconnected explanatory variables on the simulated emergent phenomena. More importantly, the results of our research indicate that some of the parameters exert influence on model outcomes only if analyzed in combination with other parameters. Without evaluating the interaction effects among inputs, we risk losing important functional relationships among ABM components and, consequently, we potentially reduce its explanatory power.

A Component-Based Extension Framework for Large-Scale Parallel Simulations in NEURON

PubMed Central

King, James G.; Hines, Michael; Hill, Sean; Goodman, Philip H.; Markram, Henry; Schürmann, Felix

2008-01-01

As neuronal simulations approach larger scales with increasing levels of detail, the neurosimulator software represents only a part of a chain of tools ranging from setup, simulation, interaction with virtual environments to analysis and visualizations. Previously published approaches to abstracting simulator engines have not received wide-spread acceptance, which in part may be to the fact that they tried to address the challenge of solving the model specification problem. Here, we present an approach that uses a neurosimulator, in this case NEURON, to describe and instantiate the network model in the simulator's native model language but then replaces the main integration loop with its own. Existing parallel network models are easily adopted to run in the presented framework. The presented approach is thus an extension to NEURON but uses a component-based architecture to allow for replaceable spike exchange components and pluggable components for monitoring, analysis, or control that can run in this framework alongside with the simulation. PMID:19430597

Micromotor-based on-off fluorescence detection of sarin and soman simulants.

PubMed

Singh, Virendra V; Kaufmann, Kevin; Orozco, Jahir; Li, Jinxing; Galarnyk, Michael; Arya, Gaurav; Wang, Joseph

2015-06-30

Self-propelled micromotor-based fluorescent "On-Off" detection of nerve agents is described. The motion-based assay utilizes Si/Pt Janus micromotors coated with fluoresceinamine toward real-time "on-the-fly" field detection of sarin and soman simulants.

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

PubMed

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

A Context-Aware Self-Adaptive Fractal Based Generalized Pedagogical Agent Framework for Mobile Learning

ERIC Educational Resources Information Center

Boulehouache, Soufiane; Maamri, Ramdane; Sahnoun, Zaidi

2015-01-01

The Pedagogical Agents (PAs) for Mobile Learning (m-learning) must be able not only to adapt the teaching to the learner knowledge level and profile but also to ensure the pedagogical efficiency within unpredictable changing runtime contexts. Therefore, to deal with this issue, this paper proposes a Context-aware Self-Adaptive Fractal Component…

ModelforAnalyzing Human Communication Network Based onAgent-Based Simulation

NASA Astrophysics Data System (ADS)

Matsuyama, Shinako; Terano, Takao

This paper discusses dynamic properties of human communications networks, which appears as a result of informationexchanges among people. We propose agent-based simulation (ABS) to examine implicit mechanisms behind the dynamics. The ABS enables us to reveal the characteristics and the differences of the networks regarding the specific communicationgroups. We perform experiments on the ABS with activity data from questionnaires survey and with virtual data which isdifferent from the activity data. We compare the difference between them and show the effectiveness of the ABS through theexperiments.

Consentaneous Agent-Based and Stochastic Model of the Financial Markets

PubMed Central

Gontis, Vygintas; Kononovicius, Aleksejus

2014-01-01

We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial markets, is very ambiguous emphasizes the exceptional value of well defined analytically tractable agent systems. Herding as one of the behavior peculiarities considered in the behavioral finance is the main property of the agent interactions we deal with in this contribution. Looking for the consentaneous agent-based and macroscopic approach we combine two origins of the noise: exogenous one, related to the information flow, and endogenous one, arising form the complex stochastic dynamics of agents. As a result we propose a three state agent-based herding model of the financial markets. From this agent-based model we derive a set of stochastic differential equations, which describes underlying macroscopic dynamics of agent population and log price in the financial markets. The obtained solution is then subjected to the exogenous noise, which shapes instantaneous return fluctuations. We test both Gaussian and q-Gaussian noise as a source of the short term fluctuations. The resulting model of the return in the financial markets with the same set of parameters reproduces empirical probability and spectral densities of absolute return observed in New York, Warsaw and NASDAQ OMX Vilnius Stock Exchanges. Our result confirms the prevalent idea in behavioral finance that herding interactions may be dominant over agent rationality and contribute towards bubble formation. PMID:25029364

A Combined Theoretical and Experimental Study for Silver Electroplating

PubMed Central

Liu, Anmin; Ren, Xuefeng; An, Maozhong; Zhang, Jinqiu; Yang, Peixia; Wang, Bo; Zhu, Yongming; Wang, Chong

2014-01-01

A novel method combined theoretical and experimental study for environmental friendly silver electroplating was introduced. Quantum chemical calculations and molecular dynamic (MD) simulations were employed for predicting the behaviour and function of the complexing agents. Electronic properties, orbital information, and single point energies of the 5,5-dimethylhydantoin (DMH), nicotinic acid (NA), as well as their silver(I)-complexes were provided by quantum chemical calculations based on density functional theory (DFT). Adsorption behaviors of the agents on copper and silver surfaces were investigated using MD simulations. Basing on the data of quantum chemical calculations and MD simulations, we believed that DMH and NA could be the promising complexing agents for silver electroplating. The experimental results, including of electrochemical measurement and silver electroplating, further confirmed the above prediction. This efficient and versatile method thus opens a new window to study or design complexing agents for generalized metal electroplating and will vigorously promote the level of this research region. PMID:24452389

A data-driven dynamics simulation framework for railway vehicles

NASA Astrophysics Data System (ADS)

Nie, Yinyu; Tang, Zhao; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2018-03-01

The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy.

Consensus pursuit of heterogeneous multi-agent systems under a directed acyclic graph

NASA Astrophysics Data System (ADS)

Yan, Jing; Guan, Xin-Ping; Luo, Xiao-Yuan

2011-04-01

This paper is concerned with the cooperative target pursuit problem by multiple agents based on directed acyclic graph. The target appears at a random location and moves only when sensed by the agents, and agents will pursue the target once they detect its existence. Since the ability of each agent may be different, we consider the heterogeneous multi-agent systems. According to the topology of the multi-agent systems, a novel consensus-based control law is proposed, where the target and agents are modeled as a leader and followers, respectively. Based on Mason's rule and signal flow graph analysis, the convergence conditions are provided to show that the agents can catch the target in a finite time. Finally, simulation studies are provided to verify the effectiveness of the proposed approach.

Impact of Different Policies on Unhealthy Dietary Behaviors in an Urban Adult Population: An Agent-Based Simulation Model

PubMed Central

Giabbanelli, Philippe J.; Arah, Onyebuchi A.; Zimmerman, Frederick J.

2014-01-01

Objectives. Unhealthy eating is a complex-system problem. We used agent-based modeling to examine the effects of different policies on unhealthy eating behaviors. Methods. We developed an agent-based simulation model to represent a synthetic population of adults in Pasadena, CA, and how they make dietary decisions. Data from the 2007 Food Attitudes and Behaviors Survey and other empirical studies were used to calibrate the parameters of the model. Simulations were performed to contrast the potential effects of various policies on the evolution of dietary decisions. Results. Our model showed that a 20% increase in taxes on fast foods would lower the probability of fast-food consumption by 3 percentage points, whereas improving the visibility of positive social norms by 10%, either through community-based or mass-media campaigns, could improve the consumption of fruits and vegetables by 7 percentage points and lower fast-food consumption by 6 percentage points. Zoning policies had no significant impact. Conclusions. Interventions emphasizing healthy eating norms may be more effective than directly targeting food prices or regulating local food outlets. Agent-based modeling may be a useful tool for testing the population-level effects of various policies within complex systems. PMID:24832414

A GPU-based framework for modeling real-time 3D lung tumor conformal dosimetry with subject-specific lung tumor motion.

PubMed

Min, Yugang; Santhanam, Anand; Neelakkantan, Harini; Ruddy, Bari H; Meeks, Sanford L; Kupelian, Patrick A

2010-09-07

In this paper, we present a graphics processing unit (GPU)-based simulation framework to calculate the delivered dose to a 3D moving lung tumor and its surrounding normal tissues, which are undergoing subject-specific lung deformations. The GPU-based simulation framework models the motion of the 3D volumetric lung tumor and its surrounding tissues, simulates the dose delivery using the dose extracted from a treatment plan using Pinnacle Treatment Planning System, Phillips, for one of the 3DCTs of the 4DCT and predicts the amount and location of radiation doses deposited inside the lung. The 4DCT lung datasets were registered with each other using a modified optical flow algorithm. The motion of the tumor and the motion of the surrounding tissues were simulated by measuring the changes in lung volume during the radiotherapy treatment using spirometry. The real-time dose delivered to the tumor for each beam is generated by summing the dose delivered to the target volume at each increase in lung volume during the beam delivery time period. The simulation results showed the real-time capability of the framework at 20 discrete tumor motion steps per breath, which is higher than the number of 4DCT steps (approximately 12) reconstructed during multiple breathing cycles.

An Application of Artificial Intelligence to the Implementation of Electronic Commerce

NASA Astrophysics Data System (ADS)

Srivastava, Anoop Kumar

In this paper, we present an application of Artificial Intelligence (AI) to the implementation of Electronic Commerce. We provide a multi autonomous agent based framework. Our agent based architecture leads to flexible design of a spectrum of multiagent system (MAS) by distributing computation and by providing a unified interface to data and programs. Autonomous agents are intelligent enough and provide autonomy, simplicity of communication, computation, and a well developed semantics. The steps of design and implementation are discussed in depth, structure of Electronic Marketplace, an ontology, the agent model, and interaction pattern between agents is given. We have developed mechanisms for coordination between agents using a language, which is called Virtual Enterprise Modeling Language (VEML). VEML is a integration of Java and Knowledge Query and Manipulation Language (KQML). VEML provides application programmers with potential to globally develop different kinds of MAS based on their requirements and applications. We have implemented a multi autonomous agent based system called VE System. We demonstrate efficacy of our system by discussing experimental results and its salient features.

Multi-agent fare optimization model of two modes problem and its analysis based on edge of chaos

NASA Astrophysics Data System (ADS)

Li, Xue-yan; Li, Xue-mei; Li, Xue-wei; Qiu, He-ting

2017-03-01

This paper proposes a new framework of fare optimization & game model for studying the competition between two travel modes (high speed railway and civil aviation) in which passengers' group behavior is taken into consideration. The small-world network is introduced to construct the multi-agent model of passengers' travel mode choice. The cumulative prospect theory is adopted to depict passengers' bounded rationality, the heterogeneity of passengers' reference point is depicted using the idea of group emotion computing. The conceptions of "Langton parameter" and "evolution entropy" in the theory of "edge of chaos" are introduced to create passengers' "decision coefficient" and "evolution entropy of travel mode choice" which are used to quantify passengers' group behavior. The numerical simulation and the analysis of passengers' behavior show that (1) the new model inherits the features of traditional model well and the idea of self-organizing traffic flow evolution fully embodies passengers' bounded rationality, (2) compared with the traditional model (logit model), when passengers are in the "edge of chaos" state, the total profit of the transportation system is higher.

Generating a Corpus of Mobile Forensic Images for Masquerading user Experimentation.

PubMed

Guido, Mark; Brooks, Marc; Grover, Justin; Katz, Eric; Ondricek, Jared; Rogers, Marcus; Sharpe, Lauren

2016-11-01

The Periodic Mobile Forensics (PMF) system investigates user behavior on mobile devices. It applies forensic techniques to an enterprise mobile infrastructure, utilizing an on-device agent named TractorBeam. The agent collects changed storage locations for later acquisition, reconstruction, and analysis. TractorBeam provides its data to an enterprise infrastructure that consists of a cloud-based queuing service, relational database, and analytical framework for running forensic processes. During a 3-month experiment with Purdue University, TractorBeam was utilized in a simulated operational setting across 34 users to evaluate techniques to identify masquerading users (i.e., users other than the intended device user). The research team surmises that all masqueraders are undesirable to an enterprise, even when a masquerader lacks malicious intent. The PMF system reconstructed 821 forensic images, extracted one million audit events, and accurately detected masqueraders. Evaluation revealed that developed methods reduced storage requirements 50-fold. This paper describes the PMF architecture, performance of TractorBeam throughout the protocol, and results of the masquerading user analysis. © 2016 American Academy of Forensic Sciences.

Developing a Problem-Based Learning Simulation: An Economics Unit on Trade

ERIC Educational Resources Information Center

Maxwell, Nan L.; Mergendoller, John R.; Bellisimo, Yolanda

2004-01-01

This article argues that the merger of simulations and problem-based learning (PBL) can enhance both active-learning strategies. Simulations benefit by using a PBL framework to promote student-directed learning and problem-solving skills to explain a simulated dilemma with multiple solutions. PBL benefits because simulations structure the…

An agent-based model identifies MRI regions of probable tumor invasion in a patient with glioblastoma

NASA Astrophysics Data System (ADS)

Chen, L. Leon; Ulmer, Stephan; Deisboeck, Thomas S.

2010-01-01

We present an application of a previously developed agent-based glioma model (Chen et al 2009 Biosystems 95 234-42) for predicting spatio-temporal tumor progression using a patient-specific MRI lattice derived from apparent diffusion coefficient (ADC) data. Agents representing collections of migrating glioma cells are initialized based upon voxels at the outer border of the tumor identified on T1-weighted (Gd+) MRI at an initial time point. These simulated migratory cells exhibit a specific biologically inspired spatial search paradigm, representing a weighting of the differential contribution from haptotactic permission and biomechanical resistance on the migration decision process. ADC data from 9 months after the initial tumor resection were used to select the best search paradigm for the simulation, which was initiated using data from 6 months after the initial operation. Using this search paradigm, 100 simulations were performed to derive a probabilistic map of tumor invasion locations. The simulation was able to successfully predict a recurrence in the dorsal/posterior aspect long before it was depicted on T1-weighted MRI, 18 months after the initial operation.

An agent-based model identifies MRI regions of probable tumor invasion in a patient with glioblastoma.

PubMed

Chen, L Leon; Ulmer, Stephan; Deisboeck, Thomas S

2010-01-21

We present an application of a previously developed agent-based glioma model (Chen et al 2009 Biosystems 95 234-42) for predicting spatio-temporal tumor progression using a patient-specific MRI lattice derived from apparent diffusion coefficient (ADC) data. Agents representing collections of migrating glioma cells are initialized based upon voxels at the outer border of the tumor identified on T1-weighted (Gd+) MRI at an initial time point. These simulated migratory cells exhibit a specific biologically inspired spatial search paradigm, representing a weighting of the differential contribution from haptotactic permission and biomechanical resistance on the migration decision process. ADC data from 9 months after the initial tumor resection were used to select the best search paradigm for the simulation, which was initiated using data from 6 months after the initial operation. Using this search paradigm, 100 simulations were performed to derive a probabilistic map of tumor invasion locations. The simulation was able to successfully predict a recurrence in the dorsal/posterior aspect long before it was depicted on T1-weighted MRI, 18 months after the initial operation.

The ultimatum game: Discrete vs. continuous offers

NASA Astrophysics Data System (ADS)

Dishon-Berkovits, Miriam; Berkovits, Richard

2014-09-01

In many experimental setups in social-sciences, psychology and economy the subjects are requested to accept or dispense monetary compensation which is usually given in discrete units. Using computer and mathematical modeling we show that in the framework of studying the dynamics of acceptance of proposals in the ultimatum game, the long time dynamics of acceptance of offers in the game are completely different for discrete vs. continuous offers. For discrete values the dynamics follow an exponential behavior. However, for continuous offers the dynamics are described by a power-law. This is shown using an agent based computer simulation as well as by utilizing an analytical solution of a mean-field equation describing the model. These findings have implications to the design and interpretation of socio-economical experiments beyond the ultimatum game.

A consensus-based framework for design, validation, and implementation of simulation-based training curricula in surgery.

PubMed

Zevin, Boris; Levy, Jeffrey S; Satava, Richard M; Grantcharov, Teodor P

2012-10-01

Simulation-based training can improve technical and nontechnical skills in surgery. To date, there is no consensus on the principles for design, validation, and implementation of a simulation-based surgical training curriculum. The aim of this study was to define such principles and formulate them into an interoperable framework using international expert consensus based on the Delphi method. Literature was reviewed, 4 international experts were queried, and consensus conference of national and international members of surgical societies was held to identify the items for the Delphi survey. Forty-five international experts in surgical education were invited to complete the online survey by ranking each item on a Likert scale from 1 to 5. Consensus was predefined as Cronbach's α ≥0.80. Items that 80% of experts ranked as ≥4 were included in the final framework. Twenty-four international experts with training in general surgery (n = 11), orthopaedic surgery (n = 2), obstetrics and gynecology (n = 3), urology (n = 1), plastic surgery (n = 1), pediatric surgery (n = 1), otolaryngology (n = 1), vascular surgery (n = 1), military (n = 1), and doctorate-level educators (n = 2) completed the iterative online Delphi survey. Consensus among participants was achieved after one round of the survey (Cronbach's α = 0.91). The final framework included predevelopment analysis; cognitive, psychomotor, and team-based training; curriculum validation evaluation and improvement; and maintenance of training. The Delphi methodology allowed for determination of international expert consensus on the principles for design, validation, and implementation of a simulation-based surgical training curriculum. These principles were formulated into a framework that can be used internationally across surgical specialties as a step-by-step guide for the development and validation of future simulation-based training curricula. Copyright © 2012 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Nature as a network of morphological infocomputational processes for cognitive agents

NASA Astrophysics Data System (ADS)

Dodig-Crnkovic, Gordana

2017-01-01

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted.

An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department.

PubMed

Yousefi, M; Ferreira, R P M

2017-03-30

This study presents an agent-based simulation modeling in an emergency department. In a traditional approach, a supervisor (or a manager) allocates the resources (receptionist, nurses, doctors, etc.) to different sections based on personal experience or by using decision-support tools. In this study, each staff agent took part in the process of allocating resources based on their observation in their respective sections, which gave the system the advantage of utilizing all the available human resources during the workday by being allocated to a different section. In this simulation, unlike previous studies, all staff agents took part in the decision-making process to re-allocate the resources in the emergency department. The simulation modeled the behavior of patients, receptionists, triage nurses, emergency room nurses and doctors. Patients were able to decide whether to stay in the system or leave the department at any stage of treatment. In order to evaluate the performance of this approach, 6 different scenarios were introduced. In each scenario, various key performance indicators were investigated before and after applying the group decision-making. The outputs of each simulation were number of deaths, number of patients who leave the emergency department without being attended, length of stay, waiting time and total number of discharged patients from the emergency department. Applying the self-organizing approach in the simulation showed an average of 12.7 and 14.4% decrease in total waiting time and number of patients who left without being seen, respectively. The results showed an average increase of 11.5% in total number of discharged patients from emergency department.

An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department

PubMed Central

Yousefi, M.; Ferreira, R.P.M.

2017-01-01

This study presents an agent-based simulation modeling in an emergency department. In a traditional approach, a supervisor (or a manager) allocates the resources (receptionist, nurses, doctors, etc.) to different sections based on personal experience or by using decision-support tools. In this study, each staff agent took part in the process of allocating resources based on their observation in their respective sections, which gave the system the advantage of utilizing all the available human resources during the workday by being allocated to a different section. In this simulation, unlike previous studies, all staff agents took part in the decision-making process to re-allocate the resources in the emergency department. The simulation modeled the behavior of patients, receptionists, triage nurses, emergency room nurses and doctors. Patients were able to decide whether to stay in the system or leave the department at any stage of treatment. In order to evaluate the performance of this approach, 6 different scenarios were introduced. In each scenario, various key performance indicators were investigated before and after applying the group decision-making. The outputs of each simulation were number of deaths, number of patients who leave the emergency department without being attended, length of stay, waiting time and total number of discharged patients from the emergency department. Applying the self-organizing approach in the simulation showed an average of 12.7 and 14.4% decrease in total waiting time and number of patients who left without being seen, respectively. The results showed an average increase of 11.5% in total number of discharged patients from emergency department. PMID:28380196

Developing interprofessional health competencies in a virtual world

PubMed Central

King, Sharla; Chodos, David; Stroulia, Eleni; Carbonaro, Mike; MacKenzie, Mark; Reid, Andrew; Torres, Lisa; Greidanus, Elaine

2012-01-01

Background Virtual worlds provide a promising means of delivering simulations for developing interprofessional health skills. However, developing and implementing a virtual world simulation is a challenging process, in part because of the novelty of virtual worlds as a simulation platform and also because of the degree of collaboration required among technical and subject experts. Thus, it can be difficult to ensure that the simulation is both technically satisfactory and educationally appropriate. Methods To address this challenge, we propose the use of de Freitas and Oliver's four-dimensional framework as a means of guiding the development process. We give an overview of the framework and describe how its principles can be applied to the development of virtual world simulations. Results We present two virtual world simulation pilot projects that adopted this approach, and describe our development experience in these projects. We directly connect this experience to the four-dimensional framework, thus validating the framework's applicability to the projects and to the context of virtual world simulations in general. Conclusions We present a series of recommendations for developing virtual world simulations for interprofessional health education. These recommendations are based on the four-dimensional framework and are also informed by our experience with the pilot projects. PMID:23195649

Conceptualizing Debates in Learning and Educational Research: Toward a Complex Systems Conceptual Framework of Learning

ERIC Educational Resources Information Center

Jacobson, Michael J.; Kapur, Manu; Reimann, Peter

2016-01-01

This article proposes a conceptual framework of learning based on perspectives and methodologies being employed in the study of complex physical and social systems to inform educational research. We argue that the contexts in which learning occurs are complex systems with elements or agents at different levels--including neuronal, cognitive,…

A Comparison of Three Approaches to Model Human Behavior

NASA Astrophysics Data System (ADS)

Palmius, Joel; Persson-Slumpi, Thomas

2010-11-01

One way of studying social processes is through the use of simulations. The use of simulations for this purpose has been established as its own field, social simulations, and has been used for studying a variety of phenomena. A simulation of a social setting can serve as an aid for thinking about that social setting, and for experimenting with different parameters and studying the outcomes caused by them. When using the simulation as an aid for thinking and experimenting, the chosen simulation approach will implicitly steer the simulationist towards thinking in a certain fashion in order to fit the model. To study the implications of model choice on the understanding of a setting where human anticipation comes into play, a simulation scenario of a coffee room was constructed using three different simulation approaches: Cellular Automata, Systems Dynamics and Agent-based modeling. The practical implementations of the models were done in three different simulation packages: Stella for Systems Dynamic, CaFun for Cellular automata and SesAM for Agent-based modeling. The models were evaluated both using Randers' criteria for model evaluation, and through introspection where the authors reflected upon how their understanding of the scenario was steered through the model choice. Further the software used for implementing the simulation models was evaluated, and practical considerations for the choice of software package are listed. It is concluded that the models have very different strengths. The Agent-based modeling approach offers the most intuitive support for thinking about and modeling a social setting where the behavior of the individual is in focus. The Systems Dynamics model would be preferable in situations where populations and large groups would be studied as wholes, but where individual behavior is of less concern. The Cellular Automata models would be preferable where processes need to be studied from the basis of a small set of very simple rules. It is further concluded that in most social simulation settings the Agent-based modeling approach would be the probable choice. This since the other models does not offer much in the way of supporting the modeling of the anticipatory behavior of humans acting in an organization.

Generic framework for mining cellular automata models on protein-folding simulations.

PubMed

Diaz, N; Tischer, I

2016-05-13

Cellular automata model identification is an important way of building simplified simulation models. In this study, we describe a generic architectural framework to ease the development process of new metaheuristic-based algorithms for cellular automata model identification in protein-folding trajectories. Our framework was developed by a methodology based on design patterns that allow an improved experience for new algorithms development. The usefulness of the proposed framework is demonstrated by the implementation of four algorithms, able to obtain extremely precise cellular automata models of the protein-folding process with a protein contact map representation. Dynamic rules obtained by the proposed approach are discussed, and future use for the new tool is outlined.

Optimization and Control of Agent-Based Models in Biology: A Perspective.

PubMed

An, G; Fitzpatrick, B G; Christley, S; Federico, P; Kanarek, A; Neilan, R Miller; Oremland, M; Salinas, R; Laubenbacher, R; Lenhart, S

2017-01-01

Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might carry out this approach. Rather than viewing the ABM as a model, it is to be viewed as a surrogate for the actual system. For a given optimization or control problem (which may change over time), the surrogate system is modeled instead, using data from the ABM and a modeling framework for which ready-made mathematical tools exist, such as differential equations, or for which control strategies can explored more easily. Once the optimization problem is solved for the model of the surrogate, it is then lifted to the surrogate and tested. The final step is to lift the optimization solution from the surrogate system to the actual system. This program is illustrated with published work, using two relatively simple ABMs as a demonstration, Sugarscape and a consumer-resource ABM. Specific techniques discussed include dimension reduction and approximation of an ABM by difference equations as well systems of PDEs, related to certain specific control objectives. This demonstration illustrates the very challenging mathematical problems that need to be solved before this approach can be realistically applied to complex and large ABMs, current and future. The paper outlines a research program to address them.

Imposing a Lagrangian Particle Framework on an Eulerian Hydrodynamics Infrastructure in Flash

NASA Technical Reports Server (NTRS)

Dubey, A.; Daley, C.; ZuHone, J.; Ricker, P. M.; Weide, K.; Graziani, C.

2012-01-01

In many astrophysical simulations, both Eulerian and Lagrangian quantities are of interest. For example, in a galaxy cluster merger simulation, the intracluster gas can have Eulerian discretization, while dark matter can be modeled using particles. FLASH, a component-based scientific simulation code, superimposes a Lagrangian framework atop an adaptive mesh refinement Eulerian framework to enable such simulations. The discretization of the field variables is Eulerian, while the Lagrangian entities occur in many different forms including tracer particles, massive particles, charged particles in particle-in-cell mode, and Lagrangian markers to model fluid structure interactions. These widely varying roles for Lagrangian entities are possible because of the highly modular, flexible, and extensible architecture of the Lagrangian framework. In this paper, we describe the Lagrangian framework in FLASH in the context of two very different applications, Type Ia supernovae and galaxy cluster mergers, which use the Lagrangian entities in fundamentally different ways.

Imposing a Lagrangian Particle Framework on an Eulerian Hydrodynamics Infrastructure in FLASH

NASA Astrophysics Data System (ADS)

Dubey, A.; Daley, C.; ZuHone, J.; Ricker, P. M.; Weide, K.; Graziani, C.

2012-08-01

In many astrophysical simulations, both Eulerian and Lagrangian quantities are of interest. For example, in a galaxy cluster merger simulation, the intracluster gas can have Eulerian discretization, while dark matter can be modeled using particles. FLASH, a component-based scientific simulation code, superimposes a Lagrangian framework atop an adaptive mesh refinement Eulerian framework to enable such simulations. The discretization of the field variables is Eulerian, while the Lagrangian entities occur in many different forms including tracer particles, massive particles, charged particles in particle-in-cell mode, and Lagrangian markers to model fluid-structure interactions. These widely varying roles for Lagrangian entities are possible because of the highly modular, flexible, and extensible architecture of the Lagrangian framework. In this paper, we describe the Lagrangian framework in FLASH in the context of two very different applications, Type Ia supernovae and galaxy cluster mergers, which use the Lagrangian entities in fundamentally different ways.

Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design

NASA Astrophysics Data System (ADS)

Ang, Chee Siang; Zaphiris, Panayiotis

We propose the use of social simulations to study and support the design of online communities. In this paper, we developed an Agent-Based Model (ABM) to simulate and study the formation of social networks in a Massively Multiplayer Online Role Playing Game (MMORPG) guild community. We first analyzed the activities and the social network (who-interacts-with-whom) of an existing guild community to identify its interaction patterns and characteristics. Then, based on the empirical results, we derived and formalized the interaction rules, which were implemented in our simulation. Using the simulation, we reproduced the observed social network of the guild community as a means of validation. The simulation was then used to examine how various parameters of the community (e.g. the level of activity, the number of neighbors of each agent, etc) could potentially influence the characteristic of the social networks.

Situation awareness-based agent transparency for human-autonomy teaming effectiveness

NASA Astrophysics Data System (ADS)

Chen, Jessie Y. C.; Barnes, Michael J.; Wright, Julia L.; Stowers, Kimberly; Lakhmani, Shan G.

2017-05-01

We developed the Situation awareness-based Agent Transparency (SAT) model to support human operators' situation awareness of the mission environment through teaming with intelligent agents. The model includes the agent's current actions and plans (Level 1), its reasoning process (Level 2), and its projection of future outcomes (Level 3). Human-inthe-loop simulation experiments have been conducted (Autonomous Squad Member and IMPACT) to illustrate the utility of the model for human-autonomy team interface designs. Across studies, the results consistently showed that human operators' task performance improved as the agents became more transparent. They also perceived transparent agents as more trustworthy.

The Littoral Combat Ship (LCS) Surface Warfare (SUW) Module: Determining the Surface-To-Surface Missile and Air-To-Surface Missile Mix

DTIC Science & Technology

2010-09-01

agent-based modeling platform known as MANA. The simulation is exercised over a broad range of different weapon systems types with their capabilities...Navy B.A., University of Florida, 2004 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN MODELING ...aerial vehicle (UAV) will have. This study uses freely available data to build a simulation utilizing an agent-based modeling platform known as MANA

An agent based simulation tool for scheduling emergency department physicians.

PubMed

Jones, Spencer S; Evans, R Scott

2008-11-06

Emergency department overcrowding is a problem that threatens the public health of communities and compromises the quality of care given to individual patients. The Institute of Medicine recommends that hospitals employ information technology and operations research methods to reduce overcrowding. This paper describes the development of an agent based simulation tool that has been designed to evaluate the impact of various physician staffing configurations on patient waiting times in the emergency department. We evaluate the feasibility of this tool at a single hospital emergency department.

Numerical simulation of the casting process of titanium removable partial denture frameworks.

PubMed

Wu, Menghuai; Wagner, Ingo; Sahm, Peter R; Augthun, Michael

2002-03-01

The objective of this work was to study the filling incompleteness and porosity defects in titanium removal partial denture frameworks by means of numerical simulation. Two frameworks, one for lower jaw and one for upper jaw, were chosen according to dentists' recommendation to be simulated. Geometry of the frameworks were laser-digitized and converted into a simulation software (MAGMASOFT). Both mold filling and solidification of the castings with different sprue designs (e.g. tree, ball, and runner-bar) were numerically calculated. The shrinkage porosity was quantitatively predicted by a feeding criterion, the potential filling defect and gas pore sensitivity were estimated based on the filling and solidification results. A satisfactory sprue design with process parameters was finally recommended for real casting trials (four replica for each frameworks). All the frameworks were successfully cast. Through X-ray radiographic inspections it was found that all the castings were acceptably sound except for only one case in which gas bubbles were detected in the grasp region of the frame. It is concluded that numerical simulation aids to achieve understanding of the casting process and defect formation in titanium frameworks, hence to minimize the risk of producing defect casting by improving the sprue design and process parameters.

Conceptual Modeling Framework for E-Area PA HELP Infiltration Model Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dyer, J. A.

A conceptual modeling framework based on the proposed E-Area Low-Level Waste Facility (LLWF) closure cap design is presented for conducting Hydrologic Evaluation of Landfill Performance (HELP) model simulations of intact and subsided cap infiltration scenarios for the next E-Area Performance Assessment (PA).

Agent-Based vs. Equation-based Epidemiological Models:A Model Selection Case Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas R; Nutaro, James J

This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation based model and an agent based model of the 1918 Spanish flu and we leverage data published in the literature for our case- study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigmmore » to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.« less

Approach for Autonomous Control of Unmanned Aerial Vehicle Using Intelligent Agents for Knowledge Creation

NASA Technical Reports Server (NTRS)

Dufrene, Warren R., Jr.

2004-01-01

This paper describes the development of a planned approach for Autonomous operation of an Unmanned Aerial Vehicle (UAV). A Hybrid approach will seek to provide Knowledge Generation through the application of Artificial Intelligence (AI) and Intelligent Agents (IA) for UAV control. The applications of several different types of AI techniques for flight are explored during this research effort. The research concentration is directed to the application of different AI methods within the UAV arena. By evaluating AI and biological system approaches. which include Expert Systems, Neural Networks. Intelligent Agents, Fuzzy Logic, and Complex Adaptive Systems, a new insight may be gained into the benefits of AI and CAS techniques applied to achieving true autonomous operation of these systems. Although flight systems were explored, the benefits should apply to many Unmanned Vehicles such as: Rovers. Ocean Explorers, Robots, and autonomous operation systems. A portion of the flight system is broken down into control agents that represent the intelligent agent approach used in AI. After the completion of a successful approach, a framework for applying an intelligent agent is presented. The initial results from simulation of a security agent for communication are presented.

A multi-stakeholder framework for sustainable energy behavior: A multidisciplinary systems study

NASA Astrophysics Data System (ADS)

Khansari, Nasrin

Growth of population and moving towards over-consumption and over-pollution are significant threats to the environment and therefore necessitate moving towards sustainability approaches. CO2 emissions are considered to be the main basis of the incredible increase in the earth's surface temperature in recent years. Most emissions result from human activities. Thus, developing a detailed framework representing the parameters affecting individuals' energy behaviors is required. This dissertation offers an integrated conceptual framework to increase the efficiency of energy systems under complex and uncertainty conditions, facilitate energy consumption problem solving, and support the development of capacities at the individual, social, and technical levels to improve managing energy consumptions in the future. This research presents a conceptual soft systems model to explore the process of individuals' energy behavior change based on socio-structural and techno-structural contexts. In addition, a comprehensive model based on systems dynamics principles is presented to address the issue of CO2 emissions related to the households' energy consumption behavior. The proposed systems dynamics model provides a broad overview of the key agents affecting energy consumption, including government/public sector, households, and power industry. The model is created based on the research in the literature discussing the causal relations between various variables. The proposed systems dynamics model is verified by simulating different scenarios. In this research a survey is designed and conducted to investigate the role of individual, social and technical behaviors in reducing energy consumption, energy costs and carbon footprints based on the energy use profile. In sum, this study investigates the process of energy behavior change based on socio-structural and techno-structural contexts.