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 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…

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.

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.

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.

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.

Towards Cooperative Predictive Data Mining in Competitive Environments

NASA Astrophysics Data System (ADS)

Lisý, Viliam; Jakob, Michal; Benda, Petr; Urban, Štěpán; Pěchouček, Michal

We study the problem of predictive data mining in a competitive multi-agent setting, in which each agent is assumed to have some partial knowledge required for correctly classifying a set of unlabelled examples. The agents are self-interested and therefore need to reason about the trade-offs between increasing their classification accuracy by collaborating with other agents and disclosing their private classification knowledge to other agents through such collaboration. We analyze the problem and propose a set of components which can enable cooperation in this otherwise competitive task. These components include measures for quantifying private knowledge disclosure, data-mining models suitable for multi-agent predictive data mining, and a set of strategies by which agents can improve their classification accuracy through collaboration. The overall framework and its individual components are validated on a synthetic experimental domain.

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.

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.

Multi-Agent Architecture with Support to Quality of Service and Quality of Control

NASA Astrophysics Data System (ADS)

Poza-Luján, Jose-Luis; Posadas-Yagüe, Juan-Luis; Simó-Ten, Jose-Enrique

Multi Agent Systems (MAS) are one of the most suitable frameworks for the implementation of intelligent distributed control system. Agents provide suitable flexibility to give support to implied heterogeneity in cyber-physical systems. Quality of Service (QoS) and Quality of Control (QoC) parameters are commonly utilized to evaluate the efficiency of the communications and the control loop. Agents can use the quality measures to take a wide range of decisions, like suitable placement on the control node or to change the workload to save energy. This article describes the architecture of a multi agent system that provides support to QoS and QoC parameters to optimize de system. The architecture uses a Publish-Subscriber model, based on Data Distribution Service (DDS) to send the control messages. Due to the nature of the Publish-Subscribe model, the architecture is suitable to implement event-based control (EBC) systems. The architecture has been called FSACtrl.

Multi-agent coordination in directed moving neighbourhood random networks

NASA Astrophysics Data System (ADS)

Shang, Yi-Lun

2010-07-01

This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results.

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.

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.

Design and Control of Large Collections of Learning Agents

NASA Technical Reports Server (NTRS)

Agogino, Adrian

2001-01-01

The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

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.

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 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.

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.

Model-free learning on robot kinematic chains using a nested multi-agent topology

NASA Astrophysics Data System (ADS)

Karigiannis, John N.; Tzafestas, Costas S.

2016-11-01

This paper proposes a model-free learning scheme for the developmental acquisition of robot kinematic control and dexterous manipulation skills. The approach is based on a nested-hierarchical multi-agent architecture that intuitively encapsulates the topology of robot kinematic chains, where the activity of each independent degree-of-freedom (DOF) is finally mapped onto a distinct agent. Each one of those agents progressively evolves a local kinematic control strategy in a game-theoretic sense, that is, based on a partial (local) view of the whole system topology, which is incrementally updated through a recursive communication process according to the nested-hierarchical topology. Learning is thus approached not through demonstration and training but through an autonomous self-exploration process. A fuzzy reinforcement learning scheme is employed within each agent to enable efficient exploration in a continuous state-action domain. This paper constitutes in fact a proof of concept, demonstrating that global dexterous manipulation skills can indeed evolve through such a distributed iterative learning of local agent sensorimotor mappings. The main motivation behind the development of such an incremental multi-agent topology is to enhance system modularity, to facilitate extensibility to more complex problem domains and to improve robustness with respect to structural variations including unpredictable internal failures. These attributes of the proposed system are assessed in this paper through numerical experiments in different robot manipulation task scenarios, involving both single and multi-robot kinematic chains. The generalisation capacity of the learning scheme is experimentally assessed and robustness properties of the multi-agent system are also evaluated with respect to unpredictable variations in the kinematic topology. Furthermore, these numerical experiments demonstrate the scalability properties of the proposed nested-hierarchical architecture, where new agents can be recursively added in the hierarchy to encapsulate individual active DOFs. The results presented in this paper demonstrate the feasibility of such a distributed multi-agent control framework, showing that the solutions which emerge are plausible and near-optimal. Numerical efficiency and computational cost issues are also discussed.

Multi-agent framework for negotiation in a closed environment

NASA Astrophysics Data System (ADS)

Cretan, Adina; Coutinho, Carlos; Bratu, Ben; Jardim-Goncalves, Ricardo

2013-10-01

The goal of this paper is to offer support for small and medium enterprises which cannot or do not want to fulfill a big contract alone. Each organization has limited resources and in order to better accomplish a higher external demand, the managers are forced to outsource parts of their contracts even to concurrent organizations. In this concurrent environment each enterprise wants to preserve its decision autonomy and to disclose as little as possible from its business information. To describe this interaction, our approach is to define a framework for managing parallel and concurrent negotiations among independent organizations acting in the same industrial market. The complexity of our negotiation framework is done by the dynamic environment in which multi-attribute and multi-participant negotiations are racing over the same set of resources. Moreover, the proposed framework helps the organizations within the collaborative networked environment to augment their efficiency and ability to react to unforeseen situations, thus improving their market competitiveness.

Consensus for multi-agent systems with time-varying input delays

NASA Astrophysics Data System (ADS)

Yuan, Chengzhi; Wu, Fen

2017-10-01

This paper addresses the consensus control problem for linear multi-agent systems subject to uniform time-varying input delays and external disturbance. A novel state-feedback consensus protocol is proposed under the integral quadratic constraint (IQC) framework, which utilises not only the relative state information from neighbouring agents but also the real-time information of delays by means of the dynamic IQC system states for feedback control. Based on this new consensus protocol, the associated IQC-based control synthesis conditions are established and fully characterised as linear matrix inequalities (LMIs), such that the consensus control solution with optimal ? disturbance attenuation performance can be synthesised efficiently via convex optimisation. A numerical example is used to demonstrate the proposed approach.

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

Automation of multi-agent control for complex dynamic systems in heterogeneous computational network

NASA Astrophysics Data System (ADS)

Oparin, Gennady; Feoktistov, Alexander; Bogdanova, Vera; Sidorov, Ivan

2017-01-01

The rapid progress of high-performance computing entails new challenges related to solving large scientific problems for various subject domains in a heterogeneous distributed computing environment (e.g., a network, Grid system, or Cloud infrastructure). The specialists in the field of parallel and distributed computing give the special attention to a scalability of applications for problem solving. An effective management of the scalable application in the heterogeneous distributed computing environment is still a non-trivial issue. Control systems that operate in networks, especially relate to this issue. We propose a new approach to the multi-agent management for the scalable applications in the heterogeneous computational network. The fundamentals of our approach are the integrated use of conceptual programming, simulation modeling, network monitoring, multi-agent management, and service-oriented programming. We developed a special framework for an automation of the problem solving. Advantages of the proposed approach are demonstrated on the parametric synthesis example of the static linear regulator for complex dynamic systems. Benefits of the scalable application for solving this problem include automation of the multi-agent control for the systems in a parallel mode with various degrees of its detailed elaboration.

A multi-agent safety response model in the construction industry.

PubMed

Meliá, José L

2015-01-01

The construction industry is one of the sectors with the highest accident rates and the most serious accidents. A multi-agent safety response approach allows a useful diagnostic tool in order to understand factors affecting risk and accidents. The special features of the construction sector can influence the relationships among safety responses along the model of safety influences. The purpose of this paper is to test a model explaining risk and work-related accidents in the construction industry as a result of the safety responses of the organization, the supervisors, the co-workers and the worker. 374 construction employees belonging to 64 small Spanish construction companies working for two main companies participated in the study. Safety responses were measured using a 45-item Likert-type questionnaire. The structure of the measure was analyzed using factor analysis and the model of effects was tested using a structural equation model. Factor analysis clearly identifies the multi-agent safety dimensions hypothesized. The proposed safety response model of work-related accidents, involving construction specific results, showed a good fit. The multi-agent safety response approach to safety climate is a useful framework for the assessment of organizational and behavioral risks in construction.

Chronic Heart Failure Follow-up Management Based on Agent Technology.

PubMed

Mohammadzadeh, Niloofar; Safdari, Reza

2015-10-01

Monitoring heart failure patients through continues assessment of sign and symptoms by information technology tools lead to large reduction in re-hospitalization. Agent technology is one of the strongest artificial intelligence areas; therefore, it can be expected to facilitate, accelerate, and improve health services especially in home care and telemedicine. The aim of this article is to provide an agent-based model for chronic heart failure (CHF) follow-up management. This research was performed in 2013-2014 to determine appropriate scenarios and the data required to monitor and follow-up CHF patients, and then an agent-based model was designed. Agents in the proposed model perform the following tasks: medical data access, communication with other agents of the framework and intelligent data analysis, including medical data processing, reasoning, negotiation for decision-making, and learning capabilities. The proposed multi-agent system has ability to learn and thus improve itself. Implementation of this model with more and various interval times at a broader level could achieve better results. The proposed multi-agent system is no substitute for cardiologists, but it could assist them in decision-making.

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.

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.

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.

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.

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

Chronic Heart Failure Follow-up Management Based on Agent Technology

PubMed Central

Safdari, Reza

2015-01-01

Objectives Monitoring heart failure patients through continues assessment of sign and symptoms by information technology tools lead to large reduction in re-hospitalization. Agent technology is one of the strongest artificial intelligence areas; therefore, it can be expected to facilitate, accelerate, and improve health services especially in home care and telemedicine. The aim of this article is to provide an agent-based model for chronic heart failure (CHF) follow-up management. Methods This research was performed in 2013-2014 to determine appropriate scenarios and the data required to monitor and follow-up CHF patients, and then an agent-based model was designed. Results Agents in the proposed model perform the following tasks: medical data access, communication with other agents of the framework and intelligent data analysis, including medical data processing, reasoning, negotiation for decision-making, and learning capabilities. Conclusions The proposed multi-agent system has ability to learn and thus improve itself. Implementation of this model with more and various interval times at a broader level could achieve better results. The proposed multi-agent system is no substitute for cardiologists, but it could assist them in decision-making. PMID:26618038

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 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

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.

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.

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

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.

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.

QUICR-learning for Multi-Agent Coordination

NASA Technical Reports Server (NTRS)

Agogino, Adrian K.; Tumer, Kagan

2006-01-01

Coordinating multiple agents that need to perform a sequence of actions to maximize a system level reward requires solving two distinct credit assignment problems. First, credit must be assigned for an action taken at time step t that results in a reward at time step t > t. Second, credit must be assigned for the contribution of agent i to the overall system performance. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning. The second credit assignment problem is typically addressed by creating custom reward functions. To address both credit assignment problems simultaneously, we propose the "Q Updates with Immediate Counterfactual Rewards-learning" (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. QUICR-learning is based on previous work on single-time-step counterfactual rewards described by the collectives framework. Results on a traffic congestion problem shows that QUICR-learning is significantly better than a Q-learner using collectives-based (single-time-step counterfactual) rewards. In addition QUICR-learning provides significant gains over conventional and local Q-learning. Additional results on a multi-agent grid-world problem show that the improvements due to QUICR-learning are not domain specific and can provide up to a ten fold increase in performance over existing methods.

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.

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…

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.

Fault-tolerant Control of a Cyber-physical System

NASA Astrophysics Data System (ADS)

Roxana, Rusu-Both; Eva-Henrietta, Dulf

2017-10-01

Cyber-physical systems represent a new emerging field in automatic control. The fault system is a key component, because modern, large scale processes must meet high standards of performance, reliability and safety. Fault propagation in large scale chemical processes can lead to loss of production, energy, raw materials and even environmental hazard. The present paper develops a multi-agent fault-tolerant control architecture using robust fractional order controllers for a (13C) cryogenic separation column cascade. The JADE (Java Agent DEvelopment Framework) platform was used to implement the multi-agent fault tolerant control system while the operational model of the process was implemented in Matlab/SIMULINK environment. MACSimJX (Multiagent Control Using Simulink with Jade Extension) toolbox was used to link the control system and the process model. In order to verify the performance and to prove the feasibility of the proposed control architecture several fault simulation scenarios were performed.

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.

Computational Model for Ethnographically Informed Systems Design

NASA Astrophysics Data System (ADS)

Iqbal, Rahat; James, Anne; Shah, Nazaraf; Terken, Jacuqes

This paper presents a computational model for ethnographically informed systems design that can support complex and distributed cooperative activities. This model is based on an ethnographic framework consisting of three important dimensions (e.g., distributed coordination, awareness of work and plans and procedure), and the BDI (Belief, Desire and Intention) model of intelligent agents. The ethnographic framework is used to conduct ethnographic analysis and to organise ethnographically driven information into three dimensions, whereas the BDI model allows such information to be mapped upon the underlying concepts of multi-agent systems. The advantage of this model is that it is built upon an adaptation of existing mature and well-understood techniques. By the use of this model, we also address the cognitive aspects of systems design.

Bargaining Agents in Wireless Contexts: An Alternating-Offers Protocol for Multi-issue Bilateral Negotiation in Mobile Marketplaces

NASA Astrophysics Data System (ADS)

Ragone, Azzurra; Ruta, Michele; di Sciascio, Eugenio; Donini, Francesco M.

We present an approach to multi-issue bilateral negotiation for mobile commerce scenarios. The negotiation mechanism has been integrated in a semantic-based application layer enhancing both RFID and Bluetooth wireless standards. OWL DL has been used to model advertisements and relationships among issues within a shared common ontology. Finally, non standard inference services integrated with utility theory help in finding suitable agreements. We illustrate and motivate the provided theoretical framework in a wireless commerce case study.

Optimal Wonderful Life Utility Functions in Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Tumer, Kagan; Swanson, Keith (Technical Monitor)

2000-01-01

The mathematics of Collective Intelligence (COINs) is concerned with the design of multi-agent systems so as to optimize an overall global utility function when those systems lack centralized communication and control. Typically in COINs each agent runs a distinct Reinforcement Learning (RL) algorithm, so that much of the design problem reduces to how best to initialize/update each agent's private utility function, as far as the ensuing value of the global utility is concerned. Traditional team game solutions to this problem assign to each agent the global utility as its private utility function. In previous work we used the COIN framework to derive the alternative Wonderful Life Utility (WLU), and experimentally established that having the agents use it induces global utility performance up to orders of magnitude superior to that induced by use of the team game utility. The WLU has a free parameter (the clamping parameter) which we simply set to zero in that previous work. Here we derive the optimal value of the clamping parameter, and demonstrate experimentally that using that optimal value can result in significantly improved performance over that of clamping to zero, over and above the improvement beyond traditional approaches.

Product Distribution Theory for Control of Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Lee, Chia Fan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for controlling Multi-Agent Systems (MAS's). First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint stare of the agents. Accordingly we can consider a team game in which the shared utility is a performance measure of the behavior of the MAS. For such a scenario the game is at equilibrium - the Lagrangian is optimized - when the joint distribution of the agents optimizes the system's expected performance. One common way to find that equilibrium is to have each agent run a reinforcement learning algorithm. Here we investigate the alternative of exploiting PD theory to run gradient descent on the Lagrangian. We present computer experiments validating some of the predictions of PD theory for how best to do that gradient descent. We also demonstrate how PD theory can improve performance even when we are not allowed to rerun the MAS from different initial conditions, a requirement implicit in some previous work.

Crafting an Agentive Self: Case Studies of Digital Storytelling

ERIC Educational Resources Information Center

Hull, Glynda A.; Katz, Mira-Lisa

2006-01-01

Drawing on data from a multi-year digital storytelling project, this comparative case study offers portraits of two emerging authors--one a child and the other a young adult--who used multiple media and modes to articulate pivotal moments in their lives and reflect on life trajectories. The conceptual framework blends recent scholarship on…

Towards an intelligent framework for multimodal affective data analysis.

PubMed

Poria, Soujanya; Cambria, Erik; Hussain, Amir; Huang, Guang-Bin

2015-03-01

An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate. Copyright © 2014 Elsevier Ltd. All rights reserved.

Coevolutionary Free Lunches

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Macready, William G.

2005-01-01

Recent work on the foundations of optimization has begun to uncover its underlying rich structure. In particular, the "No Free Lunch" (NFL) theorems [WM97] state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering most search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving agents. As a particular instance of the latter, it covers "self-play" problems. In these problems the agents work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. However in the typical coevolutionary scenarios encountered in biology, where there is no champion, NFL still holds.

Dual Contrast - Magnetic Resonance Fingerprinting (DC-MRF): A Platform for Simultaneous Quantification of Multiple MRI Contrast Agents.

PubMed

Anderson, Christian E; Donnola, Shannon B; Jiang, Yun; Batesole, Joshua; Darrah, Rebecca; Drumm, Mitchell L; Brady-Kalnay, Susann M; Steinmetz, Nicole F; Yu, Xin; Griswold, Mark A; Flask, Chris A

2017-08-16

Injectable Magnetic Resonance Imaging (MRI) contrast agents have been widely used to provide critical assessments of disease for both clinical and basic science imaging research studies. The scope of available MRI contrast agents has expanded over the years with the emergence of molecular imaging contrast agents specifically targeted to biological markers. Unfortunately, synergistic application of more than a single molecular contrast agent has been limited by MRI's ability to only dynamically measure a single agent at a time. In this study, a new Dual Contrast - Magnetic Resonance Fingerprinting (DC - MRF) methodology is described that can detect and independently quantify the local concentration of multiple MRI contrast agents following simultaneous administration. This "multi-color" MRI methodology provides the opportunity to monitor multiple molecular species simultaneously and provides a practical, quantitative imaging framework for the eventual clinical translation of molecular imaging contrast agents.

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model

NASA Astrophysics Data System (ADS)

Alexandridis, Konstantinos T.

This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land use change. Finally, the major contributions to the science are presented along with valuable directions for future research.

Clustered, regularly interspaced short palindromic repeat (CRISPR) diversity and virulence factor distribution in avian Escherichia coli.

PubMed

Fu, Qiang; Su, Zhixin; Cheng, Yuqiang; Wang, Zhaofei; Li, Shiyu; Wang, Heng'an; Sun, Jianhe; Yan, Yaxian

In order to investigate the diverse characteristics of clustered, regularly interspaced short palindromic repeat (CRISPR) arrays and the distribution of virulence factor genes in avian Escherichia coli, 80 E. coli isolates obtained from chickens with avian pathogenic E. coli (APEC) or avian fecal commensal E. coli (AFEC) were identified. Using the multiplex polymerase chain reaction (PCR), five genes were subjected to phylogenetic typing and examined for CRISPR arrays to study genetic relatedness among the strains. The strains were further analyzed for CRISPR loci and virulence factor genes to determine a possible association between their CRISPR elements and their potential virulence. The strains were divided into five phylogenetic groups: A, B1, B2, D and E. It was confirmed that two types of CRISPR arrays, CRISPR1 and CRISPR2, which contain up to 246 distinct spacers, were amplified in most of the strains. Further classification of the isolates was achieved by sorting them into nine CRISPR clusters based on their spacer profiles, which indicates a candidate typing method for E. coli. Several significant differences in invasion-associated gene distribution were found between the APEC isolates and the AFEC isolates. Our results identified the distribution of 11 virulence genes and CRISPR diversity in 80 strains. It was demonstrated that, with the exception of iucD and aslA, there was no sharp demarcation in the gene distribution between the pathogenic (APEC) and commensal (AFEC) strains, while the total number of indicated CRISPR spacers may have a positive correlation with the potential pathogenicity of the E. coli isolates. Copyright © 2016. Published by Elsevier Masson SAS.

Learning consensus in adversarial environments

NASA Astrophysics Data System (ADS)

Vamvoudakis, Kyriakos G.; García Carrillo, Luis R.; Hespanha, João. P.

2013-05-01

This work presents a game theory-based consensus problem for leaderless multi-agent systems in the presence of adversarial inputs that are introducing disturbance to the dynamics. Given the presence of enemy components and the possibility of malicious cyber attacks compromising the security of networked teams, a position agreement must be reached by the networked mobile team based on environmental changes. The problem is addressed under a distributed decision making framework that is robust to possible cyber attacks, which has an advantage over centralized decision making in the sense that a decision maker is not required to access information from all the other decision makers. The proposed framework derives three tuning laws for every agent; one associated with the cost, one associated with the controller, and one with the adversarial input.

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

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.

Adaptive Multi-Agent Systems for Constrained Optimization

NASA Technical Reports Server (NTRS)

Macready, William; Bieniawski, Stefan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.

Multi-Agent Framework for the Fair Division of Resources and Tasks

DTIC Science & Technology

2006-01-01

144 B.1.2 Application of Shake Out Algorithm to JFK Airport Test Data.........................144 B.2 Generalization...145 Figure B–2: Available Aircraft Inventory at JFK Airport ............................................. 148 Figure B–3...Available Aircraft Inventory at JFK Airport after the first shake out ....... 148 Figure B–4: Inventory Vectors for Second and Third Shake Outs

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.

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.

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.

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.

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.

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.

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

Conserving analyst attention units: use of multi-agent software and CEP methods to assist information analysis

NASA Astrophysics Data System (ADS)

Rimland, Jeffrey; McNeese, Michael; Hall, David

2013-05-01

Although the capability of computer-based artificial intelligence techniques for decision-making and situational awareness has seen notable improvement over the last several decades, the current state-of-the-art still falls short of creating computer systems capable of autonomously making complex decisions and judgments in many domains where data is nuanced and accountability is high. However, there is a great deal of potential for hybrid systems in which software applications augment human capabilities by focusing the analyst's attention to relevant information elements based on both a priori knowledge of the analyst's goals and the processing/correlation of a series of data streams too numerous and heterogeneous for the analyst to digest without assistance. Researchers at Penn State University are exploring ways in which an information framework influenced by Klein's (Recognition Primed Decision) RPD model, Endsley's model of situational awareness, and the Joint Directors of Laboratories (JDL) data fusion process model can be implemented through a novel combination of Complex Event Processing (CEP) and Multi-Agent Software (MAS). Though originally designed for stock market and financial applications, the high performance data-driven nature of CEP techniques provide a natural compliment to the proven capabilities of MAS systems for modeling naturalistic decision-making, performing process adjudication, and optimizing networked processing and cognition via the use of "mobile agents." This paper addresses the challenges and opportunities of such a framework for augmenting human observational capability as well as enabling the ability to perform collaborative context-aware reasoning in both human teams and hybrid human / software agent teams.

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.

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.

A Framework System for Intelligent Support in Open Distributed Learning Environments--A Look Back from 16 Years Later

ERIC Educational Resources Information Center

Hoppe, H. Ulrich

2016-01-01

The 1998 paper by Martin Mühlenbrock, Frank Tewissen, and myself introduced a multi-agent architecture and a component engineering approach for building open distributed learning environments to support group learning in different types of classroom settings. It took up prior work on "multiple student modeling" as a method to configure…

Framework of distributed coupled atmosphere-ocean-wave modeling system

NASA Astrophysics Data System (ADS)

Wen, Yuanqiao; Huang, Liwen; Deng, Jian; Zhang, Jinfeng; Wang, Sisi; Wang, Lijun

2006-05-01

In order to research the interactions between the atmosphere and ocean as well as their important role in the intensive weather systems of coastal areas, and to improve the forecasting ability of the hazardous weather processes of coastal areas, a coupled atmosphere-ocean-wave modeling system has been developed. The agent-based environment framework for linking models allows flexible and dynamic information exchange between models. For the purpose of flexibility, portability and scalability, the framework of the whole system takes a multi-layer architecture that includes a user interface layer, computational layer and service-enabling layer. The numerical experiment presented in this paper demonstrates the performance of the distributed coupled modeling system.

Overtaking method based on sand-sifter mechanism: Why do optimistic value functions find optimal solutions in multi-armed bandit problems?

PubMed

Ochi, Kento; Kamiura, Moto

2015-09-01

A multi-armed bandit problem is a search problem on which a learning agent must select the optimal arm among multiple slot machines generating random rewards. UCB algorithm is one of the most popular methods to solve multi-armed bandit problems. It achieves logarithmic regret performance by coordinating balance between exploration and exploitation. Since UCB algorithms, researchers have empirically known that optimistic value functions exhibit good performance in multi-armed bandit problems. The terms optimistic or optimism might suggest that the value function is sufficiently larger than the sample mean of rewards. The first definition of UCB algorithm is focused on the optimization of regret, and it is not directly based on the optimism of a value function. We need to think the reason why the optimism derives good performance in multi-armed bandit problems. In the present article, we propose a new method, which is called Overtaking method, to solve multi-armed bandit problems. The value function of the proposed method is defined as an upper bound of a confidence interval with respect to an estimator of expected value of reward: the value function asymptotically approaches to the expected value of reward from the upper bound. If the value function is larger than the expected value under the asymptote, then the learning agent is almost sure to be able to obtain the optimal arm. This structure is called sand-sifter mechanism, which has no regrowth of value function of suboptimal arms. It means that the learning agent can play only the current best arm in each time step. Consequently the proposed method achieves high accuracy rate and low regret and some value functions of it can outperform UCB algorithms. This study suggests the advantage of optimism of agents in uncertain environment by one of the simplest frameworks. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

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.

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.

The organization and control of an evolving interdependent population

PubMed Central

Vural, Dervis C.; Isakov, Alexander; Mahadevan, L.

2015-01-01

Starting with Darwin, biologists have asked how populations evolve from a low fitness state that is evolutionarily stable to a high fitness state that is not. Specifically of interest is the emergence of cooperation and multicellularity where the fitness of individuals often appears in conflict with that of the population. Theories of social evolution and evolutionary game theory have produced a number of fruitful results employing two-state two-body frameworks. In this study, we depart from this tradition and instead consider a multi-player, multi-state evolutionary game, in which the fitness of an agent is determined by its relationship to an arbitrary number of other agents. We show that populations organize themselves in one of four distinct phases of interdependence depending on one parameter, selection strength. Some of these phases involve the formation of specialized large-scale structures. We then describe how the evolution of independence can be manipulated through various external perturbations. PMID:26040593

Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

PubMed Central

Xu, Junyi; Yao, Li; Li, Le

2015-01-01

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification. PMID:25966359

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

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.

Ovine mitochondrial DNA sequence variation and its association with production and reproduction traits within an Afec-Assaf flock.

PubMed

Reicher, S; Seroussi, E; Weller, J I; Rosov, A; Gootwine, E

2012-07-01

Polymorphisms in mitochondrial DNA (mtDNA) protein- and tRNA-coding genes were shown to be associated with various diseases in humans as well as with production and reproduction traits in livestock. Alignment of full length mitochondria sequences from the 5 known ovine haplogroups: HA (n = 3), HB (n = 5), HC (n = 3), HD (n = 2), and HE (n = 2; GenBank accession nos. HE577847-50 and 11 published complete ovine mitochondria sequences) revealed sequence variation in 10 out of the 13 protein coding mtDNA sequences. Twenty-six of the 245 variable sites found in the protein coding sequences represent non-synonymous mutations. Sequence variation was observed also in 8 out of the 22 tRNA mtDNA sequences. On the basis of the mtDNA control region and cytochrome b partial sequences along with information on maternal lineages within an Afec-Assaf flock, 1,126 Afec-Assaf ewes were assigned to mitochondrial haplogroups HA, HB, and HC, with frequencies of 0.43, 0.43, and 0.14, respectively. Analysis of birth weight and growth rate records of lamb (n = 1286) and productivity from 4,993 lambing records revealed no association between mitochondrial haplogroup affiliation and female longevity, lambs perinatal survival rate, birth weight, and daily growth rate of lambs up to 150 d that averaged 1,664 d, 88.3%, 4.5 kg, and 320 g/d, respectively. However, significant (P < 0.0001) differences among the haplogroups were found for prolificacy of ewes, with prolificacies (mean ± SE) of 2.14 ± 0.04, 2.25 ± 0.04, and 2.30 ± 0.06 lamb born/ewe lambing for the HA, HB, and the HC haplogroups, respectively. Our results highlight the ovine mitogenome genetic variation in protein- and tRNA coding genes and suggest that sequence variation in ovine mtDNA is associated with variation in ewe prolificacy.

US Army Research Laboratory Visualization Framework Design Document

DTIC Science & Technology

2016-01-01

This section highlights each module in the ARL-VF and subsequent sections provide details on how each module interacts . Fig. 2 ARL-VF with the...ConfigAgent MultiTouch VizDatabase VizController TUIO VizDatabase User VizDaemon VizDaemon VizDaemon VizDaemon VizDaemon TestPoint...received by the destination. The sequence diagram in Fig. 4 shows this interaction . Approved for public release; distribution unlimited. 13 Fig. 4

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.

Adaptive, Distributed Control of Constrained Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Bieniawski, Stefan; Wolpert, David H.

2004-01-01

Product Distribution (PO) theory was recently developed as a broad framework for analyzing and optimizing distributed systems. Here we demonstrate its use for adaptive distributed control of Multi-Agent Systems (MASS), i.e., for distributed stochastic optimization using MAS s. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (Probability dist&&on on the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. One common way to find that equilibrium is to have each agent run a Reinforcement Learning (E) algorithm. PD theory reveals this to be a particular type of search algorithm for minimizing the Lagrangian. Typically that algorithm i s quite inefficient. A more principled alternative is to use a variant of Newton's method to minimize the Lagrangian. Here we compare this alternative to RL-based search in three sets of computer experiments. These are the N Queen s problem and bin-packing problem from the optimization literature, and the Bar problem from the distributed RL literature. Our results confirm that the PD-theory-based approach outperforms the RL-based scheme in all three domains.

Poly(acrylic acid) Bridged Gadolinium Metal-Organic Framework-Gold Nanoparticle Composites as Contrast Agents for Computed Tomography and Magnetic Resonance Bimodal Imaging

PubMed Central

Tian, Chixia; Zhu, Liping; Lin, Feng; Boyes, Stephen G.

2015-01-01

Imaging contrast agents for magnetic resonance imaging (MRI) and computed tomography (CT) have received significant attention in the development of techniques for early-stage cancer diagnosis. Gadolinium (Gd) (III), which has seven unpaired electrons and a large magnetic moment, can dramatically influence the water proton relaxation and hence exhibits excellent MRI contrast. On the other hand, gold (Au), which has a high atomic number and high x-ray attenuation coefficient, is an ideal contrast agent candidate for x-ray based CT imaging. Gd metal organic framework (MOF) nanoparticles with tunable size, high Gd (III) loading and multivalency can potentially overcome the limitations of clinically utilized Gd chelate contrast agents. In this work, we report for the first time the integration of GdMOF nanoparticles with gold nanoparticles (AuNPs) for the preparation of a MRI/CT bimodal imaging agent. Highly stable hybrid GdMOF/AuNPs composites have been prepared by using poly(acrylic acid) as a bridge between the GdMOF nanoparticles and AuNPs. The hybrid nanocomposites were then evaluated in MRI and CT imaging. The results revealed high longitudinal relaxivity in MRI and excellent CT imaging performance. Therefore, these GdMOF/AuNPs hybrid nanocomposites potentially provide a new platform for the development of multi-modal imaging probes. PMID:26147906

Applications of Multi-Agent Technology to Power Systems

NASA Astrophysics Data System (ADS)

Nagata, Takeshi

Currently, agents are focus of intense on many sub-fields of computer science and artificial intelligence. Agents are being used in an increasingly wide variety of applications. Many important computing applications such as planning, process control, communication networks and concurrent systems will benefit from using multi-agent system approach. A multi-agent system is a structure given by an environment together with a set of artificial agents capable to act on this environment. Multi-agent models are oriented towards interactions, collaborative phenomena, and autonomy. This article presents the applications of multi-agent technology to the power systems.

Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

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.

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

IMAGE: A Design Integration Framework Applied to the High Speed Civil Transport

NASA Technical Reports Server (NTRS)

Hale, Mark A.; Craig, James I.

1993-01-01

Effective design of the High Speed Civil Transport requires the systematic application of design resources throughout a product's life-cycle. Information obtained from the use of these resources is used for the decision-making processes of Concurrent Engineering. Integrated computing environments facilitate the acquisition, organization, and use of required information. State-of-the-art computing technologies provide the basis for the Intelligent Multi-disciplinary Aircraft Generation Environment (IMAGE) described in this paper. IMAGE builds upon existing agent technologies by adding a new component called a model. With the addition of a model, the agent can provide accountable resource utilization in the presence of increasing design fidelity. The development of a zeroth-order agent is used to illustrate agent fundamentals. Using a CATIA(TM)-based agent from previous work, a High Speed Civil Transport visualization system linking CATIA, FLOPS, and ASTROS will be shown. These examples illustrate the important role of the agent technologies used to implement IMAGE, and together they demonstrate that IMAGE can provide an integrated computing environment for the design of the High Speed Civil Transport.

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.

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

Virulence gene content in Escherichia coli isolates from poultry flocks with clinical signs of colibacillosis in Brazil.

PubMed

De Carli, Silvia; Ikuta, Nilo; Lehmann, Fernanda Kieling Moreira; da Silveira, Vinicius Proença; de Melo Predebon, Gabriela; Fonseca, André Salvador Kazantzi; Lunge, Vagner Ricardo

2015-11-01

Escherichia coli is a commensal bacterium of the bird's intestinal tract, but it can invade different tissues resulting in systemic symptoms (colibacillosis). This disease occurs only when the E. coli infecting strain presents virulence factors (encoded by specific genes) that enable the adhesion and proliferation in the host organism. Thus, it is important to differentiate pathogenic (APEC, avian pathogenic E. coli) and non-pathogenic or fecal (AFEC, avian fecal E. coli) isolates. Previous studies analyzed the occurrence of virulence factors in E. coli strains isolated from birds with colibacillosis, demonstrating a high frequency of the bacterial genes cvaC, iroN, iss, iutA, sitA, tsh, fyuA, irp-2, ompT and hlyF in pathogenic strains. The aim of the present study was to evaluate the occurrence and frequency of these virulence genes in E. coli isolated from poultry flocks in Brazil. A total of 138 isolates of E. coli was obtained from samples of different tissues and/or organs (spleen, liver, kidney, trachea, lungs, skin, ovary, oviduct, intestine, cloaca) and environmental swabs collected from chicken and turkey flocks suspected to have colibacillosis in farms from the main Brazilian producing regions. Total DNA was extracted and the 10 virulence genes were detected by traditional and/or real-time PCR. At least 11 samples of each gene were sequenced and compared to reference strains. All 10 virulence factors were detected in Brazilian E. coli isolates, with frequencies ranging from 39.9% (irp-2) to 68.8% (hlyF and sitA). Moreover, a high nucleotide similarity (over 99%) was observed between gene sequences of Brazilian isolates and reference strains. Seventy-nine isolates were defined as pathogenic (APEC) and 59 as fecal (AFEC) based on previously described criteria. In conclusion, the main virulence genes of the reference E. coli strains are also present in isolates associated with colibacillosis in Brazil. The analysis of this set of virulence factors can be used to differentiate between APEC and AFEC isolates in Brazil. © 2015 Poultry Science Association Inc.

Multi-agent Reinforcement Learning Model for Effective Action Selection

NASA Astrophysics Data System (ADS)

Youk, Sang Jo; Lee, Bong Keun

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocop Keep away which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

A Distributed Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care

NASA Astrophysics Data System (ADS)

Tapia, Dante I.; RodríGuez, Sara; Corchado, Juan M.

This chapter presents ALZ-MAS (Alzheimer multi-agent system), an ambient intelligence (AmI)-based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients. The system makes use of several context-aware technologies that allow it to automatically obtain information from users and the environment in an evenly distributed way, focusing on the characteristics of ubiquity, awareness, intelligence, mobility, etc., all of which are concepts defined by AmI. ALZ-MAS makes use of a services oriented multi-agent architecture, called flexible user and services oriented multi-agent architecture, to distribute resources and enhance its performance. It is demonstrated that a SOA approach is adequate to build distributed and highly dynamic AmI-based multi-agent systems.

A game theory-reinforcement learning (GT-RL) method to develop optimal operation policies for multi-operator reservoir systems

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Hooshyar, Milad

2014-11-01

Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner's approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system's objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system's beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner's solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory-reinforcement learning (GT-RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT-RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.

On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment.

PubMed

Cicirelli, Franco; Fortino, Giancarlo; Giordano, Andrea; Guerrieri, Antonio; Spezzano, Giandomenico; Vinci, Andrea

2016-09-01

A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.

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.

Frequency of iss and irp2 genes by PCR method in Escherichia coli isolated from poultry with colibacillosis in comparison with healthy chicken in poultry farms of Zabol, South East of Iran.

PubMed

Sadeghi Bonjar, M S; Salari, S; Jahantigh, M; Rashki, A

2017-03-01

There is no special trait for differentiation of Avian Pathogenic Escherichia coli from Avian Fecal Escherichia coli. This investigation is aimed, as a case control study, to evaluate and compare the frequency of iss and irp2 in 43 AFEC strains and also 40 and 56 E. coli strains isolated from the liver and kidney of chickens with colibacillosis, respectively, farmed in Zabol, as a border region of Iran, by PCR. 86.9% and 37.2% of isolates collected from chickens with colibacillosis and feces samples obtained from healthy chickens were positive for iss gene, respectively (P<0.05). On average, 59.3% of E. coli strains isolated from colibacillosis have irp2 gene while 27.9% of isolates from the feces of healthy birds were positive (P<0.05). 52.15% of isolates from colibacillosis and 19.62% of isolates from healthy chicken feces were positive for both genes, with statistical significant difference (p<0.05). This marked difference in the distribution of iss and irp2 genes makes these two genes good markers to differentiate AFEC and APEC strains especially in Sistan region to improve colibacillosis control measurements.

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.

Diversity of Multi-Drug Resistant Avian Pathogenic Escherichia coli (APEC) Causing Outbreaks of Colibacillosis in Broilers during 2012 in Spain.

PubMed

Solà-Ginés, Marc; Cameron-Veas, Karla; Badiola, Ignacio; Dolz, Roser; Majó, Natalia; Dahbi, Ghizlane; Viso, Susana; Mora, Azucena; Blanco, Jorge; Piedra-Carrasco, Nuria; González-López, Juan José; Migura-Garcia, Lourdes

2015-01-01

Avian pathogenic Escherichia coli (APEC) are the major cause of colibacillosis in poultry production. In this study, a total of 22 E. coli isolated from colibacillosis field cases and 10 avian faecal E. coli (AFEC) were analysed. All strains were characterised phenotypically by susceptibility testing and molecular typing methods such as pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing (MLST). The presence of 29 virulence genes associated to APEC and human extraintestinal pathogenic E. coli (ExPEC) was also evaluated. For cephalosporin resistant isolates, cephalosporin resistance genes, plasmid location and replicon typing was assessed. Avian isolates belonged to 26 O:H serotypes and 24 sequence types. Out of 22 APEC isolates, 91% contained the virulence genes predictors of APEC; iutA, hlyF, iss, iroN and ompT. Of all strains, 34% were considered ExPEC. PFGE analysis demonstrated a high degree of genetic polymorphism. All strains were multi-resistant, including those isolated from healthy animals. Eleven strains were resistant to cephalosporins; six contained blaCTX-M-14, two blaSHV-12, two blaCMY-2 and one blaSHV-2. Two strains harboured qnrA, and two qnrA together with aac(6')-Ib-cr. Additionally, the emergent clone O25b:H4-B2-ST131 was isolated from a healthy animal which harboured blaCMY-2 and qnrS genes. Cephalosporin resistant genes were mainly associated to the presence of IncK replicons. This study demonstrates a very diverse population of multi-drug resistant E. coli containing a high number of virulent genes. The E. coli population among broilers is a reservoir of resistance and virulence-associated genes that could be transmitted into the community through the food chain. More epidemiological studies are necessary to identify clonal groups and resistance mechanisms with potential relevance to public health.

Diversity of Multi-Drug Resistant Avian Pathogenic Escherichia coli (APEC) Causing Outbreaks of Colibacillosis in Broilers during 2012 in Spain

PubMed Central

Solà-Ginés, Marc; Cameron-Veas, Karla; Badiola, Ignacio; Dolz, Roser; Majó, Natalia; Dahbi, Ghizlane; Viso, Susana; Mora, Azucena; Blanco, Jorge; Piedra-Carrasco, Nuria; González-López, Juan José; Migura-Garcia, Lourdes

2015-01-01

Avian pathogenic Escherichia coli (APEC) are the major cause of colibacillosis in poultry production. In this study, a total of 22 E. coli isolated from colibacillosis field cases and 10 avian faecal E. coli (AFEC) were analysed. All strains were characterised phenotypically by susceptibility testing and molecular typing methods such as pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing (MLST). The presence of 29 virulence genes associated to APEC and human extraintestinal pathogenic E. coli (ExPEC) was also evaluated. For cephalosporin resistant isolates, cephalosporin resistance genes, plasmid location and replicon typing was assessed. Avian isolates belonged to 26 O:H serotypes and 24 sequence types. Out of 22 APEC isolates, 91% contained the virulence genes predictors of APEC; iutA, hlyF, iss, iroN and ompT. Of all strains, 34% were considered ExPEC. PFGE analysis demonstrated a high degree of genetic polymorphism. All strains were multi-resistant, including those isolated from healthy animals. Eleven strains were resistant to cephalosporins; six contained bla CTX-M-14, two bla SHV-12, two bla CMY-2 and one bla SHV-2. Two strains harboured qnrA, and two qnrA together with aac(6’)-Ib-cr. Additionally, the emergent clone O25b:H4-B2-ST131 was isolated from a healthy animal which harboured bla CMY-2 and qnrS genes. Cephalosporin resistant genes were mainly associated to the presence of IncK replicons. This study demonstrates a very diverse population of multi-drug resistant E. coli containing a high number of virulent genes. The E. coli population among broilers is a reservoir of resistance and virulence-associated genes that could be transmitted into the community through the food chain. More epidemiological studies are necessary to identify clonal groups and resistance mechanisms with potential relevance to public health. PMID:26600205

A Comparative Study of Probability Collectives Based Multi-agent Systems and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Huang, Chien-Feng; Wolpert, David H.; Bieniawski, Stefan; Strauss, Charles E. M.

2005-01-01

We compare Genetic Algorithms (GA's) with Probability Collectives (PC), a new framework for distributed optimization and control. In contrast to GA's, PC-based methods do not update populations of solutions. Instead they update an explicitly parameterized probability distribution p over the space of solutions. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. The PC approach works in both continuous and discrete problems. It does not suffer from the resolution limitation of the finite bit length encoding of parameters into GA alleles. It also has deep connections with both game theory and statistical physics. We review the PC approach using its motivation as the information theoretic formulation of bounded rationality for multi-agent systems. It is then compared with GA's on a diverse set of problems. To handle high dimensional surfaces, in the PC method investigated here p is restricted to a product distribution. Each distribution in that product is controlled by a separate agent. The test functions were selected for their difficulty using either traditional gradient descent or genetic algorithms. On those functions the PC-based approach significantly outperforms traditional GA's in both rate of descent, trapping in false minima, and long term optimization.

Research on the coordination framework for water resources utilization on the interests of mutual compensation in Lancang-Mekong River

NASA Astrophysics Data System (ADS)

Wang, Y.; Fang, D., VI; Xu, J.; Dong, Q.

2017-12-01

The Lancang-Mekong River is an important international river, cascaded hydropower stations development in which attracts the attention of downstream countries. In this paper, we proposed a coordination framework for water resources utilization on the interests of mutual compensation to relieve the conflict of upstream and downstream countries. Firstly, analyze the benefits and risks caused by the cascaded hydropower stations development and the evolution process of water resources use conflict between upstream and downstream countries. Secondly, evaluate the benefits and risks of flood control, water supply, navigation and power generation based on the energy theory of cascaded hydropower stations development in Lancang-Mekong River. Thirdly, multi-agent cooperation motivation and cooperation conditions between upstream and downstream countries in Lancang-Mekong River is given. Finally, the coordination framework for water resources utilization on the interests of mutual compensation in Lancang-Mekong River is presented. This coordination framework for water resources utilization can increase comprehensive benefits in Lancang-Mekong River.

Model of interaction in Smart Grid on the basis of multi-agent system

NASA Astrophysics Data System (ADS)

Engel, E. A.; Kovalev, I. V.; Engel, N. E.

2016-11-01

This paper presents model of interaction in Smart Grid on the basis of multi-agent system. The use of travelling waves in the multi-agent system describes the behavior of the Smart Grid from the local point, which is being the complement of the conventional approach. The simulation results show that the absorption of the wave in the distributed multi-agent systems is effectively simulated the interaction in Smart Grid.

Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange.

PubMed

Hula, Andreas; Montague, P Read; Dayan, Peter

2015-06-01

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference.

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.

Tracking dynamic team activity

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

Tambe, M.

1996-12-31

AI researchers are striving to build complex multi-agent worlds with intended applications ranging from the RoboCup robotic soccer tournaments, to interactive virtual theatre, to large-scale real-world battlefield simulations. Agent tracking - monitoring other agent`s actions and inferring their higher-level goals and intentions - is a central requirement in such worlds. While previous work has mostly focused on tracking individual agents, this paper goes beyond by focusing on agent teams. Team tracking poses the challenge of tracking a team`s joint goals and plans. Dynamic, real-time environments add to the challenge, as ambiguities have to be resolved in real-time. The central hypothesismore » underlying the present work is that an explicit team-oriented perspective enables effective team tracking. This hypothesis is instantiated using the model tracing technology employed in tracking individual agents. Thus, to track team activities, team models are put to service. Team models are a concrete application of the joint intentions framework and enable an agent to track team activities, regardless of the agent`s being a collaborative participant or a non-participant in the team. To facilitate real-time ambiguity resolution with team models: (i) aspects of tracking are cast as constraint satisfaction problems to exploit constraint propagation techniques; and (ii) a cost minimality criterion is applied to constrain tracking search. Empirical results from two separate tasks in real-world, dynamic environments one collaborative and one competitive - are provided.« less

Multi-Agent Patrolling under Uncertainty and Threats.

PubMed

Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D

2015-01-01

We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.

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...

Multi-Agent Information Classification Using Dynamic Acquaintance Lists.

ERIC Educational Resources Information Center

Mukhopadhyay, Snehasis; Peng, Shengquan; Raje, Rajeev; Palakal, Mathew; Mostafa, Javed

2003-01-01

Discussion of automated information services focuses on information classification and collaborative agents, i.e. intelligent computer programs. Highlights include multi-agent systems; distributed artificial intelligence; thesauri; document representation and classification; agent modeling; acquaintances, or remote agents discovered through…

A Bottom-up Vulnerability Analysis of Water Systems with Decentralized Decision Making and Demographic Shifts- the Case of Jordan.

NASA Astrophysics Data System (ADS)

Lachaut, T.; Yoon, J.; Klassert, C. J. A.; Talozi, S.; Mustafa, D.; Knox, S.; Selby, P. D.; Haddad, Y.; Gorelick, S.; Tilmant, A.

2016-12-01

Probabilistic approaches to uncertainty in water systems management can face challenges of several types: non stationary climate, sudden shocks such as conflict-driven migrations, or the internal complexity and dynamics of large systems. There has been a rising trend in the development of bottom-up methods that place focus on the decision side instead of probability distributions and climate scenarios. These approaches are based on defining acceptability thresholds for the decision makers and considering the entire range of possibilities over which such thresholds are crossed. We aim at improving the knowledge on the applicability and relevance of this approach by enlarging its scope beyond climate uncertainty and single decision makers; thus including demographic shifts, internal system dynamics, and multiple stakeholders at different scales. This vulnerability analysis is part of the Jordan Water Project and makes use of an ambitious multi-agent model developed by its teams with the extensive cooperation of the Ministry of Water and Irrigation of Jordan. The case of Jordan is a relevant example for migration spikes, rapid social changes, resource depletion and climate change impacts. The multi-agent modeling framework used provides a consistent structure to assess the vulnerability of complex water resources systems with distributed acceptability thresholds and stakeholder interaction. A proof of concept and preliminary results are presented for a non-probabilistic vulnerability analysis that involves different types of stakeholders, uncertainties other than climatic and the integration of threshold-based indicators. For each stakeholder (agent) a vulnerability matrix is constructed over a multi-dimensional domain, which includes various hydrologic and/or demographic variables.

A comparative review of multi-risk modelling methodologies for climate change adaptation in mountain regions

NASA Astrophysics Data System (ADS)

Terzi, Stefano; Torresan, Silvia; Schneiderbauer, Stefan

2017-04-01

Keywords: Climate change, mountain regions, multi-risk assessment, climate change adaptation. Climate change has already led to a wide range of impacts on the environment, the economy and society. Adaptation actions are needed to cope with the impacts that have already occurred (e.g. storms, glaciers melting, floods, droughts) and to prepare for future scenarios of climate change. Mountain environment is particularly vulnerable to the climate changes due to its exposure to recent climate warming (e.g. water regime changes, thawing of permafrost) and due to the high degree of specialization of both natural and human systems (e.g. alpine species, valley population density, tourism-based economy). As a consequence, the mountain local governments are encouraged to undertake territorial governance policies to climate change, considering multi-risks and opportunities for the mountain economy and identifying the best portfolio of adaptation strategies. This study aims to provide a literature review of available qualitative and quantitative tools, methodological guidelines and best practices to conduct multi-risk assessments in the mountain environment within the context of climate change. We analyzed multi-risk modelling and assessment methods applied in alpine regions (e.g. event trees, Bayesian Networks, Agent Based Models) in order to identify key concepts (exposure, resilience, vulnerability, risk, adaptive capacity), climatic drivers, cause-effect relationships and socio-ecological systems to be integrated in a comprehensive framework. The main outcomes of the review, including a comparison of existing techniques based on different criteria (e.g. scale of analysis, targeted questions, level of complexity) and a snapshot of the developed multi-risk framework for climate change adaptation will be here presented and discussed.

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.

Multi-agent cooperation rescue algorithm based on influence degree and state prediction

NASA Astrophysics Data System (ADS)

Zheng, Yanbin; Ma, Guangfu; Wang, Linlin; Xi, Pengxue

2018-04-01

Aiming at the multi-agent cooperative rescue in disaster, a multi-agent cooperative rescue algorithm based on impact degree and state prediction is proposed. Firstly, based on the influence of the information in the scene on the collaborative task, the influence degree function is used to filter the information. Secondly, using the selected information to predict the state of the system and Agent behavior. Finally, according to the result of the forecast, the cooperative behavior of Agent is guided and improved the efficiency of individual collaboration. The simulation results show that this algorithm can effectively solve the cooperative rescue problem of multi-agent and ensure the efficient completion of the task.

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 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

Concurrent Learning of Control in Multi agent Sequential Decision Tasks

DTIC Science & Technology

2018-04-17

Concurrent Learning of Control in Multi-agent Sequential Decision Tasks The overall objective of this project was to develop multi-agent reinforcement...learning (MARL) approaches for intelligent agents to autonomously learn distributed control policies in decentral- ized partially observable...shall be subject to any oenalty for failing to comply with a collection of information if it does not display a currently valid OMB control number

Guidance and Navigation Software Architecture Design for the Autonomous Multi-Agent Physically Interacting Spacecraft (AMPHIS) Test Bed

DTIC Science & Technology

2006-12-01

NAVIGATION SOFTWARE ARCHITECTURE DESIGN FOR THE AUTONOMOUS MULTI-AGENT PHYSICALLY INTERACTING SPACECRAFT (AMPHIS) TEST BED by Blake D. Eikenberry...Engineer Degree 4. TITLE AND SUBTITLE Guidance and Navigation Software Architecture Design for the Autonomous Multi- Agent Physically Interacting...iii Approved for public release; distribution is unlimited GUIDANCE AND NAVIGATION SOFTWARE ARCHITECTURE DESIGN FOR THE AUTONOMOUS MULTI

Nanoparticles for Biomedical Imaging: Fundamentals of Clinical Translation

PubMed Central

Choi, Hak Soo; Frangioni, John V.

2010-01-01

Because of their large size compared to small molecules, and their multi-functionality, nanoparticles (NPs) hold promise as biomedical imaging, diagnostic, and theragnostic agents. However, the key to their success hinges on a detailed understanding of their behavior after administration into the body. NP biodistribution, target binding, and clearance are a complex function of their physicochemical properties in serum, which include hydrodynamic diameter, solubility, stability, shape and flexibility, surface charge, composition, and formulation. Moreover, many materials used to construct NPs have real or potential toxicity, or may interfere with other medical tests. In this review, we discuss the design considerations that mediate NP behavior in the body and the fundamental principles that govern clinical translation. By analyzing those nanomaterials that have already received regulatory approval, most of which are actually therapeutic agents, we attempt to predict which types of NPs hold potential as diagnostic agents for biomedical imaging. Finally, using quantum dots as an example, we provide a framework for deciding whether an NP-based agent is the best choice for a particular clinical application. PMID:21084027

Distributed consensus for discrete-time heterogeneous multi-agent systems

NASA Astrophysics Data System (ADS)

Zhao, Huanyu; Fei, Shumin

2018-06-01

This paper studies the consensus problem for a class of discrete-time heterogeneous multi-agent systems. Two kinds of consensus algorithms will be considered. The heterogeneous multi-agent systems considered are converted into equivalent error systems by a model transformation. Then we analyse the consensus problem of the original systems by analysing the stability problem of the error systems. Some sufficient conditions for consensus of heterogeneous multi-agent systems are obtained by applying algebraic graph theory and matrix theory. Simulation examples are presented to show the usefulness of the results.

Leaderless consensus for the fractional-order nonlinear multi-agent systems under directed interaction topology

NASA Astrophysics Data System (ADS)

Bai, Jing; Wen, Guoguang; Rahmani, Ahmed

2018-04-01

Leaderless consensus for the fractional-order nonlinear multi-agent systems is investigated in this paper. At the first part, a control protocol is proposed to achieve leaderless consensus for the nonlinear single-integrator multi-agent systems. At the second part, based on sliding mode estimator, a control protocol is given to solve leaderless consensus for the the nonlinear single-integrator multi-agent systems. It shows that the control protocol can improve the systems' convergence speed. At the third part, a control protocol is designed to accomplish leaderless consensus for the nonlinear double-integrator multi-agent systems. To judge the systems' stability in this paper, two classic continuous Lyapunov candidate functions are chosen. Finally, several worked out examples under directed interaction topology are given to prove above results.

Optimal Reward Functions in Distributed Reinforcement Learning

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Tumer, Kagan

2000-01-01

We consider the design of multi-agent systems so as to optimize an overall world utility function when (1) those systems lack centralized communication and control, and (2) each agents runs a distinct Reinforcement Learning (RL) algorithm. A crucial issue in such design problems is to initialize/update each agent's private utility function, so as to induce best possible world utility. Traditional 'team game' solutions to this problem sidestep this issue and simply assign to each agent the world utility as its private utility function. In previous work we used the 'Collective Intelligence' framework to derive a better choice of private utility functions, one that results in world utility performance up to orders of magnitude superior to that ensuing from use of the team game utility. In this paper we extend these results. We derive the general class of private utility functions that both are easy for the individual agents to learn and that, if learned well, result in high world utility. We demonstrate experimentally that using these new utility functions can result in significantly improved performance over that of our previously proposed utility, over and above that previous utility's superiority to the conventional team game utility.

Evolutionary Agent-based Models to design distributed water management strategies

NASA Astrophysics Data System (ADS)

Giuliani, M.; Castelletti, A.; Reed, P. M.

2012-12-01

There is growing awareness in the scientific community that the traditional centralized approach to water resources management, as described in much of the water resources literature, provides an ideal optimal solution, which is certainly useful to quantify the best physically achievable performance, but is generally inapplicable. Most real world water resources management problems are indeed characterized by the presence of multiple, distributed and institutionally-independent decision-makers. Multi-Agent Systems provide a potentially more realistic alternative framework to model multiple and self-interested decision-makers in a credible context. Each decision-maker can be represented by an agent who, being self-interested, acts according to local objective functions and produces negative externalities on system level objectives. Different levels of coordination can potentially be included in the framework by designing coordination mechanisms to drive the current decision-making structure toward the global system efficiency. Yet, the identification of effective coordination strategies can be particularly complex in modern institutional contexts and current practice is dependent on largely ad-hoc coordination strategies. In this work we propose a novel Evolutionary Agent-based Modeling (EAM) framework that enables a mapping of fully uncoordinated and centrally coordinated solutions into their relative "many-objective" tradeoffs using multiobjective evolutionary algorithms. Then, by analysing the conflicts between local individual agent and global system level objectives it is possible to more fully understand the causes, consequences, and potential solution strategies for coordination failures. Game-theoretic criteria have value for identifying the most interesting alternatives from a policy making point of view as well as the coordination mechanisms that can be applied to obtain these interesting solutions. The proposed approach is numerically tested on a synthetic case study, representing a Y-shaped system composed by two regulated lakes, whose releases merge just upstream of a city. Each reservoir is operated by an agent in order to prevent floods along the lake shores (local objective). However, the optimal operation of the reservoirs with respect to the local objectives is conflicting with the minimization of floods in the city (global objective). The evolution of the Agent-based Model from individualistic management strategies of the reservoirs toward a global compromise that reduces the costs for the city is analysed.

Learning Natural Selection in 4th Grade with Multi-Agent-Based Computational Models

ERIC Educational Resources Information Center

Dickes, Amanda Catherine; Sengupta, Pratim

2013-01-01

In this paper, we investigate how elementary school students develop multi-level explanations of population dynamics in a simple predator-prey ecosystem, through scaffolded interactions with a multi-agent-based computational model (MABM). The term "agent" in an MABM indicates individual computational objects or actors (e.g., cars), and these…

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

ERIC Educational Resources Information Center

Gifford, Christopher M.

2009-01-01

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

System design in an evolving system-of-systems architecture and concept of operations

NASA Astrophysics Data System (ADS)

Rovekamp, Roger N., Jr.

Proposals for space exploration architectures have increased in complexity and scope. Constituent systems (e.g., rovers, habitats, in-situ resource utilization facilities, transfer vehicles, etc) must meet the needs of these architectures by performing in multiple operational environments and across multiple phases of the architecture's evolution. This thesis proposes an approach for using system-of-systems engineering principles in conjunction with system design methods (e.g., Multi-objective optimization, genetic algorithms, etc) to create system design options that perform effectively at both the system and system-of-systems levels, across multiple concepts of operations, and over multiple architectural phases. The framework is presented by way of an application problem that investigates the design of power systems within a power sharing architecture for use in a human Lunar Surface Exploration Campaign. A computer model has been developed that uses candidate power grid distribution solutions for a notional lunar base. The agent-based model utilizes virtual control agents to manage the interactions of various exploration and infrastructure agents. The philosophy behind the model is based both on lunar power supply strategies proposed in literature, as well as on the author's own approaches for power distribution strategies of future lunar bases. In addition to proposing a framework for system design, further implications of system-of-systems engineering principles are briefly explored, specifically as they relate to producing more robust cross-cultural system-of-systems architecture solutions.

Faithful teleportation of multi-particle states involving multi spatially remote agents via probabilistic channels

NASA Astrophysics Data System (ADS)

Jiang, Min; Li, Hui; Zhang, Zeng-ke; Zeng, Jia

2011-02-01

We present an approach to faithfully teleport an unknown quantum state of entangled particles in a multi-particle system involving multi spatially remote agents via probabilistic channels. In our scheme, the integrity of an entangled multi-particle state can be maintained even when the construction of a faithful channel fails. Furthermore, in a quantum teleportation network, there are generally multi spatially remote agents which play the role of relay nodes between a sender and a distant receiver. Hence, we propose two schemes for directly and indirectly constructing a faithful channel between the sender and the distant receiver with the assistance of relay agents, respectively. Our results show that the required auxiliary particle resources, local operations and classical communications are considerably reduced for the present purpose.

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.

A Multi-Agent System for Intelligent Online Education.

ERIC Educational Resources Information Center

O'Riordan, Colm; Griffith, Josephine

1999-01-01

Describes the system architecture of an intelligent Web-based education system that includes user modeling agents, information filtering agents for automatic information gathering, and the multi-agent interaction. Discusses information management; user interaction; support for collaborative peer-peer learning; implementation; testing; and future…

Verifying Multi-Agent Systems via Unbounded Model Checking

NASA Technical Reports Server (NTRS)

Kacprzak, M.; Lomuscio, A.; Lasica, T.; Penczek, W.; Szreter, M.

2004-01-01

We present an approach to the problem of verification of epistemic properties in multi-agent systems by means of symbolic model checking. In particular, it is shown how to extend the technique of unbounded model checking from a purely temporal setting to a temporal-epistemic one. In order to achieve this, we base our discussion on interpreted systems semantics, a popular semantics used in multi-agent systems literature. We give details of the technique and show how it can be applied to the well known train, gate and controller problem. Keywords: model checking, unbounded model checking, multi-agent systems

An integrated decision-making framework for transportation architectures: Application to aviation systems design

NASA Astrophysics Data System (ADS)

Lewe, Jung-Ho

The National Transportation System (NTS) is undoubtedly a complex system-of-systems---a collection of diverse 'things' that evolve over time, organized at multiple levels, to achieve a range of possibly conflicting objectives, and never quite behaving as planned. The purpose of this research is to develop a virtual transportation architecture for the ultimate goal of formulating an integrated decision-making framework. The foundational endeavor begins with creating an abstraction of the NTS with the belief that a holistic frame of reference is required to properly study such a multi-disciplinary, trans-domain system. The culmination of the effort produces the Transportation Architecture Field (TAF) as a mental model of the NTS, in which the relationships between four basic entity groups are identified and articulated. This entity-centric abstraction framework underpins the construction of a virtual NTS couched in the form of an agent-based model. The transportation consumers and the service providers are identified as adaptive agents that apply a set of preprogrammed behavioral rules to achieve their respective goals. The transportation infrastructure and multitude of exogenous entities (disruptors and drivers) in the whole system can also be represented without resorting to an extremely complicated structure. The outcome is a flexible, scalable, computational model that allows for examination of numerous scenarios which involve the cascade of interrelated effects of aviation technology, infrastructure, and socioeconomic changes throughout the entire system.

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.

Modeling of a production system using the multi-agent approach

NASA Astrophysics Data System (ADS)

Gwiazda, A.; Sękala, A.; Banaś, W.

2017-08-01

The method that allows for the analysis of complex systems is a multi-agent simulation. The multi-agent simulation (Agent-based modeling and simulation - ABMS) is modeling of complex systems consisting of independent agents. In the case of the model of the production system agents may be manufactured pieces set apart from other types of agents like machine tools, conveyors or replacements stands. Agents are magazines and buffers. More generally speaking, the agents in the model can be single individuals, but you can also be defined as agents of collective entities. They are allowed hierarchical structures. It means that a single agent could belong to a certain class. Depending on the needs of the agent may also be a natural or physical resource. From a technical point of view, the agent is a bundle of data and rules describing its behavior in different situations. Agents can be autonomous or non-autonomous in making the decision about the types of classes of agents, class sizes and types of connections between elements of the system. Multi-agent modeling is a very flexible technique for modeling and model creating in the convention that could be adapted to any research problem analyzed from different points of views. One of the major problems associated with the organization of production is the spatial organization of the production process. Secondly, it is important to include the optimal scheduling. For this purpose use can approach multi-purposeful. In this regard, the model of the production process will refer to the design and scheduling of production space for four different elements. The program system was developed in the environment NetLogo. It was also used elements of artificial intelligence. The main agent represents the manufactured pieces that, according to previously assumed rules, generate the technological route and allow preprint the schedule of that line. Machine lines, reorientation stands, conveyors and transport devices also represent the other type of agent that are utilized in the described simulation. The article presents the idea of an integrated program approach and shows the resulting production layout as a virtual model. This model was developed in the NetLogo multi-agent program environment.

Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning

NASA Astrophysics Data System (ADS)

Li, Xue-yan; Li, Xue-mei; Yang, Lingrun; Li, Jing

2018-07-01

Most of the previous studies on dynamic traffic assignment are based on traditional analytical framework, for instance, the idea of Dynamic User Equilibrium has been widely used in depicting both the route choice and the departure time choice. However, some recent studies have demonstrated that the dynamic traffic flow assignment largely depends on travelers' rationality degree, travelers' heterogeneity and what the traffic information the travelers have. In this paper, we develop a new self-adaptive multi agent model to depict travelers' behavior in Dynamic Traffic Assignment. We use Cumulative Prospect Theory with heterogeneous reference points to illustrate travelers' bounded rationality. We use reinforcement-learning model to depict travelers' route and departure time choosing behavior under the condition of imperfect information. We design the evolution rule of travelers' expected arrival time and the algorithm of traffic flow assignment. Compared with the traditional model, the self-adaptive multi agent model we proposed in this paper can effectively help travelers avoid the rush hour. Finally, we report and analyze the effect of travelers' group behavior on the transportation system, and give some insights into the relation between travelers' group behavior and the performance of transportation system.

Massive Multi-Agent Systems Control

NASA Technical Reports Server (NTRS)

Campagne, Jean-Charles; Gardon, Alain; Collomb, Etienne; Nishida, Toyoaki

2004-01-01

In order to build massive multi-agent systems, considered as complex and dynamic systems, one needs a method to analyze and control the system. We suggest an approach using morphology to represent and control the state of large organizations composed of a great number of light software agents. Morphology is understood as representing the state of the multi-agent system as shapes in an abstract geometrical space, this notion is close to the notion of phase space in physics.

Social opinion dynamics is not chaotic

NASA Astrophysics Data System (ADS)

Lim, Chjan; Zhang, Weituo

2016-08-01

Motivated by the research on social opinion dynamics over large and dense networks, a general framework for verifying the monotonicity property of multi-agent dynamics is introduced. This allows a derivation of sociologically meaningful sufficient conditions for monotonicity that are tailor-made for social opinion dynamics, which typically have high nonlinearity. A direct consequence of monotonicity is that social opinion dynamics is nonchaotic. A key part of this framework is the definition of a partial order relation that is suitable for a large class of social opinion dynamics such as the generalized naming games. Comparisons are made to previous techniques to verify monotonicity. Using the results obtained, we extend many of the consequences of monotonicity to this class of social dynamics, including several corollaries on their asymptotic behavior, such as global convergence to consensus and tipping points of a minority fraction of zealots or leaders.

75 FR 23223 - Multi-Agency Informational Meeting Concerning Compliance With the Federal Select Agent Program...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-03

...] Multi-Agency Informational Meeting Concerning Compliance With the Federal Select Agent Program; Public... Select Agent Program established under the Public Health Security and Bioterrorism Preparedness and... Roberson, Veterinary Permit Examiner, APHIS Select Agent Program, VS, ASAP, APHIS, 4700 River Road Unit 2...

76 FR 14896 - Multi-Agency Informational Meeting Concerning Compliance With the Federal Select Agent Program...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-18

...] Multi-Agency Informational Meeting Concerning Compliance With the Federal Select Agent Program; Public... specific regulatory guidance related to the Federal Select Agent Program established under the Public.... Sarah Kwiatkowski, Veterinary Program Assistant, APHIS Select Agent Program, APHIS, 4700 River Road Unit...

76 FR 17617 - Multi-Agency Informational Meeting Concerning Compliance With the Federal Select Agent Program...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-30

...] Multi-Agency Informational Meeting Concerning Compliance With the Federal Select Agent Program; Public... specific regulatory guidance related to the Federal Select Agent Program established under the Public.... Sarah Kwiatkowski, Veterinary Program Assistant, APHIS Select Agent Program, APHIS, 4700 River Road Unit...

Research and Implementation of Key Technologies in Multi-Agent System to Support Distributed Workflow

NASA Astrophysics Data System (ADS)

Pan, Tianheng

2018-01-01

In recent years, the combination of workflow management system and Multi-agent technology is a hot research field. The problem of lack of flexibility in workflow management system can be improved by introducing multi-agent collaborative management. The workflow management system adopts distributed structure. It solves the problem that the traditional centralized workflow structure is fragile. In this paper, the agent of Distributed workflow management system is divided according to its function. The execution process of each type of agent is analyzed. The key technologies such as process execution and resource management are analyzed.

Conflict resolution in multi-agent hybrid systems

DOT National Transportation Integrated Search

1996-12-01

A conflict resolution architecture for multi-agent hybrid systems with emphasis on Air Traffic Management Systems (ATMS) is presented. In such systems, conflicts arise in the form of potential collisions which are resolved locally by inter-agent coor...

Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

PubMed Central

Ma, Da; Cardoso, Manuel J.; Modat, Marc; Powell, Nick; Wells, Jack; Holmes, Holly; Wiseman, Frances; Tybulewicz, Victor; Fisher, Elizabeth; Lythgoe, Mark F.; Ourselin, Sébastien

2014-01-01

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework. PMID:24475148

Application of agent-based system for bioprocess description and process improvement.

PubMed

Gao, Ying; Kipling, Katie; Glassey, Jarka; Willis, Mark; Montague, Gary; Zhou, Yuhong; Titchener-Hooker, Nigel J

2010-01-01

Modeling plays an important role in bioprocess development for design and scale-up. Predictive models can also be used in biopharmaceutical manufacturing to assist decision-making either to maintain process consistency or to identify optimal operating conditions. To predict the whole bioprocess performance, the strong interactions present in a processing sequence must be adequately modeled. Traditionally, bioprocess modeling considers process units separately, which makes it difficult to capture the interactions between units. In this work, a systematic framework is developed to analyze the bioprocesses based on a whole process understanding and considering the interactions between process operations. An agent-based approach is adopted to provide a flexible infrastructure for the necessary integration of process models. This enables the prediction of overall process behavior, which can then be applied during process development or once manufacturing has commenced, in both cases leading to the capacity for fast evaluation of process improvement options. The multi-agent system comprises a process knowledge base, process models, and a group of functional agents. In this system, agent components co-operate with each other in performing their tasks. These include the description of the whole process behavior, evaluating process operating conditions, monitoring of the operating processes, predicting critical process performance, and providing guidance to decision-making when coping with process deviations. During process development, the system can be used to evaluate the design space for process operation. During manufacture, the system can be applied to identify abnormal process operation events and then to provide suggestions as to how best to cope with the deviations. In all cases, the function of the system is to ensure an efficient manufacturing process. The implementation of the agent-based approach is illustrated via selected application scenarios, which demonstrate how such a framework may enable the better integration of process operations by providing a plant-wide process description to facilitate process improvement. Copyright 2009 American Institute of Chemical Engineers

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Trajectory Control of Scale-Free Dynamical Networks with Exogenous Disturbances

NASA Astrophysics Data System (ADS)

Yang, Hong-Yong; Zhang, Shun; Zong, Guang-Deng

2011-01-01

In this paper, the trajectory control of multi-agent dynamical systems with exogenous disturbances is studied. Suppose multiple agents composing of a scale-free network topology, the performance of rejecting disturbances for the low degree node and high degree node is analyzed. Firstly, the consensus of multi-agent systems without disturbances is studied by designing a pinning control strategy on a part of agents, where this pinning control can bring multiple agents' states to an expected consensus track. Then, the influence of the disturbances is considered by developing disturbance observers, and disturbance observers based control (DOBC) are developed for disturbances generated by an exogenous system to estimate the disturbances. Asymptotical consensus of the multi-agent systems with disturbances under the composite controller can be achieved for scale-free network topology. Finally, by analyzing examples of multi-agent systems with scale-free network topology and exogenous disturbances, the verities of the results are proved. Under the DOBC with the designed parameters, the trajectory convergence of multi-agent systems is researched by pinning two class of the nodes. We have found that it has more stronger robustness to exogenous disturbances for the high degree node pinned than that of the low degree node pinned.

Proposed Methodology for Application of Human-like gradual Multi-Agent Q-Learning (HuMAQ) for Multi-robot Exploration

NASA Astrophysics Data System (ADS)

Narayan Ray, Dip; Majumder, Somajyoti

2014-07-01

Several attempts have been made by the researchers around the world to develop a number of autonomous exploration techniques for robots. But it has been always an important issue for developing the algorithm for unstructured and unknown environments. Human-like gradual Multi-agent Q-leaming (HuMAQ) is a technique developed for autonomous robotic exploration in unknown (and even unimaginable) environments. It has been successfully implemented in multi-agent single robotic system. HuMAQ uses the concept of Subsumption architecture, a well-known Behaviour-based architecture for prioritizing the agents of the multi-agent system and executes only the most common action out of all the different actions recommended by different agents. Instead of using new state-action table (Q-table) each time, HuMAQ uses the immediate past table for efficient and faster exploration. The proof of learning has also been established both theoretically and practically. HuMAQ has the potential to be used in different and difficult situations as well as applications. The same architecture has been modified to use for multi-robot exploration in an environment. Apart from all other existing agents used in the single robotic system, agents for inter-robot communication and coordination/ co-operation with the other similar robots have been introduced in the present research. Current work uses a series of indigenously developed identical autonomous robotic systems, communicating with each other through ZigBee protocol.

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

DTIC Science & Technology

2009-03-01

Normal hierarchy vs entangled hierarchy 2.5.7 Quantifying Entangledness . While self organization means that the swarm develops a consistent structure of...flexibility due to centralization of control and com- munication. Thus, self organized, entangled hierarchy multi-agent swarms are evolved in this study to...technique. The resulting design exhibits a self organized multi-agent swarm (SOMAS) with entangled hierarchical control and communication through the

Distributed Market-Based Algorithms for Multi-Agent Planning with Shared Resources

DTIC Science & Technology

2013-02-01

1 Introduction 1 2 Distributed Market-Based Multi-Agent Planning 5 2.1 Problem Formulation...over the deterministic planner, on the “test set” of scenarios with changing economies. . . 50 xi xii Chapter 1 Introduction Multi-agent planning is...representation of the objective (4.2.1). For example, for the supply chain mangement problem, we assumed a sequence of Bernoulli coin flips, which seems

Research of negotiation in network trade system based on multi-agent

NASA Astrophysics Data System (ADS)

Cai, Jun; Wang, Guozheng; Wu, Haiyan

2009-07-01

A construction and implementation technology of network trade based on multi-agent is described in this paper. First, we researched the technology of multi-agent, then we discussed the consumer's behaviors and the negotiation between purchaser and bargainer which emerges in the traditional business mode and analysed the key technology to implement the network trade system. Finally, we implement the system.

Agents Control in Intelligent Learning Systems: The Case of Reactive Characteristics

ERIC Educational Resources Information Center

Laureano-Cruces, Ana Lilia; Ramirez-Rodriguez, Javier; de Arriaga, Fernando; Escarela-Perez, Rafael

2006-01-01

Intelligent learning systems (ILSs) have evolved in the last few years basically because of influences received from multi-agent architectures (MAs). Conflict resolution among agents has been a very important problem for multi-agent systems, with specific features in the case of ILSs. The literature shows that ILSs with cognitive or pedagogical…

Quicker Q-Learning in Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian K.; Tumer, Kagan

2005-01-01

Multi-agent learning in Markov Decisions Problems is challenging because of the presence ot two credit assignment problems: 1) How to credit an action taken at time step t for rewards received at t' greater than t; and 2) How to credit an action taken by agent i considering the system reward is a function of the actions of all the agents. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning OK TD(lambda) The second credit assi,onment problem is typically addressed either by hand-crafting reward functions that assign proper credit to an agent, or by making certain independence assumptions about an agent's state-space and reward function. To address both credit assignment problems simultaneously, we propose the Q Updates with Immediate Counterfactual Rewards-learning (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. Instead of assuming that an agent s value function can be made independent of other agents, this method suppresses the impact of other agents using counterfactual rewards. Results on multi-agent grid-world problems over multiple topologies show that QUICR-learning can achieve up to thirty fold improvements in performance over both conventional and local Q-learning in the largest tested systems.

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.

Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.

PubMed

Asman, Andrew J; Huo, Yuankai; Plassard, Andrew J; Landman, Bennett A

2015-12-01

We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on fusing local learners. In the largest whole-brain multi-atlas study yet reported, multi-atlas segmentations are estimated for a training set of 3464 MR brain images. Using these multi-atlas estimates we (1) estimate a low-dimensional representation for selecting locally appropriate example images, and (2) build AdaBoost learners that map a weak initial segmentation to the multi-atlas segmentation result. Thus, to segment a new target image we project the image into the low-dimensional space, construct a weak initial segmentation, and fuse the trained, locally selected, learners. The MLF framework cuts the runtime on a modern computer from 36 h down to 3-8 min - a 270× speedup - by completely bypassing the need for deformable atlas-target registrations. Additionally, we (1) describe a technique for optimizing the weak initial segmentation and the AdaBoost learning parameters, (2) quantify the ability to replicate the multi-atlas result with mean accuracies approaching the multi-atlas intra-subject reproducibility on a testing set of 380 images, (3) demonstrate significant increases in the reproducibility of intra-subject segmentations when compared to a state-of-the-art multi-atlas framework on a separate reproducibility dataset, (4) show that under the MLF framework the large-scale data model significantly improve the segmentation over the small-scale model under the MLF framework, and (5) indicate that the MLF framework has comparable performance as state-of-the-art multi-atlas segmentation algorithms without using non-local information. Copyright © 2015 Elsevier B.V. All rights reserved.

Based on a multi-agent system for multi-scale simulation and application of household's LUCC: a case study for Mengcha village, Mizhi county, Shaanxi province.

PubMed

Chen, Hai; Liang, Xiaoying; Li, Rui

2013-01-01

Multi-Agent Systems (MAS) offer a conceptual approach to include multi-actor decision making into models of land use change. Through the simulation based on the MAS, this paper tries to show the application of MAS in the micro scale LUCC, and reveal the transformation mechanism of difference scale. This paper starts with a description of the context of MAS research. Then, it adopts the Nested Spatial Choice (NSC) method to construct the multi-scale LUCC decision-making model. And a case study for Mengcha village, Mizhi County, Shaanxi Province is reported. Finally, the potentials and drawbacks of the following approach is discussed and concluded. From our design and implementation of the MAS in multi-scale model, a number of observations and conclusions can be drawn on the implementation and future research directions. (1) The use of the LUCC decision-making and multi-scale transformation framework provides, according to us, a more realistic modeling of multi-scale decision making process. (2) By using continuous function, rather than discrete function, to construct the decision-making of the households is more realistic to reflect the effect. (3) In this paper, attempts have been made to give a quantitative analysis to research the household interaction. And it provides the premise and foundation for researching the communication and learning among the households. (4) The scale transformation architecture constructed in this paper helps to accumulate theory and experience for the interaction research between the micro land use decision-making and the macro land use landscape pattern. Our future research work will focus on: (1) how to rational use risk aversion principle, and put the rule on rotation between household parcels into model. (2) Exploring the methods aiming at researching the household decision-making over a long period, it allows us to find the bridge between the long-term LUCC data and the short-term household decision-making. (3) Researching the quantitative method and model, especially the scenario analysis model which may reflect the interaction among different household types.

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.

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).

Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection

NASA Astrophysics Data System (ADS)

Hashimoto, Ryoji; Matsumura, Tomoya; Nozato, Yoshihiro; Watanabe, Kenji; Onoye, Takao

A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640×512 pixel input images can be processed in real-time with three agents at a rate of 9fps in 48MHz operation.

A Quantum Approach to Multi-Agent Systems (MAS), Organizations, and Control

DTIC Science & Technology

2003-06-01

interdependent interactions between individuals represented approximately as vocal harmonic I resonators. Then the growth rate of an organization fits ...A quantum approach to multi-agent systems (MAS), organizations , and control W.F. Lawless Paine College 1235 15th Street Augusta, GA 30901...AND SUBTITLE A quantum approach to multi-agent systems (MAS), organizations , and control 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

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.

Shared investment projects and forecasting errors: setting framework conditions for coordination and sequencing data quality activities.

PubMed

Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

2015-01-01

In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments' efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that-in some setups-a certain extent of misforecasting is desirable from the firm's point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that-in particular for relatively good forecasters-most of our results are robust to changes in setting the parameters of our multi-agent simulation model.

Shared Investment Projects and Forecasting Errors: Setting Framework Conditions for Coordination and Sequencing Data Quality Activities

PubMed Central

Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

2015-01-01

In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments’ efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that—in some setups—a certain extent of misforecasting is desirable from the firm’s point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that—in particular for relatively good forecasters—most of our results are robust to changes in setting the parameters of our multi-agent simulation model. PMID:25803736

Multimedia-modeling integration development environment

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

Pelton, Mitchell A.; Hoopes, Bonnie L.

2002-09-02

There are many framework systems available; however, the purpose of the framework presented here is to capitalize on the successes of the Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES) and Multi-media Multi-pathway Multi-receptor Risk Assessment (3MRA) methodology as applied to the Hazardous Waste Identification Rule (HWIR) while focusing on the development of software tools to simplify the module developer?s effort of integrating a module into the framework.

Towards an agent based traffic regulation and recommendation system for the on-road air quality control.

PubMed

Sadiq, Abderrahmane; El Fazziki, Abdelaziz; Ouarzazi, Jamal; Sadgal, Mohamed

2016-01-01

This paper presents an integrated and adaptive problem-solving approach to control the on-road air quality by modeling the road infrastructure, managing traffic based on pollution level and generating recommendations for road users. The aim is to reduce vehicle emissions in the most polluted road segments and optimizing the pollution levels. For this we propose the use of historical and real time pollution records and contextual data to calculate the air quality index on road networks and generate recommendations for reassigning traffic flow in order to improve the on-road air quality. The resulting air quality indexes are used in the system's traffic network generation, which the cartography is represented by a weighted graph. The weights evolve according to the pollution indexes and path properties and the graph is therefore dynamic. Furthermore, the systems use the available pollution data and meteorological records in order to predict the on-road pollutant levels by using an artificial neural network based prediction model. The proposed approach combines the benefits of multi-agent systems, Big data technology, machine learning tools and the available data sources. For the shortest path searching in the road network, we use the Dijkstra algorithm over Hadoop MapReduce framework. The use Hadoop framework in the data retrieve and analysis process has significantly improved the performance of the proposed system. Also, the agent technology allowed proposing a suitable solution in terms of robustness and agility.

The application of statistical mechanics on the study of glassy behaviors in transportation networks and dynamics in models of financial markets

NASA Astrophysics Data System (ADS)

Yeung, Chi Ho

In this thesis, we study two interdisciplinary problems in the framework of statistical physics, which show the broad applicability of physics on problems with various origins. The first problem corresponds to an optimization problem in allocating resources on random regular networks. Frustrations arise from competition for resources. When the initial resources are uniform, different regimes with discrete fractions of satisfied nodes are observed, resembling the Devil's staircase. We apply the spin glass theory in analyses and demonstrate how functional recursions are converted to simple recursions of probabilities. Equilibrium properties such as the average energy and the fraction of free nodes are derived. When the initial resources are bimodally distributed, increases in the fraction of rich nodes induce a glassy transition, entering a glassy phase described by the existence of multiple metastable states, in which we employ the replica symmetry breaking ansatz for analysis. The second problem corresponds to the study of multi-agent systems modeling financial markets. Agents in the system trade among themselves, and self-organize to produce macroscopic trading behaviors resembling the real financial markets. These behaviors include the arbitraging activities, the setting up and the following of price trends. A phase diagram of these behaviors is obtained, as a function of the sensitivity of price and the market impact factor. We finally test the applicability of the models with real financial data including the Hang Seng Index, the Nasdaq Composite and the Dow Jones Industrial Average. A substantial fraction of agents gains faster than the inflation rate of the indices, suggesting the possibility of using multi-agent systems as a tool for real trading.

Organization of the secure distributed computing based on multi-agent system

NASA Astrophysics Data System (ADS)

Khovanskov, Sergey; Rumyantsev, Konstantin; Khovanskova, Vera

2018-04-01

Nowadays developing methods for distributed computing is received much attention. One of the methods of distributed computing is using of multi-agent systems. The organization of distributed computing based on the conventional network computers can experience security threats performed by computational processes. Authors have developed the unified agent algorithm of control system of computing network nodes operation. Network PCs is used as computing nodes. The proposed multi-agent control system for the implementation of distributed computing allows in a short time to organize using of the processing power of computers any existing network to solve large-task by creating a distributed computing. Agents based on a computer network can: configure a distributed computing system; to distribute the computational load among computers operated agents; perform optimization distributed computing system according to the computing power of computers on the network. The number of computers connected to the network can be increased by connecting computers to the new computer system, which leads to an increase in overall processing power. Adding multi-agent system in the central agent increases the security of distributed computing. This organization of the distributed computing system reduces the problem solving time and increase fault tolerance (vitality) of computing processes in a changing computing environment (dynamic change of the number of computers on the network). Developed a multi-agent system detects cases of falsification of the results of a distributed system, which may lead to wrong decisions. In addition, the system checks and corrects wrong results.

Emergence of grouping in multi-resource minority game dynamics

NASA Astrophysics Data System (ADS)

Huang, Zi-Gang; Zhang, Ji-Qiang; Dong, Jia-Qi; Huang, Liang; Lai, Ying-Cheng

2012-10-01

Complex systems arising in a modern society typically have many resources and strategies available for their dynamical evolutions. To explore quantitatively the behaviors of such systems, we propose a class of models to investigate Minority Game (MG) dynamics with multiple strategies. In particular, agents tend to choose the least used strategies based on available local information. A striking finding is the emergence of grouping states defined in terms of distinct strategies. We develop an analytic theory based on the mean-field framework to understand the ``bifurcations'' of the grouping states. The grouping phenomenon has also been identified in the Shanghai Stock-Market system, and we discuss its prevalence in other real-world systems. Our work demonstrates that complex systems obeying the MG rules can spontaneously self-organize themselves into certain divided states, and our model represents a basic and general mathematical framework to address this kind of phenomena in social, economical and political systems.

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.

Multi-Agent Strategic Modeling in a Specific Environment

NASA Astrophysics Data System (ADS)

Gams, Matjaz; Bezek, Andraz

Multi-agent modeling in ambient intelligence (AmI) is concerned with the following task [19]: How can external observations of multi-agent systems in the ambient be used to analyze, model, and direct agent behavior? The main purpose is to obtain knowledge about acts in the environment thus enabling proper actions of the AmI systems [1]. Analysis of such systems must thus capture complex world state representation and asynchronous agent activities. Instead of studying basic numerical data, researchers often use more complex data structures, such as rules and decision trees. Some methods are extremely useful when characterizing state space, but lack the ability to clearly represent temporal state changes occurred by agent actions. To comprehend simultaneous agent actions and complex changes of state space, most often a combination of graphical and symbolical representation performs better in terms of human understanding and performance.

Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system.

PubMed

Shen, Ying; Colloc, Joël; Jacquet-Andrieu, Armelle; Lei, Kai

2015-08-01

This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts. Copyright © 2015 Elsevier Inc. All rights reserved.

Adaptive tracking control of leader-following linear multi-agent systems with external disturbances

NASA Astrophysics Data System (ADS)

Lin, Hanquan; Wei, Qinglai; Liu, Derong; Ma, Hongwen

2016-10-01

In this paper, the consensus problem for leader-following linear multi-agent systems with external disturbances is investigated. Brownian motions are used to describe exogenous disturbances. A distributed tracking controller based on Riccati inequalities with an adaptive law for adjusting coupling weights between neighbouring agents is designed for leader-following multi-agent systems under fixed and switching topologies. In traditional distributed static controllers, the coupling weights depend on the communication graph. However, coupling weights associated with the feedback gain matrix in our method are updated by state errors between neighbouring agents. We further present the stability analysis of leader-following multi-agent systems with stochastic disturbances under switching topology. Most traditional literature requires the graph to be connected all the time, while the communication graph is only assumed to be jointly connected in this paper. The design technique is based on Riccati inequalities and algebraic graph theory. Finally, simulations are given to show the validity of our method.

Multi-objective optimal dispatch of distributed energy resources

NASA Astrophysics Data System (ADS)

Longe, Ayomide

This thesis is composed of two papers which investigate the optimal dispatch for distributed energy resources. In the first paper, an economic dispatch problem for a community microgrid is studied. In this microgrid, each agent pursues an economic dispatch for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, a simple market structure is introduced as a framework for energy trades in a small community microgrid such as the Solar Village. It was found that both sellers and buyers benefited by participating in this market. In the second paper, Semidefinite Programming (SDP) for convex relaxation of power flow equations is used for optimal active and reactive dispatch for Distributed Energy Resources (DER). Various objective functions including voltage regulation, reduced transmission line power losses, and minimized reactive power charges for a microgrid are introduced. Combinations of these goals are attained by solving a multiobjective optimization for the proposed ORPD problem. Also, both centralized and distributed versions of this optimal dispatch are investigated. It was found that SDP made the optimal dispatch faster and distributed solution allowed for scalability.

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…

Controllability of multi-agent systems with time-delay in state and switching topology

NASA Astrophysics Data System (ADS)

Ji, Zhijian; Wang, Zidong; Lin, Hai; Wang, Zhen

2010-02-01

In this article, the controllability issue is addressed for an interconnected system of multiple agents. The network associated with the system is of the leader-follower structure with some agents taking leader role and others being followers interconnected via the neighbour-based rule. Sufficient conditions are derived for the controllability of multi-agent systems with time-delay in state, as well as a graph-based uncontrollability topology structure is revealed. Both single and double integrator dynamics are considered. For switching topology, two algebraic necessary and sufficient conditions are derived for the controllability of multi-agent systems. Several examples are also presented to illustrate how to control the system to shape into the desired configurations.

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.

Autonomous Decentralized Voltage Profile Control of Super Distributed Energy System using Multi-agent Technology

NASA Astrophysics Data System (ADS)

Tsuji, Takao; Hara, Ryoichi; Oyama, Tsutomu; Yasuda, Keiichiro

A super distributed energy system is a future energy system in which the large part of its demand is fed by a huge number of distributed generators. At one time some nodes in the super distributed energy system behave as load, however, at other times they behave as generator - the characteristic of each node depends on the customers' decision. In such situation, it is very difficult to regulate voltage profile over the system due to the complexity of power flows. This paper proposes a novel control method of distributed generators that can achieve the autonomous decentralized voltage profile regulation by using multi-agent technology. The proposed multi-agent system employs two types of agent; a control agent and a mobile agent. Control agents generate or consume reactive power to regulate the voltage profile of neighboring nodes and mobile agents transmit the information necessary for VQ-control among the control agents. The proposed control method is tested through numerical simulations.

Toward a theory of organisms: Three founding principles in search of a useful integration

PubMed Central

SOTO, ANA M.; LONGO, GIUSEPPE; MIQUEL, PAUL-ANTOINE; MONTEVIL, MAËL; MOSSIO, MATTEO; PERRET, NICOLE; POCHEVILLE, ARNAUD; SONNENSCHEIN, CARLOS

2016-01-01

Organisms, be they uni- or multi-cellular, are agents capable of creating their own norms; they are continuously harmonizing their ability to create novelty and stability, that is, they combine plasticity with robustness. Here we articulate the three principles for a theory of organisms proposed in this issue, namely: the default state of proliferation with variation and motility, the principle of variation and the principle of organization. These principles profoundly change both biological observables and their determination with respect to the theoretical framework of physical theories. This radical change opens up the possibility of anchoring mathematical modeling in biologically proper principles. PMID:27498204

Research and application of multi-agent genetic algorithm in tower defense game

NASA Astrophysics Data System (ADS)

Jin, Shaohua

2018-04-01

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game's monster.

Coordination of fractional-order nonlinear multi-agent systems via distributed impulsive control

NASA Astrophysics Data System (ADS)

Ma, Tiedong; Li, Teng; Cui, Bing

2018-01-01

The coordination of fractional-order nonlinear multi-agent systems via distributed impulsive control method is studied in this paper. Based on the theory of impulsive differential equations, algebraic graph theory, Lyapunov stability theory and Mittag-Leffler function, two novel sufficient conditions for achieving the cooperative control of a class of fractional-order nonlinear multi-agent systems are derived. Finally, two numerical simulations are verified to illustrate the effectiveness and feasibility of the proposed method.

Multiagent pursuit-evasion games: Algorithms and experiments

NASA Astrophysics Data System (ADS)

Kim, Hyounjin

Deployment of intelligent agents has been made possible through advances in control software, microprocessors, sensor/actuator technology, communication technology, and artificial intelligence. Intelligent agents now play important roles in many applications where human operation is too dangerous or inefficient. There is little doubt that the world of the future will be filled with intelligent robotic agents employed to autonomously perform tasks, or embedded in systems all around us, extending our capabilities to perceive, reason and act, and replacing human efforts. There are numerous real-world applications in which a single autonomous agent is not suitable and multiple agents are required. However, after years of active research in multi-agent systems, current technology is still far from achieving many of these real-world applications. Here, we consider the problem of deploying a team of unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV) to pursue a second team of UGV evaders while concurrently building a map in an unknown environment. This pursuit-evasion game encompasses many of the challenging issues that arise in operations using intelligent multi-agent systems. We cast the problem in a probabilistic game theoretic framework and consider two computationally feasible pursuit policies: greedy and global-max. We also formulate this probabilistic pursuit-evasion game as a partially observable Markov decision process and employ a policy search algorithm to obtain a good pursuit policy from a restricted class of policies. The estimated value of this policy is guaranteed to be uniformly close to the optimal value in the given policy class under mild conditions. To implement this scenario on real UAVs and UGVs, we propose a distributed hierarchical hybrid system architecture which emphasizes the autonomy of each agent yet allows for coordinated team efforts. We then describe our implementation on a fleet of UGVs and UAVs, detailing components such as high level pursuit policy computation, inter-agent communication, navigation, sensing, and regulation. We present both simulation and experimental results on real pursuit-evasion games between our fleet of UAVs and UGVs and evaluate the pursuit policies, relating expected capture times to the speed and intelligence of the evaders and the sensing capabilities of the pursuers. The architecture and algorithmsis described in this dissertation are general enough to be applied to many real-world applications.

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 flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological interactions.

PubMed

He, Chenlong; Feng, Zuren; Ren, Zhigang

2018-01-01

In this paper, we propose a connectivity-preserving flocking algorithm for multi-agent systems in which the neighbor set of each agent is determined by the hybrid metric-topological distance so that the interaction topology can be represented as the range-limited Delaunay graph, which combines the properties of the commonly used disk graph and Delaunay graph. As a result, the proposed flocking algorithm has the following advantages over the existing ones. First, range-limited Delaunay graph is sparser than the disk graph so that the information exchange among agents is reduced significantly. Second, some links irrelevant to the connectivity can be dynamically deleted during the evolution of the system. Thus, the proposed flocking algorithm is more flexible than existing algorithms, where links are not allowed to be disconnected once they are created. Finally, the multi-agent system spontaneously generates a regular quasi-lattice formation without imposing the constraint on the ratio of the sensing range of the agent to the desired distance between two adjacent agents. With the interaction topology induced by the hybrid distance, the proposed flocking algorithm can still be implemented in a distributed manner. We prove that the proposed flocking algorithm can steer the multi-agent system to a stable flocking motion, provided the initial interaction topology of multi-agent systems is connected and the hysteresis in link addition is smaller than a derived upper bound. The correctness and effectiveness of the proposed algorithm are verified by extensive numerical simulations, where the flocking algorithms based on the disk and Delaunay graph are compared.

A flocking algorithm for multi-agent systems with connectivity preservation under hybrid metric-topological interactions

PubMed Central

Feng, Zuren; Ren, Zhigang

2018-01-01

In this paper, we propose a connectivity-preserving flocking algorithm for multi-agent systems in which the neighbor set of each agent is determined by the hybrid metric-topological distance so that the interaction topology can be represented as the range-limited Delaunay graph, which combines the properties of the commonly used disk graph and Delaunay graph. As a result, the proposed flocking algorithm has the following advantages over the existing ones. First, range-limited Delaunay graph is sparser than the disk graph so that the information exchange among agents is reduced significantly. Second, some links irrelevant to the connectivity can be dynamically deleted during the evolution of the system. Thus, the proposed flocking algorithm is more flexible than existing algorithms, where links are not allowed to be disconnected once they are created. Finally, the multi-agent system spontaneously generates a regular quasi-lattice formation without imposing the constraint on the ratio of the sensing range of the agent to the desired distance between two adjacent agents. With the interaction topology induced by the hybrid distance, the proposed flocking algorithm can still be implemented in a distributed manner. We prove that the proposed flocking algorithm can steer the multi-agent system to a stable flocking motion, provided the initial interaction topology of multi-agent systems is connected and the hysteresis in link addition is smaller than a derived upper bound. The correctness and effectiveness of the proposed algorithm are verified by extensive numerical simulations, where the flocking algorithms based on the disk and Delaunay graph are compared. PMID:29462217

Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

NASA Astrophysics Data System (ADS)

Rienow, A.; Menz, G.

2015-12-01

Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future face of post-industrial cities are revealed. Finally, the advantages and limitations of linking pixels and people by combining AI and machine learning techniques in a multi-scale geosimulation approach are to be discussed.

Distributed Evaluation Functions for Fault Tolerant Multi-Rover Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian; Turner, Kagan

2005-01-01

The ability to evolve fault tolerant control strategies for large collections of agents is critical to the successful application of evolutionary strategies to domains where failures are common. Furthermore, while evolutionary algorithms have been highly successful in discovering single-agent control strategies, extending such algorithms to multiagent domains has proven to be difficult. In this paper we present a method for shaping evaluation functions for agents that provide control strategies that both are tolerant to different types of failures and lead to coordinated behavior in a multi-agent setting. This method neither relies of a centralized strategy (susceptible to single point of failures) nor a distributed strategy where each agent uses a system wide evaluation function (severe credit assignment problem). In a multi-rover problem, we show that agents using our agent-specific evaluation perform up to 500% better than agents using the system evaluation. In addition we show that agents are still able to maintain a high level of performance when up to 60% of the agents fail due to actuator, communication or controller faults.

Multi-issue Agent Negotiation Based on Fairness

NASA Astrophysics Data System (ADS)

Zuo, Baohe; Zheng, Sue; Wu, Hong

Agent-based e-commerce service has become a hotspot now. How to make the agent negotiation process quickly and high-efficiently is the main research direction of this area. In the multi-issue model, MAUT(Multi-attribute Utility Theory) or its derived theory usually consider little about the fairness of both negotiators. This work presents a general model of agent negotiation which considered the satisfaction of both negotiators via autonomous learning. The model can evaluate offers from the opponent agent based on the satisfaction degree, learn online to get the opponent's knowledge from interactive instances of history and negotiation of this time, make concessions dynamically based on fair object. Through building the optimal negotiation model, the bilateral negotiation achieved a higher efficiency and fairer deal.

Deterministic Teleportation of Multi-qudit States in a Network via Various Probabilistic Channels

NASA Astrophysics Data System (ADS)

Zhang, Ti-Hang; Jiang, Min; Huang, Xu; Wan, Min

2014-04-01

In this paper, we present a generalized approach to faithfully teleport an unknown state of a multi-qudit system involving multi spatially remote agents via various probabilistic channels. In a quantum teleportation network, there are generally multi spatially remote relay agents between a sender and a distant receiver. With the assistance of the relay agents, it is possible to directly construct a deterministic channel between the sender and the distant receiver. In our scheme, different from previous probabilistic teleportation protocols, the integrity of the unknown multi-qudit state could be maintained even when the construction of faithful channel fails. Our results also show that the required auxiliary particle resources, local operations and classical communications are considerably reduced for the present purpose.

Multi-agent modelling framework for water, energy and other resource networks

NASA Astrophysics Data System (ADS)

Knox, S.; Selby, P. D.; Meier, P.; Harou, J. J.; Yoon, J.; Lachaut, T.; Klassert, C. J. A.; Avisse, N.; Mohamed, K.; Tomlinson, J.; Khadem, M.; Tilmant, A.; Gorelick, S.

2015-12-01

Bespoke modelling tools are often needed when planning future engineered interventions in the context of various climate, socio-economic and geopolitical futures. Such tools can help improve system operating policies or assess infrastructure upgrades and their risks. A frequently used approach is to simulate and/or optimise the impact of interventions in engineered systems. Modelling complex infrastructure systems can involve incorporating multiple aspects into a single model, for example physical, economic and political. This presents the challenge of combining research from diverse areas into a single system effectively. We present the Pynsim 'Python Network Simulator' framework, a library for building simulation models capable of representing, the physical, institutional and economic aspects of an engineered resources system. Pynsim is an open source, object oriented code aiming to promote integration of different modelling processes through a single code library. We present two case studies that demonstrate important features of Pynsim's design. The first is a large interdisciplinary project of a national water system in the Middle East with modellers from fields including water resources, economics, hydrology and geography each considering different facets of a multi agent system. It includes: modelling water supply and demand for households and farms; a water tanker market with transfer of water between farms and households, and policy decisions made by government institutions at district, national and international level. This study demonstrates that a well-structured library of code can provide a hub for development and act as a catalyst for integrating models. The second focuses on optimising the location of new run-of-river hydropower plants. Using a multi-objective evolutionary algorithm, this study analyses different network configurations to identify the optimal placement of new power plants within a river network. This demonstrates that Pynsim can be used to evaluate a multitude of topologies for identifying the optimal location of infrastructure investments. Pynsim is available on GitHub or via standard python installer packages such as pip. It comes with several examples and online documentation, making it attractive for those less experienced in software engineering.

Cultural Geography Modeling and Analysis in Helmand Province

DTIC Science & Technology

2010-10-01

the application of an agent-based model called “Cultural Geography” to represent the civilian populace. This project uses a multi-agent system ...represent the civilian populace. This project uses a multi-agent system consisting of an environment, agents, objects (things), operations that can be...environments[1]. The model is patterned after the conflict eco- system described by Kilcullen[2] in an attempt to capture the complexities of irregular

Multi-agent planning and scheduling, execution monitoring and incremental rescheduling: Application to motorway traffic

NASA Technical Reports Server (NTRS)

Mourou, Pascal; Fade, Bernard

1992-01-01

This article describes a planning method applicable to agents with great perception and decision-making capabilities and the ability to communicate with other agents. Each agent has a task to fulfill allowing for the actions of other agents in its vicinity. Certain simultaneous actions may cause conflicts because they require the same resource. The agent plans each of its actions and simultaneously transmits these to its neighbors. In a similar way, it receives plans from the other agents and must take account of these plans. The planning method allows us to build a distributed scheduling system. Here, these agents are robot vehicles on a highway communicating by radio. In this environment, conflicts between agents concern the allocation of space in time and are connected with the inertia of the vehicles. Each vehicle made a temporal, spatial, and situated reasoning in order to drive without collision. The flexibility and reactivity of the method presented here allows the agent to generate its plan based on assumptions concerning the other agents and then check these assumptions progressively as plans are received from the other agents. A multi-agent execution monitoring of these plans can be done, using data generated during planning and the multi-agent decision-making algorithm described here. A selective backtrack allows us to perform incremental rescheduling.

Construction of a Learning Agent Handling Its Rewards According to Environmental Situations

NASA Astrophysics Data System (ADS)

Moriyama, Koichi; Numao, Masayuki

The authors aim at constructing an agent which learns appropriate actions in a Multi-Agent environment with and without social dilemmas. For this aim, the agent must have nonrationality that makes it give up its own profit when it should do that. Since there are many studies on rational learning that brings more and more profit, it is desirable to utilize them for constructing the agent. Therefore, we use a reward-handling manner that makes internal evaluation from the agent's rewards, and then the agent learns actions by a rational learning method with the internal evaluation. If the agent has only a fixed manner, however, it does not act well in the environment with and without dilemmas. Thus, the authors equip the agent with several reward-handling manners and criteria for selecting an effective one for the environmental situation. In the case of humans, what generates the internal evaluation is usually called emotion. Hence, this study also aims at throwing light on emotional activities of humans from a constructive view. In this paper, we divide a Multi-Agent environment into three situations and construct an agent having the reward-handling manners and the criteria. We observe that the agent acts well in all the three Multi-Agent situations composed of homogeneous agents.

Who acquires infection from whom and how? Disentangling multi-host and multi-mode transmission dynamics in the ‘elimination’ era

PubMed Central

Borlase, Anna; Rudge, James W.

2017-01-01

Multi-host infectious agents challenge our abilities to understand, predict and manage disease dynamics. Within this, many infectious agents are also able to use, simultaneously or sequentially, multiple modes of transmission. Furthermore, the relative importance of different host species and modes can itself be dynamic, with potential for switches and shifts in host range and/or transmission mode in response to changing selective pressures, such as those imposed by disease control interventions. The epidemiology of such multi-host, multi-mode infectious agents thereby can involve a multi-faceted community of definitive and intermediate/secondary hosts or vectors, often together with infectious stages in the environment, all of which may represent potential targets, as well as specific challenges, particularly where disease elimination is proposed. Here, we explore, focusing on examples from both human and animal pathogen systems, why and how we should aim to disentangle and quantify the relative importance of multi-host multi-mode infectious agent transmission dynamics under contrasting conditions, and ultimately, how this can be used to help achieve efficient and effective disease control. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’. PMID:28289259

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.

Fundamental properties of cooperative contagion processes

NASA Astrophysics Data System (ADS)

Chen, Li; Ghanbarnejad, Fakhteh; Brockmann, Dirk

2017-10-01

We investigate the effects of cooperativity between contagion processes that spread and persist in a host population. We propose and analyze a dynamical model in which individuals that are affected by one transmissible agent A exhibit a higher than baseline propensity of being affected by a second agent B and vice versa. The model is a natural extension of the traditional susceptible-infected-susceptible model used for modeling single contagion processes. We show that cooperativity changes the dynamics of the system considerably when cooperativity is strong. The system exhibits discontinuous phase transitions not observed in single agent contagion, multi-stability, a separation of the traditional epidemic threshold into different thresholds for inception and extinction as well as hysteresis. These properties are robust and are corroborated by stochastic simulations on lattices and generic network topologies. Finally, we investigate wave propagation and transients in a spatially extended version of the model and show that especially for intermediate values of baseline reproduction ratios the system is characterized by various types of wave-front speeds. The system can exhibit spatially heterogeneous stationary states for some parameters and negative front speeds (receding wave fronts). The two agent model can be employed as a starting point for more complex contagion processes, involving several interacting agents, a model framework particularly suitable for modeling the spread and dynamics of microbiological ecosystems in host populations.

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.

Coordination between Generation and Transmission Maintenance Scheduling by Means of Multi-agent Technique

NASA Astrophysics Data System (ADS)

Nagata, Takeshi; Tao, Yasuhiro; Utatani, Masahiro; Sasaki, Hiroshi; Fujita, Hideki

This paper proposes a multi-agent approach to maintenance scheduling in restructured power systems. The restructuring of electric power industry has resulted in market-based approaches for unbundling a multitude of service provided by self-interested entities such as power generating companies (GENCOs), transmission providers (TRANSCOs) and distribution companies (DISCOs). The Independent System Operator (ISO) is responsible for the security of the system operation. The schedule submitted to ISO by GENCOs and TRANSCOs should satisfy security and reliability constraints. The proposed method consists of several GENCO Agents (GAGs), TARNSCO Agents (TAGs) and a ISO Agent(IAG). The IAG’s role in maintenance scheduling is limited to ensuring that the submitted schedules do not cause transmission congestion or endanger the system reliability. From the simulation results, it can be seen the proposed multi-agent approach could coordinate between generation and transmission maintenance schedules.

Endogenous Price Bubbles in a Multi-Agent System of the Housing Market

PubMed Central

2015-01-01

Economic history shows a large number of boom-bust cycles, with the U.S. real estate market as one of the latest examples. Classical economic models have not been able to provide a full explanation for this type of market dynamics. Therefore, we analyze home prices in the U.S. using an alternative approach, a multi-agent complex system. Instead of the classical assumptions of agent rationality and market efficiency, agents in the model are heterogeneous, adaptive, and boundedly rational. We estimate the multi-agent system with historical house prices for the U.S. market. The model fits the data well and a deterministic version of the model can endogenously produce boom-and-bust cycles on the basis of the estimated coefficients. This implies that trading between agents themselves can create major price swings in absence of fundamental news. PMID:26107740

Sensei: A Multi-Modal Framework for Assessing Stress Resiliency

DTIC Science & Technology

2013-04-30

DATE MAY2013 2. REPORT TYPE 4. TITLE AND SUBTITLE Sensei: A Multi-Modal Framework for Assessing Stress Resiliency 6. AUTHOR(S) 7. PERFORMING...Report: Distribution A Page 1 of 3 SRI International (Sarnoff) Document Sensei: A Multi-Modal Framework for Assessing Stress Resiliency (April... Stress Markers in Real-Time in Lab Environment with graded exposure to ICT’s scenarios MAC 1-6 During this reporting period, we established

Modeling and Evaluating Emotions Impact on Cognition

DTIC Science & Technology

2013-07-01

Causality and Responsibility Judgment in Multi-Agent Interactions: Extended abstract. 23rd International Joint Conference on Artificial Inteligence ...responsibility judgment in multi-agent interactions." Journal of Artificial Intelligence Research v44(1), 223- 273. • Morteza Dehghani, Jonathan Gratch... Artificial Intelligence (AAAI’11). Grant related invited talks: • Keynote speaker, Workshop on Empathic and Emotional Agents at the International

Distributed Cooperation Solution Method of Complex System Based on MAS

NASA Astrophysics Data System (ADS)

Weijin, Jiang; Yuhui, Xu

To adapt the model in reconfiguring fault diagnosing to dynamic environment and the needs of solving the tasks of complex system fully, the paper introduced multi-Agent and related technology to the complicated fault diagnosis, an integrated intelligent control system is studied in this paper. Based on the thought of the structure of diagnostic decision and hierarchy in modeling, based on multi-layer decomposition strategy of diagnosis task, a multi-agent synchronous diagnosis federation integrated different knowledge expression modes and inference mechanisms are presented, the functions of management agent, diagnosis agent and decision agent are analyzed, the organization and evolution of agents in the system are proposed, and the corresponding conflict resolution algorithm in given, Layered structure of abstract agent with public attributes is build. System architecture is realized based on MAS distributed layered blackboard. The real world application shows that the proposed control structure successfully solves the fault diagnose problem of the complex plant, and the special advantage in the distributed domain.

Proceedings of the (20th) Annual Military Librarians’ Workshop on Public Relations, United States Naval Academy, Annapolis, Maryland, 20-22 October 1976

DTIC Science & Technology

1976-10-22

tat afec. out livs dont now wathyarvoig They’ doin’t i.now what they are doing simply becausc the have no adequa te basis to judge the effects of thir...publicity toGls. The main ones that I use are radio, te!hvision, newsletters for patrons, staff notes, booklets, posters , and last of all--the library...especially in the libraries. It is the most God awful color. You can cover t 13 it though. You can get loads of posters from all over the place--foreign

A Decision Support Framework For Science-Based, Multi-Stakeholder Deliberation: A Coral Reef Example

EPA Science Inventory

We present a decision support framework for science-based assessment and multi-stakeholder deliberation. The framework consists of two parts: a DPSIR (Drivers-Pressures-States-Impacts-Responses) analysis to identify the important causal relationships among anthropogenic environ...

Adaptive behaviors in multi-agent source localization using passive sensing.

PubMed

Shaukat, Mansoor; Chitre, Mandar

2016-12-01

In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. The algorithm is a set of individualistic and social behaviors where the individualistic behavior is as simple as an agent keeping its previous heading and is not self-sufficient in localizing the source. Source localization is achieved as an emergent property through agent's adaptive interactions with the neighbors and the environment. Given a single agent is incapable of localizing the source, maintaining team connectivity at all times is crucial. Two simple temporal sampling behaviors, intensity-based-adaptation and connectivity-based-adaptation, ensure an efficient localization strategy with minimal agent breakaways. The agent behaviors are simultaneously optimized using a two phase evolutionary optimization process. The optimized behaviors are estimated with analytical models and the resulting collective behavior is validated against the agent's sensor and actuator noise, strong multi-path interference due to environment variability, initialization distance sensitivity and loss of source signal.

A Scalable and Robust Multi-Agent Approach to Distributed Optimization

NASA Technical Reports Server (NTRS)

Tumer, Kagan

2005-01-01

Modularizing a large optimization problem so that the solutions to the subproblems provide a good overall solution is a challenging problem. In this paper we present a multi-agent approach to this problem based on aligning the agent objectives with the system objectives, obviating the need to impose external mechanisms to achieve collaboration among the agents. This approach naturally addresses scaling and robustness issues by ensuring that the agents do not rely on the reliable operation of other agents We test this approach in the difficult distributed optimization problem of imperfect device subset selection [Challet and Johnson, 2002]. In this problem, there are n devices, each of which has a "distortion", and the task is to find the subset of those n devices that minimizes the average distortion. Our results show that in large systems (1000 agents) the proposed approach provides improvements of over an order of magnitude over both traditional optimization methods and traditional multi-agent methods. Furthermore, the results show that even in extreme cases of agent failures (i.e., half the agents fail midway through the simulation) the system remains coordinated and still outperforms a failure-free and centralized optimization algorithm.

towards a theory-based multi-dimensional framework for assessment in mathematics: The "SEA" framework

NASA Astrophysics Data System (ADS)

Anku, Sitsofe E.

1997-09-01

Using the reform documents of the National Council of Teachers of Mathematics (NCTM) (NCTM, 1989, 1991, 1995), a theory-based multi-dimensional assessment framework (the "SEA" framework) which should help expand the scope of assessment in mathematics is proposed. This framework uses a context based on mathematical reasoning and has components that comprise mathematical concepts, mathematical procedures, mathematical communication, mathematical problem solving, and mathematical disposition.

Distributed Consensus of Stochastic Delayed Multi-agent Systems Under Asynchronous Switching.

PubMed

Wu, Xiaotai; Tang, Yang; Cao, Jinde; Zhang, Wenbing

2016-08-01

In this paper, the distributed exponential consensus of stochastic delayed multi-agent systems with nonlinear dynamics is investigated under asynchronous switching. The asynchronous switching considered here is to account for the time of identifying the active modes of multi-agent systems. After receipt of confirmation of mode's switching, the matched controller can be applied, which means that the switching time of the matched controller in each node usually lags behind that of system switching. In order to handle the coexistence of switched signals and stochastic disturbances, a comparison principle of stochastic switched delayed systems is first proved. By means of this extended comparison principle, several easy to verified conditions for the existence of an asynchronously switched distributed controller are derived such that stochastic delayed multi-agent systems with asynchronous switching and nonlinear dynamics can achieve global exponential consensus. Two examples are given to illustrate the effectiveness of the proposed method.

A Multi-Scale, Multi-Physics Optimization Framework for Additively Manufactured Structural Components

NASA Astrophysics Data System (ADS)

El-Wardany, Tahany; Lynch, Mathew; Gu, Wenjiong; Hsu, Arthur; Klecka, Michael; Nardi, Aaron; Viens, Daniel

This paper proposes an optimization framework enabling the integration of multi-scale / multi-physics simulation codes to perform structural optimization design for additively manufactured components. Cold spray was selected as the additive manufacturing (AM) process and its constraints were identified and included in the optimization scheme. The developed framework first utilizes topology optimization to maximize stiffness for conceptual design. The subsequent step applies shape optimization to refine the design for stress-life fatigue. The component weight was reduced by 20% while stresses were reduced by 75% and the rigidity was improved by 37%. The framework and analysis codes were implemented using Altair software as well as an in-house loading code. The optimized design was subsequently produced by the cold spray process.

A Novel Network Attack Audit System based on Multi-Agent Technology

NASA Astrophysics Data System (ADS)

Jianping, Wang; Min, Chen; Xianwen, Wu

A network attack audit system which includes network attack audit Agent, host audit Agent and management control center audit Agent is proposed. And the improved multi-agent technology is carried out in the network attack audit Agent which has achieved satisfactory audit results. The audit system in terms of network attack is just in-depth, and with the function improvement of network attack audit Agent, different attack will be better analyzed and audit. In addition, the management control center Agent should manage and analyze audit results from AA (or HA) and audit data on time. And the history files of network packets and host log data should also be audit to find deeper violations that cannot be found in real time.

Multiagent model and mean field theory of complex auction dynamics

NASA Astrophysics Data System (ADS)

Chen, Qinghua; Huang, Zi-Gang; Wang, Yougui; Lai, Ying-Cheng

2015-09-01

Recent years have witnessed a growing interest in analyzing a variety of socio-economic phenomena using methods from statistical and nonlinear physics. We study a class of complex systems arising from economics, the lowest unique bid auction (LUBA) systems, which is a recently emerged class of online auction game systems. Through analyzing large, empirical data sets of LUBA, we identify a general feature of the bid price distribution: an inverted J-shaped function with exponential decay in the large bid price region. To account for the distribution, we propose a multi-agent model in which each agent bids stochastically in the field of winner’s attractiveness, and develop a theoretical framework to obtain analytic solutions of the model based on mean field analysis. The theory produces bid-price distributions that are in excellent agreement with those from the real data. Our model and theory capture the essential features of human behaviors in the competitive environment as exemplified by LUBA, and may provide significant quantitative insights into complex socio-economic phenomena.

Thieno[3,2-c]pyran-4-one based novel small molecules: their synthesis, crystal structure analysis and in vitro evaluation as potential anticancer agents.

PubMed

Nakhi, Ali; Adepu, Raju; Rambabu, D; Kishore, Ravada; Vanaja, G R; Kalle, Arunasree M; Pal, Manojit

2012-07-01

Novel thieno[3,2-c]pyran-4-one based small molecules were designed as potential anticancer agents. Expeditious synthesis of these compounds was carried out via a multi-step sequence consisting of few steps such as Gewald reaction, Sandmeyer type iodination, Sonogashira type coupling followed by iodocyclization and then Pd-mediated various C-C bond forming reactions. The overall strategy involved the construction of thiophene ring followed by the fused pyranone moiety and then functionalization at C-7 position of the resultant thieno[3,2-c]pyran-4-one framework. Some of the compounds synthesized showed selective growth inhibition of cancer cells in vitro among which two compounds for example, 5d and 6c showed IC(50) values in the range of 2.0-2.5 μM. The crystal structure analysis of an active compound along with hydrogen bonding patterns and molecular arrangement present within the molecule is described. Copyright © 2012 Elsevier Ltd. All rights reserved.

Attention control learning in the decision space using state estimation

NASA Astrophysics Data System (ADS)

Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

2016-05-01

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

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.

A Lego Mindstorms NXT based test bench for multiagent exploratory systems and distributed network partitioning

NASA Astrophysics Data System (ADS)

Patil, Riya Raghuvir

Networks of communicating agents require distributed algorithms for a variety of tasks in the field of network analysis and control. For applications such as swarms of autonomous vehicles, ad hoc and wireless sensor networks, and such military and civilian applications as exploring and patrolling a robust autonomous system that uses a distributed algorithm for selfpartitioning can be significantly helpful. A single team of autonomous vehicles in a field may need to self-dissemble into multiple teams, conducive to completing multiple control tasks. Moreover, because communicating agents are subject to changes, namely, addition or failure of an agent or link, a distributed or decentralized algorithm is favorable over having a central agent. A framework to help with the study of self-partitioning of such multi agent systems that have most basic mobility model not only saves our time in conception but also gives us a cost effective prototype without negotiating the physical realization of the proposed idea. In this thesis I present my work on the implementation of a flexible and distributed stochastic partitioning algorithm on the LegoRTM Mindstorms' NXT on a graphical programming platform using National Instruments' LabVIEW(TM) forming a team of communicating agents via NXT-Bee radio module. We single out mobility, communication and self-partition as the core elements of the work. The goal is to randomly explore a precinct for reference sites. Agents who have discovered the reference sites announce their target acquisition to form a network formed based upon the distance of each agent with the other wherein the self-partitioning begins to find an optimal partition. Further, to illustrate the work, an experimental test-bench of five Lego NXT robots is presented.

Biomorphic Multi-Agent Architecture for Persistent Computing

NASA Technical Reports Server (NTRS)

Lodding, Kenneth N.; Brewster, Paul

2009-01-01

A multi-agent software/hardware architecture, inspired by the multicellular nature of living organisms, has been proposed as the basis of design of a robust, reliable, persistent computing system. Just as a multicellular organism can adapt to changing environmental conditions and can survive despite the failure of individual cells, a multi-agent computing system, as envisioned, could adapt to changing hardware, software, and environmental conditions. In particular, the computing system could continue to function (perhaps at a reduced but still reasonable level of performance) if one or more component( s) of the system were to fail. One of the defining characteristics of a multicellular organism is unity of purpose. In biology, the purpose is survival of the organism. The purpose of the proposed multi-agent architecture is to provide a persistent computing environment in harsh conditions in which repair is difficult or impossible. A multi-agent, organism-like computing system would be a single entity built from agents or cells. Each agent or cell would be a discrete hardware processing unit that would include a data processor with local memory, an internal clock, and a suite of communication equipment capable of both local line-of-sight communications and global broadcast communications. Some cells, denoted specialist cells, could contain such additional hardware as sensors and emitters. Each cell would be independent in the sense that there would be no global clock, no global (shared) memory, no pre-assigned cell identifiers, no pre-defined network topology, and no centralized brain or control structure. Like each cell in a living organism, each agent or cell of the computing system would contain a full description of the system encoded as genes, but in this case, the genes would be components of a software genome.

Identifying the domains of context important to implementation science: a study protocol.

PubMed

Squires, Janet E; Graham, Ian D; Hutchinson, Alison M; Michie, Susan; Francis, Jill J; Sales, Anne; Brehaut, Jamie; Curran, Janet; Ivers, Noah; Lavis, John; Linklater, Stefanie; Fenton, Shannon; Noseworthy, Thomas; Vine, Jocelyn; Grimshaw, Jeremy M

2015-09-28

There is growing recognition that "context" can and does modify the effects of implementation interventions aimed at increasing healthcare professionals' use of research evidence in clinical practice. However, conceptual clarity about what exactly comprises "context" is lacking. The purpose of this research program is to develop, refine, and validate a framework that identifies the key domains of context (and their features) that can facilitate or hinder (1) healthcare professionals' use of evidence in clinical practice and (2) the effectiveness of implementation interventions. A multi-phased investigation of context using mixed methods will be conducted. The first phase is a concept analysis of context using the Walker and Avant method to distinguish between the defining and irrelevant attributes of context. This phase will result in a preliminary framework for context that identifies its important domains and their features according to the published literature. The second phase is a secondary analysis of qualitative data from 13 studies of interviews with 312 healthcare professionals on the perceived barriers and enablers to their application of research evidence in clinical practice. These data will be analyzed inductively using constant comparative analysis. For the third phase, we will conduct semi-structured interviews with key health system stakeholders and change agents to elicit their knowledge and beliefs about the contextual features that influence the effectiveness of implementation interventions and healthcare professionals' use of evidence in clinical practice. Results from all three phases will be synthesized using a triangulation protocol to refine the context framework drawn from the concept analysis. The framework will then be assessed for content validity using an iterative Delphi approach with international experts (researchers and health system stakeholders/change agents). This research program will result in a framework that identifies the domains of context and their features that can facilitate or hinder: (1) healthcare professionals' use of evidence in clinical practice and (2) the effectiveness of implementation interventions. The framework will increase the conceptual clarity of the term "context" for advancing implementation science, improving healthcare professionals' use of evidence in clinical practice, and providing greater understanding of what interventions are likely to be effective in which contexts.

Flocking with connectivity preservation for disturbed nonlinear multi-agent systems by output feedback

NASA Astrophysics Data System (ADS)

Li, Ping; Zhang, Baoyong; Ma, Qian; Xu, Shengyuan; Chen, Weimin; Zhang, Zhengqiang

2018-05-01

This paper considers the problem of flocking with connectivity preservation for a class of disturbed nonlinear multi-agent systems. In order to deal with the nonlinearities in the dynamic of all agents, some auxiliary variables are introduced into the state observer for stability analysis. By proposing a bounded potential function and using adaptive theory, a novel output feedback consensus algorithm is developed to guarantee that the states of all agents achieve flocking with connectivity preservation.

IDEA: Planning at the Core of Autonomous Reactive Agents

NASA Technical Reports Server (NTRS)

Muscettola, Nicola; Dorais, Gregory A.; Fry, Chuck; Levinson, Richard; Plaunt, Christian; Clancy, Daniel (Technical Monitor)

2002-01-01

Several successful autonomous systems are separated into technologically diverse functional layers operating at different levels of abstraction. This diversity makes them difficult to implement and validate. In this paper, we present IDEA (Intelligent Distributed Execution Architecture), a unified planning and execution framework. In IDEA a layered system can be implemented as separate agents, one per layer, each representing its interactions with the world in a model. At all levels, the model representation primitives and their semantics is the same. Moreover, each agent relies on a single model, plan database, plan runner and on a variety of planners, both reactive and deliberative. The framework allows the specification of agents that operate, within a guaranteed reaction time and supports flexible specification of reactive vs. deliberative agent behavior. Within the IDEA framework we are working to fully duplicate the functionalities of the DS1 Remote Agent and extend it to domains of higher complexity than autonomous spacecraft control.

Tumor angiogenesis assessment using multi-fluorescent scans on murine slices by Markov random field framework

NASA Astrophysics Data System (ADS)

Laifa, Oumeima; Le Guillou-Buffello, Delphine; Racoceanu, Daniel

2017-11-01

The fundamental role of vascular supply in tumor growth makes the evaluation of the angiogenesis crucial in assessing effect of anti-angiogenic therapies. Since many years, such therapies are designed to inhibit the vascular endothelial growth factor (VEGF). To contribute to the assessment of anti-angiogenic agent (Pazopanib) effect on vascular and cellular structures, we acquired data from tumors extracted from a murine tumor model using Multi- Fluorescence Scanning. In this paper, we implemented an unsupervised algorithm combining the Watershed segmentation and Markov Random Field model (MRF). This algorithm allowed us to quantify the proportion of apoptotic endothelial cells and to generate maps according to cell density. Stronger association between apoptosis and endothelial cells was revealed in the tumors receiving anti-angiogenic therapy (n = 4) as compared to those receiving placebo (n = 4). A high percentage of apoptotic cells in the tumor area are endothelial. Lower density cells were detected in tumor slices presenting higher apoptotic endothelial areas.

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.

Global adaptation in networks of selfish components: emergent associative memory at the system scale.

PubMed

Watson, Richard A; Mills, Rob; Buckley, C L

2011-01-01

In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organize into structures that enhance global adaptation, efficiency, or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology, and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalization, and optimization are well understood. Such global functions within a single agent or organism are not wholly surprising, since the mechanisms (e.g., Hebbian learning) that create these neural organizations may be selected for this purpose; but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviors when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g., when they can influence which other agents they interact with), then, in adapting these inter-agent relationships to maximize their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviors as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalize by idealizing stored patterns and/or creating new combinations of subpatterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviors in the same sense, and by the same mechanism, as with the organizational principles familiar in connectionist models of organismic learning.

Multi Agent Systems with Symbiotic Learning and Evolution using GNP

NASA Astrophysics Data System (ADS)

Eguchi, Toru; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

Recently, various attempts relevant to Multi Agent Systems (MAS) which is one of the most promising systems based on Distributed Artificial Intelligence have been studied to control large and complicated systems efficiently. In these trends of MAS, Multi Agent Systems with Symbiotic Learning and Evolution named Masbiole has been proposed. In Masbiole, symbiotic phenomena among creatures are considered in the process of learning and evolution of MAS. So we can expect more flexible and sophisticated solutions than conventional MAS. In this paper, we apply Masbiole to Iterative Prisoner’s Dilemma Games (IPD Games) using Genetic Network Programming (GNP) which is a newly developed evolutionary computation method for constituting agents. Some characteristics of Masbiole using GNP in IPD Games are clarified.

Research on monitoring system of water resources in Shiyang River Basin based on Multi-agent

NASA Astrophysics Data System (ADS)

Zhao, T. H.; Yin, Z.; Song, Y. Z.

2012-11-01

The Shiyang River Basin is the most populous, economy relatively develop, the highest degree of development and utilization of water resources, water conflicts the most prominent, ecological environment problems of the worst hit areas in Hexi inland river basin in Gansu province. the contradiction between people and water is aggravated constantly in the basin. This text combines multi-Agent technology with monitoring system of water resource, the establishment of a management center, telemetry Agent Federation, as well as the communication network between the composition of the Shiyang River Basin water resources monitoring system. By taking advantage of multi-agent system intelligence and communications coordination to improve the timeliness of the basin water resources monitoring.

Adaptive Agent Modeling of Distributed Language: Investigations on the Effects of Cultural Variation and Internal Action Representations

ERIC Educational Resources Information Center

Cangelosi, Angelo

2007-01-01

In this paper we present the "grounded adaptive agent" computational framework for studying the emergence of communication and language. This modeling framework is based on simulations of population of cognitive agents that evolve linguistic capabilities by interacting with their social and physical environment (internal and external symbol…

An Automatic Multi-Target Independent Analysis Framework for Non-Planar Infrared-Visible Registration.

PubMed

Sun, Xinglong; Xu, Tingfa; Zhang, Jizhou; Zhao, Zishu; Li, Yuankun

2017-07-26

In this paper, we propose a novel automatic multi-target registration framework for non-planar infrared-visible videos. Previous approaches usually analyzed multiple targets together and then estimated a global homography for the whole scene, however, these cannot achieve precise multi-target registration when the scenes are non-planar. Our framework is devoted to solving the problem using feature matching and multi-target tracking. The key idea is to analyze and register each target independently. We present a fast and robust feature matching strategy, where only the features on the corresponding foreground pairs are matched. Besides, new reservoirs based on the Gaussian criterion are created for all targets, and a multi-target tracking method is adopted to determine the relationships between the reservoirs and foreground blobs. With the matches in the corresponding reservoir, the homography of each target is computed according to its moving state. We tested our framework on both public near-planar and non-planar datasets. The results demonstrate that the proposed framework outperforms the state-of-the-art global registration method and the manual global registration matrix in all tested datasets.

A stochastic multi-agent optimization model for energy infrastructure planning under uncertainty and competition.

DOT National Transportation Integrated Search

2017-07-04

This paper presents a stochastic multi-agent optimization model that supports energy infrastruc- : ture planning under uncertainty. The interdependence between dierent decision entities in the : system is captured in an energy supply chain network, w...

Coordination of heterogeneous nonlinear multi-agent systems with prescribed behaviours

NASA Astrophysics Data System (ADS)

Tang, Yutao

2017-10-01

In this paper, we consider a coordination problem for a class of heterogeneous nonlinear multi-agent systems with a prescribed input-output behaviour which was represented by another input-driven system. In contrast to most existing multi-agent coordination results with an autonomous (virtual) leader, this formulation takes possible control inputs of the leader into consideration. First, the coordination was achieved by utilising a group of distributed observers based on conventional assumptions of model matching problem. Then, a fully distributed adaptive extension was proposed without using the input of this input-output behaviour. An example was given to verify their effectiveness.

Plasmid Replicon Typing of Commensal and Pathogenic Escherichia coli Isolates▿

PubMed Central

Johnson, Timothy J.; Wannemuehler, Yvonne M.; Johnson, Sara J.; Logue, Catherine M.; White, David G.; Doetkott, Curt; Nolan, Lisa K.

2007-01-01

Despite the critical role of plasmids in horizontal gene transfer, few studies have characterized plasmid relatedness among different bacterial populations. Recently, a multiplex PCR replicon typing protocol was developed for classification of plasmids occurring in members of the Enterobacteriaceae. Here, a simplified version of this replicon typing procedure which requires only three multiplex panels to identify 18 plasmid replicons is described. This method was used to screen 1,015 Escherichia coli isolates of avian, human, and poultry meat origin for plasmid replicon types. Additionally, the isolates were assessed for their content of several colicin-associated genes. Overall, a high degree of plasmid variability was observed, with 221 different profiles occurring among the 1,015 isolates examined. IncFIB plasmids were the most common type identified, regardless of the source type of E. coli. IncFIB plasmids occurred significantly more often in avian pathogenic E. coli (APEC) and retail poultry E. coli (RPEC) than in uropathogenic E. coli (UPEC) and avian and human fecal commensal E. coli isolates (AFEC and HFEC, respectively). APEC and RPEC were also significantly more likely than UPEC, HFEC, and AFEC to possess the colicin-associated genes cvaC, cbi, and/or cma in conjunction with one or more plasmid replicons. The results suggest that E. coli isolates contaminating retail poultry are notably similar to APEC with regard to plasmid profiles, with both generally containing multiple plasmid replicon types in conjunction with colicin-related genes. In contrast, UPEC and human and avian commensal E. coli isolates generally lack the plasmid replicons and colicin-related genes seen in APEC and RPEC, suggesting limited dissemination of such plasmids among these bacterial populations. PMID:17277222

Modeling and Advanced Control for Sustainable Process ...

EPA Pesticide Factsheets

This book chapter introduces a novel process systems engineering framework that integrates process control with sustainability assessment tools for the simultaneous evaluation and optimization of process operations. The implemented control strategy consists of a biologically-inspired, multi-agent-based method. The sustainability and performance assessment of process operating points is carried out using the U.S. E.P.A.’s GREENSCOPE assessment tool that provides scores for the selected economic, material management, environmental and energy indicators. The indicator results supply information on whether the implementation of the controller is moving the process towards a more sustainable operation. The effectiveness of the proposed framework is illustrated through a case study of a continuous bioethanol fermentation process whose dynamics are characterized by steady-state multiplicity and oscillatory behavior. This book chapter contribution demonstrates the application of novel process control strategies for sustainability by increasing material management, energy efficiency, and pollution prevention, as needed for SHC Sustainable Uses of Wastes and Materials Management.

Multirate delivery of multiple therapeutic agents from metal-organic frameworks

DOE PAGES

McKinlay, Alistair C.; Allan, Phoebe K.; Renouf, Catherine L.; ...

2014-12-01

The highly porous nature of metal-organic frameworks (MOFs) offers great potential for the delivery of therapeutic agents. Here, we show that highly porous metal-organic frameworks can be used to deliver multiple therapeutic agents—a biologically active gas, an antibiotic drug molecule, and an active metal ion—simultaneously but at different rates. The possibilities offered by delivery of multiple agents with different mechanisms of action and, in particular, variable timescales may allow new therapy approaches. Here, we show that the loaded MOFs are highly active against various strains of bacteria.

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.

A nonlinear merging protocol for consensus in multi-agent systems on signed and weighted graphs

NASA Astrophysics Data System (ADS)

Feng, Shasha; Wang, Li; Li, Yijia; Sun, Shiwen; Xia, Chengyi

2018-01-01

In this paper, we investigate the multi-agent consensus for networks with undirected graphs which are not connected, especially for the signed graph in which some edge weights are positive and some edges have negative weights, and the negative-weight graph whose edge weights are negative. We propose a novel nonlinear merging consensus protocol to drive the states of all agents to converge to the same state zero which is not dependent upon the initial states of agents. If the undirected graph whose edge weights are positive is connected, then the states of all agents converge to the same state more quickly when compared to most other protocols. While the undirected graph whose edge weights might be positive or negative is unconnected, the states of all agents can still converge to the same state zero under the premise that the undirected graph can be divided into several connected subgraphs with more than one node. Furthermore, we also discuss the impact of parameter r presented in our protocol. Current results can further deepen the understanding of consensus processes for multi-agent systems.

The Role of Cooperation and Information Exchange in Transnational River Basins: the Zambezi River case

NASA Astrophysics Data System (ADS)

Castelletti, A.; Giuliani, M.; Soncini-Sessa, R.

2012-12-01

The presence of multiple, institutionally independent but physically interconnected decision-makers is a distinctive features of many water resources systems, especially of transnational river basins. The adoption of a centralized approach to study the optimal operation of these systems, as mostly done in the water resources literature, is conceptually interesting to quantify the best achievable performance, but of little practical impact given the real political and institutional setting. Centralized management indeed assumes a cooperative attitude and full information exchange by the involved parties. However, when decision-makers belong to different countries or institutions, it is very likely that they act considering only their local objectives, producing global externalities that negatively impact on other objectives. In this work we adopt a Multi-Agent Systems framework, which naturally allows to represent a set of self-interested agents (decision-makers and/or stakeholders) acting in a distributed decision-making process. According to this agent-based approach, each agent represents a decision-maker, whose decisions are defined by an explicit optimization problem considering only the agent's local interests. In particular, this work assesses the role of information exchange and increasing level of cooperation among originally non-cooperative agents. The Zambezi River basin is used to illustrate the methodology: the four largest reservoirs in the basin (Ithezhithezhi, Kafue-Gorge, Kariba and Cahora Bassa) are mainly operated for maximizing the economic revenue from hydropower energy production with considerably negative effects on the aquatic ecosystem in the Zambezi delta due to the alteration of the natural flow regime. We comparatively analyse the ideal centralized solution and the current situation where all the decision-makers act independently and non-cooperatively. Indeed, although a new basin-level institution called Zambezi Watercourse Commission (ZAMCON) should be established in the next future, Zambia recently refused to sign and ratify the ZAMCON Protocol and the road toward a fully cooperative framework is still long. Results show that increasing levels of information exchange can help in mitigating the conflict generated by a non-cooperative setting as it allows the downstream agents, i.e. Mozambique country, to better adapt to the upstream management strategies. Furthermore, the role of information exchange depends on the considered objectives and it is particularly relevant for environmental interests.

A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling.

PubMed

Singh, Karandeep; Ahn, Chang-Won; Paik, Euihyun; Bae, Jang Won; Lee, Chun-Hee

2018-01-01

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or "soft," aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

Multi-agent system as a new approach to effective chronic heart failure management: key considerations.

PubMed

Mohammadzadeh, Niloofar; Safdari, Reza; Rahimi, Azin

2013-09-01

Given the importance of the follow-up of chronic heart failure (CHF) patients to reduce common causes of re-admission and deterioration of their status that lead to imposing spiritual and physical costs on patients and society, modern technology tools should be used to the best advantage. The aim of this article is to explain key points which should be considered in designing an appropriate multi-agent system to improve CHF management. In this literature review articles were searched with keywords like multi-agent system, heart failure, chronic disease management in Science Direct, Google Scholar and PubMed databases without regard to the year of publications. Agents are an innovation in the field of artificial intelligence. Because agents are capable of solving complex and dynamic health problems, to take full advantage of e-Health, the healthcare system must take steps to make use of this technology. Key factors in CHF management through a multi-agent system approach must be considered such as organization, confidentiality in general aspects and design and architecture points in specific aspects. Note that use of agent systems only with a technical view is associated with many problems. Hence, in delivering healthcare to CHF patients, considering social and human aspects is essential. It is obvious that identifying and resolving technical and non-technical challenges is vital in the successful implementation of this technology.

Multi-Agent System as a New Approach to Effective Chronic Heart Failure Management: Key Considerations

PubMed Central

Mohammadzadeh, Niloofar; Rahimi, Azin

2013-01-01

Objectives Given the importance of the follow-up of chronic heart failure (CHF) patients to reduce common causes of re-admission and deterioration of their status that lead to imposing spiritual and physical costs on patients and society, modern technology tools should be used to the best advantage. The aim of this article is to explain key points which should be considered in designing an appropriate multi-agent system to improve CHF management. Methods In this literature review articles were searched with keywords like multi-agent system, heart failure, chronic disease management in Science Direct, Google Scholar and PubMed databases without regard to the year of publications. Results Agents are an innovation in the field of artificial intelligence. Because agents are capable of solving complex and dynamic health problems, to take full advantage of e-Health, the healthcare system must take steps to make use of this technology. Key factors in CHF management through a multi-agent system approach must be considered such as organization, confidentiality in general aspects and design and architecture points in specific aspects. Conclusions Note that use of agent systems only with a technical view is associated with many problems. Hence, in delivering healthcare to CHF patients, considering social and human aspects is essential. It is obvious that identifying and resolving technical and non-technical challenges is vital in the successful implementation of this technology. PMID:24195010

TACtic- A Multi Behavioral Agent for Trading Agent Competition

NASA Astrophysics Data System (ADS)

Khosravi, Hassan; Shiri, Mohammad E.; Khosravi, Hamid; Iranmanesh, Ehsan; Davoodi, Alireza

Software agents are increasingly being used to represent humans in online auctions. Such agents have the advantages of being able to systematically monitor a wide variety of auctions and then make rapid decisions about what bids to place in what auctions. They can do this continuously and repetitively without losing concentration. To provide a means of evaluating and comparing (benchmarking) research methods in this area the trading agent competition (TAC) was established. This paper describes the design, of TACtic. Our agent uses multi behavioral techniques at the heart of its decision making to make bidding decisions in the face of uncertainty, to make predictions about the likely outcomes of auctions, and to alter the agent's bidding strategy in response to the prevailing market conditions.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

PubMed

Cruz-Piris, Luis; Rivera, Diego; Fernandez, Susel; Marsa-Maestre, Ivan

2018-02-02

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

PubMed Central

2018-01-01

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network. PMID:29393884

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

A Buyer Behaviour Framework for the Development and Design of Software Agents in E-Commerce.

ERIC Educational Resources Information Center

Sproule, Susan; Archer, Norm

2000-01-01

Software agents are computer programs that run in the background and perform tasks autonomously as delegated by the user. This paper blends models from marketing research and findings from the field of decision support systems to build a framework for the design of software agents to support in e-commerce buying applications. (Contains 35…

Multi Agent Reward Analysis for Learning in Noisy Domains

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Agogino, Adrian K.

2005-01-01

In many multi agent learning problems, it is difficult to determine, a priori, the agent reward structure that will lead to good performance. This problem is particularly pronounced in continuous, noisy domains ill-suited to simple table backup schemes commonly used in TD(lambda)/Q-learning. In this paper, we present a new reward evaluation method that allows the tradeoff between coordination among the agents and the difficulty of the learning problem each agent faces to be visualized. This method is independent of the learning algorithm and is only a function of the problem domain and the agents reward structure. We then use this reward efficiency visualization method to determine an effective reward without performing extensive simulations. We test this method in both a static and a dynamic multi-rover learning domain where the agents have continuous state spaces and where their actions are noisy (e.g., the agents movement decisions are not always carried out properly). Our results show that in the more difficult dynamic domain, the reward efficiency visualization method provides a two order of magnitude speedup in selecting a good reward. Most importantly it allows one to quickly create and verify rewards tailored to the observational limitations of the domain.

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

Carpet: Adaptive Mesh Refinement for the Cactus Framework

NASA Astrophysics Data System (ADS)

Schnetter, Erik; Hawley, Scott; Hawke, Ian

2016-11-01

Carpet is an adaptive mesh refinement and multi-patch driver for the Cactus Framework (ascl:1102.013). Cactus is a software framework for solving time-dependent partial differential equations on block-structured grids, and Carpet acts as driver layer providing adaptive mesh refinement, multi-patch capability, as well as parallelization and efficient I/O.

An Agent-Based Data Mining System for Ontology Evolution

NASA Astrophysics Data System (ADS)

Hadzic, Maja; Dillon, Darshan

We have developed an evidence-based mental health ontological model that represents mental health in multiple dimensions. The ongoing addition of new mental health knowledge requires a continual update of the Mental Health Ontology. In this paper, we describe how the ontology evolution can be realized using a multi-agent system in combination with data mining algorithms. We use the TICSA methodology to design this multi-agent system which is composed of four different types of agents: Information agent, Data Warehouse agent, Data Mining agents and Ontology agent. We use UML 2.1 sequence diagrams to model the collaborative nature of the agents and a UML 2.1 composite structure diagram to model the structure of individual agents. The Mental Heath Ontology has the potential to underpin various mental health research experiments of a collaborative nature which are greatly needed in times of increasing mental distress and illness.

Space-Time Processing for Tactical Mobile Ad Hoc Networks

DTIC Science & Technology

2008-08-01

vision for multiple concurrent communication settings, i.e., a many-to-many framework where multi-packet transmissions (MPTs) and multi-packet...modelling framework of capacity-delay tradeoffs We have introduced the first unified modeling framework for the computation of fundamental limits o We...dalities in wireless n twor i-packet modelling framework to account for the use of m lti-packet reception (MPR) f ad hoc networks with MPT under

Multi-Agent Systems Design for Novices

ERIC Educational Resources Information Center

Lynch, Simon; Rajendran, Keerthi

2005-01-01

Advanced approaches to the construction of software systems can present difficulties to learners. This is true for multi-agent systems (MAS) which exhibit concurrency, non-determinacy of structure and composition and sometimes emergent behavior characteristics. Additional barriers exist for learners because mainstream MAS technology is young and…

Novel probabilistic and distributed algorithms for guidance, control, and nonlinear estimation of large-scale multi-agent systems

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Saptarshi

Multi-agent systems are widely used for constructing a desired formation shape, exploring an area, surveillance, coverage, and other cooperative tasks. This dissertation introduces novel algorithms in the three main areas of shape formation, distributed estimation, and attitude control of large-scale multi-agent systems. In the first part of this dissertation, we address the problem of shape formation for thousands to millions of agents. Here, we present two novel algorithms for guiding a large-scale swarm of robotic systems into a desired formation shape in a distributed and scalable manner. These probabilistic swarm guidance algorithms adopt an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled using tunable Markov chains. In the first algorithm - Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) - each agent determines its bin transition probabilities using a time-inhomogeneous Markov chain that is constructed in real-time using feedback from the current swarm distribution. This PSG-IMC algorithm minimizes the expected cost of the transitions required to achieve and maintain the desired formation shape, even when agents are added to or removed from the swarm. The algorithm scales well with a large number of agents and complex formation shapes, and can also be adapted for area exploration applications. In the second algorithm - Probabilistic Swarm Guidance using Optimal Transport (PSG-OT) - each agent determines its bin transition probabilities by solving an optimal transport problem, which is recast as a linear program. In the presence of perfect feedback of the current swarm distribution, this algorithm minimizes the given cost function, guarantees faster convergence, reduces the number of transitions for achieving the desired formation, and is robust to disturbances or damages to the formation. We demonstrate the effectiveness of these two proposed swarm guidance algorithms using results from numerical simulations and closed-loop hardware experiments on multiple quadrotors. In the second part of this dissertation, we present two novel discrete-time algorithms for distributed estimation, which track a single target using a network of heterogeneous sensing agents. The Distributed Bayesian Filtering (DBF) algorithm, the sensing agents combine their normalized likelihood functions using the logarithmic opinion pool and the discrete-time dynamic average consensus algorithm. Each agent's estimated likelihood function converges to an error ball centered on the joint likelihood function of the centralized multi-sensor Bayesian filtering algorithm. Using a new proof technique, the convergence, stability, and robustness properties of the DBF algorithm are rigorously characterized. The explicit bounds on the time step of the robust DBF algorithm are shown to depend on the time-scale of the target dynamics. Furthermore, the DBF algorithm for linear-Gaussian models can be cast into a modified form of the Kalman information filter. In the Bayesian Consensus Filtering (BCF) algorithm, the agents combine their estimated posterior pdfs multiple times within each time step using the logarithmic opinion pool scheme. Thus, each agent's consensual pdf minimizes the sum of Kullback-Leibler divergences with the local posterior pdfs. The performance and robust properties of these algorithms are validated using numerical simulations. In the third part of this dissertation, we present an attitude control strategy and a new nonlinear tracking controller for a spacecraft carrying a large object, such as an asteroid or a boulder. If the captured object is larger or comparable in size to the spacecraft and has significant modeling uncertainties, conventional nonlinear control laws that use exact feed-forward cancellation are not suitable because they exhibit a large resultant disturbance torque. The proposed nonlinear tracking control law guarantees global exponential convergence of tracking errors with finite-gain Lp stability in the presence of modeling uncertainties and disturbances, and reduces the resultant disturbance torque. Further, this control law permits the use of any attitude representation and its integral control formulation eliminates any constant disturbance. Under small uncertainties, the best strategy for stabilizing the combined system is to track a fuel-optimal reference trajectory using this nonlinear control law, because it consumes the least amount of fuel. In the presence of large uncertainties, the most effective strategy is to track the derivative plus proportional-derivative based reference trajectory, because it reduces the resultant disturbance torque. The effectiveness of the proposed attitude control law is demonstrated by using results of numerical simulation based on an Asteroid Redirect Mission concept. The new algorithms proposed in this dissertation will facilitate the development of versatile autonomous multi-agent systems that are capable of performing a variety of complex tasks in a robust and scalable manner.

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.

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.

Distributed event-triggered consensus tracking of second-order multi-agent systems with a virtual leader

NASA Astrophysics Data System (ADS)

Jie, Cao; Zhi-Hai, Wu; Li, Peng

2016-05-01

This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy. Project supported by the National Natural Science Foundation of China (Grant Nos. 61203147, 61374047, and 61403168).

Human-Robot Teaming in a Multi-Agent Space Assembly Task

NASA Technical Reports Server (NTRS)

Rehnmark, Fredrik; Currie, Nancy; Ambrose, Robert O.; Culbert, Christopher

2004-01-01

NASA's Human Space Flight program depends heavily on spacewalks performed by pairs of suited human astronauts. These Extra-Vehicular Activities (EVAs) are severely restricted in both duration and scope by consumables and available manpower. An expanded multi-agent EVA team combining the information-gathering and problem-solving skills of humans with the survivability and physical capabilities of robots is proposed and illustrated by example. Such teams are useful for large-scale, complex missions requiring dispersed manipulation, locomotion and sensing capabilities. To study collaboration modalities within a multi-agent EVA team, a 1-g test is conducted with humans and robots working together in various supporting roles.

Distributed-observer-based cooperative control for synchronization of linear discrete-time multi-agent systems.

PubMed

Liang, Hongjing; Zhang, Huaguang; Wang, Zhanshan

2015-11-01

This paper considers output synchronization of discrete-time multi-agent systems with directed communication topologies. The directed communication graph contains a spanning tree and the exosystem as its root. Distributed observer-based consensus protocols are proposed, based on the relative outputs of neighboring agents. A multi-step algorithm is presented to construct the observer-based protocols. In light of the discrete-time algebraic Riccati equation and internal model principle, synchronization problem is completed. At last, numerical simulation is provided to verify the effectiveness of the theoretical results. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Delay-dependent coupling for a multi-agent LTI consensus system with inter-agent delays

NASA Astrophysics Data System (ADS)

Qiao, Wei; Sipahi, Rifat

2014-01-01

Delay-dependent coupling (DDC) is considered in this paper in a broadly studied linear time-invariant multi-agent consensus system in which agents communicate with each other under homogeneous delays, while attempting to reach consensus. The coupling among the agents is designed here as an explicit parameter of this delay, allowing couplings to autonomously adapt based on the delay value, and in order to guarantee stability and a certain degree of robustness in the network despite the destabilizing effect of delay. Design procedures, analysis of convergence speed of consensus, comprehensive numerical studies for the case of time-varying delay, and limitations are presented.

A Multi-Agent Environment for Negotiation

NASA Astrophysics Data System (ADS)

Hindriks, Koen V.; Jonker, Catholijn M.; Tykhonov, Dmytro

In this chapter we introduce the System for Analysis of Multi-Issue Negotiation (SAMIN). SAMIN offers a negotiation environment that supports and facilitates the setup of various negotiation setups. The environment has been designed to analyse negotiation processes between human negotiators, between human and software agents, and between software agents. It offers a range of different agents, different domains, and other options useful to define a negotiation setup. The environment has been used to test and evaluate a range of negotiation strategies in various domains playing against other negotiating agents as well as humans. We discuss some of the results obtained by means of these experiments.

A Generalized Mixture Framework for Multi-label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input–output and output–output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods. PMID:26613069

Toward a Model Framework of Generalized Parallel Componential Processing of Multi-Symbol Numbers

ERIC Educational Resources Information Center

Huber, Stefan; Cornelsen, Sonja; Moeller, Korbinian; Nuerk, Hans-Christoph

2015-01-01

In this article, we propose and evaluate a new model framework of parallel componential multi-symbol number processing, generalizing the idea of parallel componential processing of multi-digit numbers to the case of negative numbers by considering the polarity signs similar to single digits. In a first step, we evaluated this account by defining…

Using Principal-Agent Theory as a Framework for Analysis in Evaluating the Multiple Stakeholders Involved in the Accreditation and Quality Assurance of International Medical Branch Campuses

ERIC Educational Resources Information Center

Borgos, Jill E.

2013-01-01

This article applies the theoretical framework of principal-agent theory in order to better understand the complex organisational relationships emerging between entities invested in the establishment and monitoring of cross-border international branch campus medical schools. Using the key constructs of principal-agent theory, information asymmetry…

Reinforcement learning in supply chains.

PubMed

Valluri, Annapurna; North, Michael J; Macal, Charles M

2009-10-01

Effective management of supply chains creates value and can strategically position companies. In practice, human beings have been found to be both surprisingly successful and disappointingly inept at managing supply chains. The related fields of cognitive psychology and artificial intelligence have postulated a variety of potential mechanisms to explain this behavior. One of the leading candidates is reinforcement learning. This paper applies agent-based modeling to investigate the comparative behavioral consequences of three simple reinforcement learning algorithms in a multi-stage supply chain. For the first time, our findings show that the specific algorithm that is employed can have dramatic effects on the results obtained. Reinforcement learning is found to be valuable in multi-stage supply chains with several learning agents, as independent agents can learn to coordinate their behavior. However, learning in multi-stage supply chains using these postulated approaches from cognitive psychology and artificial intelligence take extremely long time periods to achieve stability which raises questions about their ability to explain behavior in real supply chains. The fact that it takes thousands of periods for agents to learn in this simple multi-agent setting provides new evidence that real world decision makers are unlikely to be using strict reinforcement learning in practice.

Relay tracking control for second-order multi-agent systems with damaged agents.

PubMed

Dong, Lijing; Li, Jing; Liu, Qin

2017-11-01

This paper investigates a situation where smart agents capable of sensory and mobility are deployed to monitor a designated area. A preset number of agents start tracking when a target intrudes this area. Some of the tracking agents are possible to be out of order over the tracking course. Thus, we propose a cooperative relay tracking strategy to ensure the successful tracking with existence of damaged agents. Relay means that, when a tracking agent quits tracking due to malfunction, one of the near deployed agents replaces it to continue the tracking task. This results in jump of tracking errors and dynamic switching of topology of the multi-agent system. Switched system technique is employed to solve this specific problem. Finally, the effectiveness of proposed tracking strategy and validity of the theoretical results are verified by conducting a numerical simulation. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Overview of the Special Issue: A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

EPA Science Inventory

The Climate Change Impacts and Risk Analysis (CIRA) project establishes a new multi-model framework to systematically assess the impacts, economic damages, and risks from climate change in the United States. The primary goal of this framework to estimate how climate change impac...

Distributed cooperative H∞ optimal tracking control of MIMO nonlinear multi-agent systems in strict-feedback form via adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Luy, N. T.

2018-04-01

The design of distributed cooperative H∞ optimal controllers for multi-agent systems is a major challenge when the agents' models are uncertain multi-input and multi-output nonlinear systems in strict-feedback form in the presence of external disturbances. In this paper, first, the distributed cooperative H∞ optimal tracking problem is transformed into controlling the cooperative tracking error dynamics in affine form. Second, control schemes and online algorithms are proposed via adaptive dynamic programming (ADP) and the theory of zero-sum differential graphical games. The schemes use only one neural network (NN) for each agent instead of three from ADP to reduce computational complexity as well as avoid choosing initial NN weights for stabilising controllers. It is shown that despite not using knowledge of cooperative internal dynamics, the proposed algorithms not only approximate values to Nash equilibrium but also guarantee all signals, such as the NN weight approximation errors and the cooperative tracking errors in the closed-loop system, to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is shown by simulation results of an application to wheeled mobile multi-robot systems.

A Multi-Scale Energy Food Systems Modeling Framework For Climate Adaptation

NASA Astrophysics Data System (ADS)

Siddiqui, S.; Bakker, C.; Zaitchik, B. F.; Hobbs, B. F.; Broaddus, E.; Neff, R.; Haskett, J.; Parker, C.

2016-12-01

Our goal is to understand coupled system dynamics across scales in a manner that allows us to quantify the sensitivity of critical human outcomes (nutritional satisfaction, household economic well-being) to development strategies and to climate or market induced shocks in sub-Saharan Africa. We adopt both bottom-up and top-down multi-scale modeling approaches focusing our efforts on food, energy, water (FEW) dynamics to define, parameterize, and evaluate modeled processes nationally as well as across climate zones and communities. Our framework comprises three complementary modeling techniques spanning local, sub-national and national scales to capture interdependencies between sectors, across time scales, and on multiple levels of geographic aggregation. At the center is a multi-player micro-economic (MME) partial equilibrium model for the production, consumption, storage, and transportation of food, energy, and fuels, which is the focus of this presentation. We show why such models can be very useful for linking and integrating across time and spatial scales, as well as a wide variety of models including an agent-based model applied to rural villages and larger population centers, an optimization-based electricity infrastructure model at a regional scale, and a computable general equilibrium model, which is applied to understand FEW resources and economic patterns at national scale. The MME is based on aggregating individual optimization problems for relevant players in an energy, electricity, or food market and captures important food supply chain components of trade and food distribution accounting for infrastructure and geography. Second, our model considers food access and utilization by modeling food waste and disaggregating consumption by income and age. Third, the model is set up to evaluate the effects of seasonality and system shocks on supply, demand, infrastructure, and transportation in both energy and food.

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.

Global Sensitivity Analysis for Large-scale Socio-hydrological Models using the Cloud

NASA Astrophysics Data System (ADS)

Hu, Y.; Garcia-Cabrejo, O.; Cai, X.; Valocchi, A. J.; Dupont, B.

2014-12-01

In the context of coupled human and natural system (CHNS), incorporating human factors into water resource management provides us with the opportunity to understand the interactions between human and environmental systems. A multi-agent system (MAS) model is designed to couple with the physically-based Republican River Compact Administration (RRCA) groundwater model, in an attempt to understand the declining water table and base flow in the heavily irrigated Republican River basin. For MAS modelling, we defined five behavioral parameters (κ_pr, ν_pr, κ_prep, ν_prep and λ) to characterize the agent's pumping behavior given the uncertainties of the future crop prices and precipitation. κ and ν describe agent's beliefs in their prior knowledge of the mean and variance of crop prices (κ_pr, ν_pr) and precipitation (κ_prep, ν_prep), and λ is used to describe the agent's attitude towards the fluctuation of crop profits. Notice that these human behavioral parameters as inputs to the MAS model are highly uncertain and even not measurable. Thus, we estimate the influences of these behavioral parameters on the coupled models using Global Sensitivity Analysis (GSA). In this paper, we address two main challenges arising from GSA with such a large-scale socio-hydrological model by using Hadoop-based Cloud Computing techniques and Polynomial Chaos Expansion (PCE) based variance decomposition approach. As a result, 1,000 scenarios of the coupled models are completed within two hours with the Hadoop framework, rather than about 28days if we run those scenarios sequentially. Based on the model results, GSA using PCE is able to measure the impacts of the spatial and temporal variations of these behavioral parameters on crop profits and water table, and thus identifies two influential parameters, κ_pr and λ. The major contribution of this work is a methodological framework for the application of GSA in large-scale socio-hydrological models. This framework attempts to find a balance between the heavy computational burden regarding model execution and the number of model evaluations required in the GSA analysis, particularly through an organic combination of Hadoop-based Cloud Computing to efficiently evaluate the socio-hydrological model and PCE where the sensitivity indices are efficiently estimated from its coefficients.

Distributed Information Fusion through Advanced Multi-Agent Control

DTIC Science & Technology

2016-10-17

AFRL-AFOSR-JP-TR-2016-0080 Distributed Information Fusion through Advanced Multi-Agent Control Adrian Bishop NATIONAL ICT AUSTRALIA LIMITED Final...TASK NUMBER 5f.  WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) NATIONAL ICT AUSTRALIA LIMITED L 5 13 GARDEN ST EVELEIGH, 2015

Distributed Information Fusion through Advanced Multi-Agent Control

DTIC Science & Technology

2016-09-09

AFRL-AFOSR-JP-TR-2016-0080 Distributed Information Fusion through Advanced Multi-Agent Control Adrian Bishop NATIONAL ICT AUSTRALIA LIMITED Final...TASK NUMBER 5f.  WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) NATIONAL ICT AUSTRALIA LIMITED L 5 13 GARDEN ST EVELEIGH, 2015

Multi-Agent Simulation of Allocating and Routing Ambulances Under Condition of Street Blockage after Natural Disaster

NASA Astrophysics Data System (ADS)

Azimi, S.; Delavar, M. R.; Rajabifard, A.

2017-09-01

In response to natural disasters, efficient planning for optimum allocation of the medical assistance to wounded as fast as possible and wayfinding of first responders immediately to minimize the risk of natural disasters are of prime importance. This paper aims to propose a multi-agent based modeling for optimum allocation of space to emergency centers according to the population, street network and number of ambulances in emergency centers by constraint network Voronoi diagrams, wayfinding of ambulances from emergency centers to the wounded locations and return based on the minimum ambulances travel time and path length implemented by NSGA and the use of smart city facilities to accelerate the rescue operation. Simulated annealing algorithm has been used for minimizing the difference between demands and supplies of the constrained network Voronoi diagrams. In the proposed multi-agent system, after delivering the location of the wounded and their symptoms, the constraint network Voronoi diagram for each emergency center is determined. This process was performed simultaneously for the multi-injuries in different Voronoi diagrams. In the proposed multi-agent system, the priority of the injuries for receiving medical assistance and facilities of the smart city for reporting the blocked streets was considered. Tehran Municipality District 5 was considered as the study area and during 3 minutes intervals, the volunteers reported the blocked street. The difference between the supply and the demand divided to the supply in each Voronoi diagram decreased to 0.1601. In the proposed multi-agent system, the response time of the ambulances is decreased about 36.7%.

Multi-Agent Social Simulation

NASA Astrophysics Data System (ADS)

Noda, Itsuki; Stone, Peter; Yamashita, Tomohisa; Kurumatani, Koichi

While ambient intelligence and smart environments (AISE) technologies are expected to provide large impacts to human lives and social activities, it is generally difficult to show utilities and effects of these technologies on societies. AISE technologies are not only methods to improve performance and functionality of existing services in the society, but also frameworks to introduce new systems and services to the society. For example, no one expected beforehand what Internet or mobile phone brought into out social activities and services, although they changes our social system and patterns of behaviors drastically and emerge new services (and risks, unfortunately). The main reason of this difficulty is that actual effects of IT systems appear when enough number of people in the society use the technologies.

A Real-time Strategy Agent Framework and Strategy Classifier for Computer Generated Forces

DTIC Science & Technology

2012-06-01

via our strategy definition schema, plays the game according to the defined strategy. 4 ) Generate a quality RTS data set. 5) Create an accurate and...General Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.6 Thesis Overview...Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4 Agent Framework

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.

Polymeric micelles for multi-drug delivery in cancer.

PubMed

Cho, Hyunah; Lai, Tsz Chung; Tomoda, Keishiro; Kwon, Glen S

2015-02-01

Drug combinations are common in cancer treatment and are rapidly evolving, moving beyond chemotherapy combinations to combinations of signal transduction inhibitors. For the delivery of drug combinations, i.e., multi-drug delivery, major considerations are synergy, dose regimen (concurrent versus sequential), pharmacokinetics, toxicity, and safety. In this contribution, we review recent research on polymeric micelles for multi-drug delivery in cancer. In concurrent drug delivery, polymeric micelles deliver multi-poorly water-soluble anticancer agents, satisfying strict requirements in solubility, stability, and safety. In sequential drug delivery, polymeric micelles participate in pretreatment strategies that "prime" solid tumors and enhance the penetration of secondarily administered anticancer agent or nanocarrier. The improved delivery of multiple poorly water-soluble anticancer agents by polymeric micelles via concurrent or sequential regimens offers novel and interesting strategies for drug combinations in cancer treatment.

A Framework for Supporting Survivability, Network Planning and Cross-Layer Optimization in Future Multi-Domain Terabit Networks

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

Baldin, Ilya; Huang, Shu; Gopidi, Rajesh

This final project report describes the accomplishments, products and publications from the award. It includes the overview of the project goals to devise a framework for managing resources in multi-domain, multi-layer networks, as well the details of the mathematical problem formulation and the description of the prototype built to prove the concept.

Mission Profiles and Evidential Reasoning for Estimating Information Relevancy in Multi-Agent Supervisory Control Applications

DTIC Science & Technology

2010-06-01

artificial agents, their limited scope and singular purpose lead us to believe that human-machine trust will be very portable. That is, if one operator... Artificial Intelligence Review 2(2), 1988. [E88] M.R. Endsley. Situation awareness global assessment technique (SAGAT). In Proceedings of the National...1995. [F98] J. Ferber, Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, Addison- Wesley, 1998. [NP01] I. Niles and A

Impact of immigrants on a multi-agent economical system

PubMed Central

Razakanirina, Ranaivo; Groen, Derek

2018-01-01

We consider a multi-agent model of a simple economical system and study the impacts of a wave of immigrants on the stability of the system. Our model couples a labor market with a goods market. We first create a stable economy with N agents and study the impact of adding n new workers in the system. The time to reach a new equilibrium market is found to obey a power law in n. The new wages and market prices are observed to decrease as 1/n, whereas the wealth of agents remains unchanged. PMID:29795633

The Study on Collaborative Manufacturing Platform Based on Agent

NASA Astrophysics Data System (ADS)

Zhang, Xiao-yan; Qu, Zheng-geng

To fulfill the trends of knowledge-intensive in collaborative manufacturing development, we have described multi agent architecture supporting knowledge-based platform of collaborative manufacturing development platform. In virtue of wrapper service and communication capacity agents provided, the proposed architecture facilitates organization and collaboration of multi-disciplinary individuals and tools. By effectively supporting the formal representation, capture, retrieval and reuse of manufacturing knowledge, the generalized knowledge repository based on ontology library enable engineers to meaningfully exchange information and pass knowledge across boundaries. Intelligent agent technology increases traditional KBE systems efficiency and interoperability and provides comprehensive design environments for engineers.

Synthesis and time-resolved structural characterization of framework and mineral sulfides

NASA Astrophysics Data System (ADS)

Cahill, Christopher Langley

A new class of open-framework organic/inorganic hybrid materials based on In-S chemistry has been discovered. The compounds therein exhibit unprecedented structural diversity compared to known porous sulfides, primarily due to variation in framework building units. Further, large increases in pore dimensions (vs. zeolites, for example) are observed as these materials consist of comer and edge linked clusters, e.g. In10S20, In9S17, In4S10 and In6S 15. Choice of organic structure directing agents (templates) and careful control of reaction conditions (temperature, pH) both in the In-S and Ge-S systems is shown not only to dictate which building unit will form, but also to direct the resulting framework topology. Several of the compounds described herein crystallize either as powders, or as crystals too small for standard in-house X-ray structural analysis. Diffraction experiments have thus required synchrotron based single crystal techniques for structure determination. Further, certain reaction mixture compositions result in multi-phase end products, the formation pathways of which have been studied with time resolved, in situ synchrotron powder diffraction. An extension of the applicability of the in situ techniques investigated the role of oxygen in hydrothermal systems. Oxidation state is proposed to dictate speciation in the Ni-Ge-S system and to promote phase transformations in the Fe-S mineral system.

Observer-based distributed adaptive iterative learning control for linear multi-agent systems

NASA Astrophysics Data System (ADS)

Li, Jinsha; Liu, Sanyang; Li, Junmin

2017-10-01

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

Research on Production Scheduling System with Bottleneck Based on Multi-agent

NASA Astrophysics Data System (ADS)

Zhenqiang, Bao; Weiye, Wang; Peng, Wang; Pan, Quanke

Aimed at the imbalance problem of resource capacity in Production Scheduling System, this paper uses Production Scheduling System based on multi-agent which has been constructed, and combines the dynamic and autonomous of Agent; the bottleneck problem in the scheduling is solved dynamically. Firstly, this paper uses Bottleneck Resource Agent to find out the bottleneck resource in the production line, analyses the inherent mechanism of bottleneck, and describes the production scheduling process based on bottleneck resource. Bottleneck Decomposition Agent harmonizes the relationship of job's arrival time and transfer time in Bottleneck Resource Agent and Non-Bottleneck Resource Agents, therefore, the dynamic scheduling problem is simplified as the single machine scheduling of each resource which takes part in the scheduling. Finally, the dynamic real-time scheduling problem is effectively solved in Production Scheduling System.

Agent oriented programming: An overview of the framework and summary of recent research

NASA Technical Reports Server (NTRS)

Shoham, Yoav

1993-01-01

This is a short overview of the agent-oriented programming (AOP) framework. AOP can be viewed as an specialization of object-oriented programming. The state of an agent consists of components called beliefs, choices, capabilities, commitments, and possibly others; for this reason the state of an agent is called its mental state. The mental state of agents is captured formally in an extension of standard epistemic logics: beside temporalizing the knowledge and belief operators, AOP introduces operators for commitment, choice and capability. Agents are controlled by agent programs, which include primitives for communicating with other agents. In the spirit of speech-act theory, each communication primitive is of a certain type: informing, requesting, offering, etc. This document describes these features in more detail and summarizes recent results and ongoing AOP-related work.

Superpixel-based segmentation of muscle fibers in multi-channel microscopy.

PubMed

Nguyen, Binh P; Heemskerk, Hans; So, Peter T C; Tucker-Kellogg, Lisa

2016-12-05

Confetti fluorescence and other multi-color genetic labelling strategies are useful for observing stem cell regeneration and for other problems of cell lineage tracing. One difficulty of such strategies is segmenting the cell boundaries, which is a very different problem from segmenting color images from the real world. This paper addresses the difficulties and presents a superpixel-based framework for segmentation of regenerated muscle fibers in mice. We propose to integrate an edge detector into a superpixel algorithm and customize the method for multi-channel images. The enhanced superpixel method outperforms the original and another advanced superpixel algorithm in terms of both boundary recall and under-segmentation error. Our framework was applied to cross-section and lateral section images of regenerated muscle fibers from confetti-fluorescent mice. Compared with "ground-truth" segmentations, our framework yielded median Dice similarity coefficients of 0.92 and higher. Our segmentation framework is flexible and provides very good segmentations of multi-color muscle fibers. We anticipate our methods will be useful for segmenting a variety of tissues in confetti fluorecent mice and in mice with similar multi-color labels.

A Distributed Intelligent E-Learning System

ERIC Educational Resources Information Center

Kristensen, Terje

2016-01-01

An E-learning system based on a multi-agent (MAS) architecture combined with the Dynamic Content Manager (DCM) model of E-learning, is presented. We discuss the benefits of using such a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA). This MAS architecture may also be used within…

Diagnosing Disaster Resilience of Communities as Multi-scale Complex Socio-ecological Systems

NASA Astrophysics Data System (ADS)

Liu, Wei; Mochizuki, Junko; Keating, Adriana; Mechler, Reinhard; Williges, Keith; Hochrainer, Stefan

2014-05-01

Global environmental change, growing anthropogenic influence, and increasing globalisation of society have made it clear that disaster vulnerability and resilience of communities cannot be understood without knowledge on the broader social-ecological system in which they are embedded. We propose a framework for diagnosing community resilience to disasters, as a form of disturbance to social-ecological systems, with feedbacks from the local to the global scale. Inspired by iterative multi-scale analysis employed by Resilience Alliance, the related socio-ecological systems framework of Ostrom, and the sustainable livelihood framework, we developed a multi-tier framework for thinking of communities as multi-scale social-ecological systems and analyzing communities' disaster resilience and also general resilience. We highlight the cross-scale influences and feedbacks on communities that exist from lower (e.g., household) to higher (e.g., regional, national) scales. The conceptual framework is then applied to a real-world resilience assessment situation, to illustrate how key components of socio-ecological systems, including natural hazards, natural and man-made environment, and community capacities can be delineated and analyzed.

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…

Seldon v.3.0

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

Berry, Nina; Ko, Teresa; Shneider, Max

Seldon is an agent-based social simulation framework that uniquely integrates concepts from a variety of different research areas including psychology, social science, and agent-based modeling. Development has been taking place for a number of years, previously focusing on gang and terrorist recruitment. The toolkit consists of simple agents (individuals) and abstract agents (groups of individuals representing social/institutional concepts) that interact according to exchangeable rule sets (i.e. linear attraction, linear reinforcement). Each agent has a set of customizable attributes that get modified during the interactions. Interactions create relationships between agents, and each agent has a maximum amount of relationship energy thatmore » it can expend. As relationships evolve, they form multiple levels of social networks (i.e. acquaintances, friends, cliques) that in turn drive future interactions. Agents can also interact randomly if they are not connected through a network, mimicking the chance interactions that real people have in everyday life. We are currently integrating Seldon with the cognitive framework (also developed at Sandia). Each individual agent has a lightweight cognitive model that is created automatically from textual sources. Cognitive information is exchanged during interactions, and can also be injected into a running simulation. The entire framework has been parallelized to allow for larger simulations in an HPC environment. We have also added more detail to the agents themselves (a"Big Five" personality model) and their interactions (an enhanced relationship model) for a more realistic representation.« less

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

Multi-Sided Markets for Transforming Healthcare Service Delivery.

PubMed

Kuziemsky, Craig; Vimarlund, Vivian

2018-01-01

Changes in healthcare delivery needs have necessitated the design of new models for connecting providers and consumers of services. While healthcare delivery has traditionally been a push market, multi-sided markets offer the potential for transitioning to a pull market for service delivery. However, there is a need to better understand the business model for multi-sided markets as a first step to using them in healthcare. This paper addressed that need and describes a multi-sided market evaluation framework. Our framework identifies patient, governance and service delivery as three levels of brokerage consideration for evaluating multi-sided markets in healthcare.

Multi-agent grid system Agent-GRID with dynamic load balancing of cluster nodes

NASA Astrophysics Data System (ADS)

Satymbekov, M. N.; Pak, I. T.; Naizabayeva, L.; Nurzhanov, Ch. A.

2017-12-01

In this study the work presents the system designed for automated load balancing of the contributor by analysing the load of compute nodes and the subsequent migration of virtual machines from loaded nodes to less loaded ones. This system increases the performance of cluster nodes and helps in the timely processing of data. A grid system balances the work of cluster nodes the relevance of the system is the award of multi-agent balancing for the solution of such problems.

Monte-Carlo Tree Search in Settlers of Catan

NASA Astrophysics Data System (ADS)

Szita, István; Chaslot, Guillaume; Spronck, Pieter

Games are considered important benchmark opportunities for artificial intelligence research. Modern strategic board games can typically be played by three or more people, which makes them suitable test beds for investigating multi-player strategic decision making. Monte-Carlo Tree Search (MCTS) is a recently published family of algorithms that achieved successful results with classical, two-player, perfect-information games such as Go. In this paper we apply MCTS to the multi-player, non-deterministic board game Settlers of Catan. We implemented an agent that is able to play against computer-controlled and human players. We show that MCTS can be adapted successfully to multi-agent environments, and present two approaches of providing the agent with a limited amount of domain knowledge. Our results show that the agent has a considerable playing strength when compared to game implementation with existing heuristics. So, we may conclude that MCTS is a suitable tool for achieving a strong Settlers of Catan player.

Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data.

PubMed

Ozery-Flato, Michal; Yanover, Chen; Gottlieb, Assaf; Weissbrod, Omer; Parush Shear-Yashuv, Naama; Goldschmidt, Yaara

2017-01-01

We present a framework for feature engineering, tailored for longitudinal structured data, such as electronic health records (EHRs). To fast-track feature engineering and extraction, the framework combines general-use plug-in extractors, a multi-cohort management mechanism, and modular memoization. Using this framework, we rapidly extracted thousands of features from diverse and large healthcare data sources in multiple projects.

Multi-objective optimization of radiotherapy: distributed Q-learning and agent-based simulation

NASA Astrophysics Data System (ADS)

Jalalimanesh, Ammar; Haghighi, Hamidreza Shahabi; Ahmadi, Abbas; Hejazian, Hossein; Soltani, Madjid

2017-09-01

Radiotherapy (RT) is among the regular techniques for the treatment of cancerous tumours. Many of cancer patients are treated by this manner. Treatment planning is the most important phase in RT and it plays a key role in therapy quality achievement. As the goal of RT is to irradiate the tumour with adequately high levels of radiation while sparing neighbouring healthy tissues as much as possible, it is a multi-objective problem naturally. In this study, we propose an agent-based model of vascular tumour growth and also effects of RT. Next, we use multi-objective distributed Q-learning algorithm to find Pareto-optimal solutions for calculating RT dynamic dose. We consider multiple objectives and each group of optimizer agents attempt to optimise one of them, iteratively. At the end of each iteration, agents compromise the solutions to shape the Pareto-front of multi-objective problem. We propose a new approach by defining three schemes of treatment planning created based on different combinations of our objectives namely invasive, conservative and moderate. In invasive scheme, we enforce killing cancer cells and pay less attention about irradiation effects on normal cells. In conservative scheme, we take more care of normal cells and try to destroy cancer cells in a less stressed manner. The moderate scheme stands in between. For implementation, each of these schemes is handled by one agent in MDQ-learning algorithm and the Pareto optimal solutions are discovered by the collaboration of agents. By applying this methodology, we could reach Pareto treatment plans through building different scenarios of tumour growth and RT. The proposed multi-objective optimisation algorithm generates robust solutions and finds the best treatment plan for different conditions.

Frameworks to assess health systems governance: a systematic review.

PubMed

Pyone, Thidar; Smith, Helen; van den Broek, Nynke

2017-06-01

Governance of the health system is a relatively new concept and there are gaps in understanding what health system governance is and how it could be assessed. We conducted a systematic review of the literature to describe the concept of governance and the theories underpinning as applied to health systems; and to identify which frameworks are available and have been applied to assess health systems governance. Frameworks were reviewed to understand how the principles of governance might be operationalized at different levels of a health system. Electronic databases and web portals of international institutions concerned with governance were searched for publications in English for the period January 1994 to February 2016. Sixteen frameworks developed to assess governance in the health system were identified and are described. Of these, six frameworks were developed based on theories from new institutional economics; three are primarily informed by political science and public management disciplines; three arise from the development literature and four use multidisciplinary approaches. Only five of the identified frameworks have been applied. These used the principal-agent theory, theory of common pool resources, North's institutional analysis and the cybernetics theory. Governance is a practice, dependent on arrangements set at political or national level, but which needs to be operationalized by individuals at lower levels in the health system; multi-level frameworks acknowledge this. Three frameworks were used to assess governance at all levels of the health system. Health system governance is complex and difficult to assess; the concept of governance originates from different disciplines and is multidimensional. There is a need to validate and apply existing frameworks and share lessons learnt regarding which frameworks work well in which settings. A comprehensive assessment of governance could enable policy makers to prioritize solutions for problems identified as well as replicate and scale-up examples of good practice. © The Author 2017. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.

Frameworks to assess health systems governance: a systematic review

PubMed Central

Smith, Helen; van den Broek, Nynke

2017-01-01

Abstract Governance of the health system is a relatively new concept and there are gaps in understanding what health system governance is and how it could be assessed. We conducted a systematic review of the literature to describe the concept of governance and the theories underpinning as applied to health systems; and to identify which frameworks are available and have been applied to assess health systems governance. Frameworks were reviewed to understand how the principles of governance might be operationalized at different levels of a health system. Electronic databases and web portals of international institutions concerned with governance were searched for publications in English for the period January 1994 to February 2016. Sixteen frameworks developed to assess governance in the health system were identified and are described. Of these, six frameworks were developed based on theories from new institutional economics; three are primarily informed by political science and public management disciplines; three arise from the development literature and four use multidisciplinary approaches. Only five of the identified frameworks have been applied. These used the principal–agent theory, theory of common pool resources, North’s institutional analysis and the cybernetics theory. Governance is a practice, dependent on arrangements set at political or national level, but which needs to be operationalized by individuals at lower levels in the health system; multi-level frameworks acknowledge this. Three frameworks were used to assess governance at all levels of the health system. Health system governance is complex and difficult to assess; the concept of governance originates from different disciplines and is multidimensional. There is a need to validate and apply existing frameworks and share lessons learnt regarding which frameworks work well in which settings. A comprehensive assessment of governance could enable policy makers to prioritize solutions for problems identified as well as replicate and scale-up examples of good practice. PMID:28334991

A multi-scale, multi-disciplinary approach for assessing the technological, economic and environmental performance of bio-based chemicals.

PubMed

Herrgård, Markus; Sukumara, Sumesh; Campodonico, Miguel; Zhuang, Kai

2015-12-01

In recent years, bio-based chemicals have gained interest as a renewable alternative to petrochemicals. However, there is a significant need to assess the technological, biological, economic and environmental feasibility of bio-based chemicals, particularly during the early research phase. Recently, the Multi-scale framework for Sustainable Industrial Chemicals (MuSIC) was introduced to address this issue by integrating modelling approaches at different scales ranging from cellular to ecological scales. This framework can be further extended by incorporating modelling of the petrochemical value chain and the de novo prediction of metabolic pathways connecting existing host metabolism to desirable chemical products. This multi-scale, multi-disciplinary framework for quantitative assessment of bio-based chemicals will play a vital role in supporting engineering, strategy and policy decisions as we progress towards a sustainable chemical industry. © 2015 Authors; published by Portland Press Limited.

A theoretical framework for negotiating the path of emergency management multi-agency coordination.

PubMed

Curnin, Steven; Owen, Christine; Paton, Douglas; Brooks, Benjamin

2015-03-01

Multi-agency coordination represents a significant challenge in emergency management. The need for liaison officers working in strategic level emergency operations centres to play organizational boundary spanning roles within multi-agency coordination arrangements that are enacted in complex and dynamic emergency response scenarios creates significant research and practical challenges. The aim of the paper is to address a gap in the literature regarding the concept of multi-agency coordination from a human-environment interaction perspective. We present a theoretical framework for facilitating multi-agency coordination in emergency management that is grounded in human factors and ergonomics using the methodology of core-task analysis. As a result we believe the framework will enable liaison officers to cope more efficiently within the work domain. In addition, we provide suggestions for extending the theory of core-task analysis to an alternate high reliability environment. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Multi-Targeted Agents in Cancer Cell Chemosensitization: What We Learnt from Curcumin Thus Far.

PubMed

Bordoloi, Devivasha; Roy, Nand K; Monisha, Javadi; Padmavathi, Ganesan; Kunnumakkara, Ajaikumar B

2016-01-01

Research over the past several years has developed many mono-targeted therapies for the prevention and treatment of cancer, but it still remains one of the fatal diseases in the world killing 8.2 million people annually. It has been well-established that development of chemoresistance in cancer cells against mono-targeted chemotherapeutic agents by modulation of multiple survival pathways is the major cause of failure of cancer chemotherapy. Therefore, inhibition of these pathways by non-toxic multi-targeted agents may have profoundly high potential in preventing drug resistance and sensitizing cancer cells to chemotherapeutic agents. To study the potential of curcumin, a multi-targeted natural compound, obtained from the plant Turmeric (Curcuma longa) in combination with standard chemotherapeutic agents to inhibit drug resistance and sensitize cancer cells to these agents based on available literature and patents. An extensive literature survey was performed in PubMed and Google for the chemosensitizing potential of curcumin in different cancers published so far and the patents published during 2014-2015. Our search resulted in many in vitro, in vivo and clinical reports signifying the chemosensitizing potential of curcumin in diverse cancers. There were 160 in vitro studies, 62 in vivo studies and 5 clinical studies. Moreover, 11 studies reported on hybrid curcumin: the next generation of curcumin based therapeutics. Also, 34 patents on curcumin's biological activity have been retrieved. Altogether, the present study reveals the enormous potential of curcumin, a natural, non-toxic, multi-targeted agent in overcoming drug resistance in cancer cells and sensitizing them to chemotherapeutic drugs.

Heme-Containing Metal-Organic Frameworks for the Oxidative Degradation of Chemical Warfare Agents

DTIC Science & Technology

2016-04-14

stability of the oxo without sacrificing its inherent reactivity, we have synthesized a new framework featuring fluorinated groups in the ortho...especially suitable for the degradation of electrophilic phosphorous center, leading to the cleavage of P-S or P-O bond present in VX nerve agents

Using the social structure of markets as a framework for analyzing vaccination debates: The case of emergency polio vaccination

PubMed Central

Connelly, Yaron; Ziv, Arnona; Goren, Uri; Tal, Orna; Kaplan, Giora; Velan, Baruch

2016-01-01

ABSTRACT The framework of the social structure of markets was used to analyze an online debate revolving around an emergency poliovirus vaccination campaign in Israel. Examination of a representative sample of 200 discussions revealed the activity of three parties: authoritative agents promoting vaccinations, alternative agents promoting anti-vaccination, both representing sellers, and the impartial agents, representing the customers—the general public deliberating whether to comply with vaccination or not. Both sellers interacted with consumers using mechanisms of luring and convincing. The authoritative agents conveyed their message by exhibiting professionalism, building trust and offering to share information. The alternative agents spread doubts and evoked negative emotions of distrust and fear. Among themselves, the alternative agents strived to discredit the authoritative agents, while the latter preferred to ignore the former. Content analysis of discussions conducted by the general public reveal reiteration of the messages conveyed by the sellers, implying that the transaction of pro and anti-vaccination ideas indeed took place. We suggest that the framework of the market as a social structure can be applied to the analysis of other vaccination debates, and thereby provide additional insights into vaccination polemics. PMID:27058586

Meeting Report: Pancreatic Cancer Chemoprevention Translational Workshop

PubMed Central

Miller, Mark Steven; Allen, Peter; Brentnall, Teresa; Goggins, Michael; Hruban, Ralph H.; Petersen, Gloria M.; Rao, Chinthalapally V.; Whitcomb, David C.; Brand, Randall E.; Chari, Suresh; Klein, Alison; Lubman, David; Rhim, Andrew; Simeone, Diane M.; Wolpin, Brian; Umar, Asad; Srivastava, Sudhir; Steele, Vernon E.; Ann Rinaudo, Jo

2016-01-01

Pancreatic cancer is the 4th leading cause of cancer related deaths in the US with a 5 year survival rate of <10%. The Division of Cancer Prevention of the NCI sponsored the Pancreatic Cancer Chemoprevention Translational Workshop on September 10–11th 2015. The goal of the workshop was to obtain information regarding the current state of the science and future scientific areas that should be prioritized for pancreatic cancer prevention research, including early detection and intervention for high-risk precancerous lesions. The workshop addressed the molecular/genetic landscape of pancreatic cancer and precursor lesions; high risk populations and criteria to identify a high risk population for potential chemoprevention trials; identification of chemopreventative/immuopreventative agents; and use of potential biomarkers and imaging for assessing short term efficacy of a preventative agent. The field of chemoprevention for pancreatic cancer is emerging and this workshop was organized to begin to address these important issues and promote multi-institutional efforts in this area. The meeting participants recommended the development of an NCI working group to coordinate efforts, provide a framework, and identify opportunities for chemoprevention of pancreatic cancer. PMID:27518363

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 Framework for Multi-Stakeholder Decision-Making and Conflict Resolution (abstract)

EPA Science Inventory

This contribution describes the implementation of the conditional-value-at-risk (CVaR) metric to create a general multi-stakeholder decision-making framework. It is observed that stakeholder dissatisfactions (distance to their individual ideal solutions) can be interpreted as ran...

A new web-based framework development for fuzzy multi-criteria group decision-making.

PubMed

Hanine, Mohamed; Boutkhoum, Omar; Tikniouine, Abdessadek; Agouti, Tarik

2016-01-01

Fuzzy multi-criteria group decision making (FMCGDM) process is usually used when a group of decision-makers faces imprecise data or linguistic variables to solve the problems. However, this process contains many methods that require many time-consuming calculations depending on the number of criteria, alternatives and decision-makers in order to reach the optimal solution. In this study, a web-based FMCGDM framework that offers decision-makers a fast and reliable response service is proposed. The proposed framework includes commonly used tools for multi-criteria decision-making problems such as fuzzy Delphi, fuzzy AHP and fuzzy TOPSIS methods. The integration of these methods enables taking advantages of the strengths and complements each method's weakness. Finally, a case study of location selection for landfill waste in Morocco is performed to demonstrate how this framework can facilitate decision-making process. The results demonstrate that the proposed framework can successfully accomplish the goal of this study.

Field-scale multi-phase LNAPL remediation: Validating a new computational framework against sequential field pilot trials.

PubMed

Sookhak Lari, Kaveh; Johnston, Colin D; Rayner, John L; Davis, Greg B

2018-03-05

Remediation of subsurface systems, including groundwater, soil and soil gas, contaminated with light non-aqueous phase liquids (LNAPLs) is challenging. Field-scale pilot trials of multi-phase remediation were undertaken at a site to determine the effectiveness of recovery options. Sequential LNAPL skimming and vacuum-enhanced skimming, with and without water table drawdown were trialled over 78days; in total extracting over 5m 3 of LNAPL. For the first time, a multi-component simulation framework (including the multi-phase multi-component code TMVOC-MP and processing codes) was developed and applied to simulate the broad range of multi-phase remediation and recovery methods used in the field trials. This framework was validated against the sequential pilot trials by comparing predicted and measured LNAPL mass removal rates and compositional changes. The framework was tested on both a Cray supercomputer and a cluster. Simulations mimicked trends in LNAPL recovery rates (from 0.14 to 3mL/s) across all remediation techniques each operating over periods of 4-14days over the 78day trial. The code also approximated order of magnitude compositional changes of hazardous chemical concentrations in extracted gas during vacuum-enhanced recovery. The verified framework enables longer term prediction of the effectiveness of remediation approaches allowing better determination of remediation endpoints and long-term risks. Copyright © 2017 Commonwealth Scientific and Industrial Research Organisation. Published by Elsevier B.V. All rights reserved.

Resolving the Framework Position of Organic Structure-Directing Agents in Hierarchical Zeolites via Polarized Stimulated Raman Scattering.

PubMed

Fleury, Guillaume; Steele, Julian A; Gerber, Iann C; Jolibois, F; Puech, P; Muraoka, Koki; Keoh, Sye Hoe; Chaikittisilp, Watcharop; Okubo, Tatsuya; Roeffaers, Maarten B J

2018-04-05

The direct synthesis of hierarchically intergrown silicalite-1 can be achieved using a specific diquaternary ammonium agent. However, the location of these molecules in the zeolite framework, which is critical to understand the formation of the material, remains unclear. Where traditional characterization tools have previously failed, herein we use polarized stimulated Raman scattering (SRS) microscopy to resolve molecular organization inside few-micron-sized crystals. Through a combination of experiment and first-principles calculations, our investigation reveals the preferential location of the templating agent inside the linear pores of the MFI framework. Besides illustrating the attractiveness of SRS microscopy in the field of material science to study and spatially resolve local molecular distribution as well as orientation, these results can be exploited in the design of new templating agents for the preparation of hierarchical zeolites.

Multi-sources data fusion framework for remote triage prioritization in telehealth.

PubMed

Salman, O H; Rasid, M F A; Saripan, M I; Subramaniam, S K

2014-09-01

The healthcare industry is streamlining processes to offer more timely and effective services to all patients. Computerized software algorithm and smart devices can streamline the relation between users and doctors by providing more services inside the healthcare telemonitoring systems. This paper proposes a multi-sources framework to support advanced healthcare applications. The proposed framework named Multi Sources Healthcare Architecture (MSHA) considers multi-sources: sensors (ECG, SpO2 and Blood Pressure) and text-based inputs from wireless and pervasive devices of Wireless Body Area Network. The proposed framework is used to improve the healthcare scalability efficiency by enhancing the remote triaging and remote prioritization processes for the patients. The proposed framework is also used to provide intelligent services over telemonitoring healthcare services systems by using data fusion method and prioritization technique. As telemonitoring system consists of three tiers (Sensors/ sources, Base station and Server), the simulation of the MSHA algorithm in the base station is demonstrated in this paper. The achievement of a high level of accuracy in the prioritization and triaging patients remotely, is set to be our main goal. Meanwhile, the role of multi sources data fusion in the telemonitoring healthcare services systems has been demonstrated. In addition to that, we discuss how the proposed framework can be applied in a healthcare telemonitoring scenario. Simulation results, for different symptoms relate to different emergency levels of heart chronic diseases, demonstrate the superiority of our algorithm compared with conventional algorithms in terms of classify and prioritize the patients remotely.

A framework for learning and planning against switching strategies in repeated games

NASA Astrophysics Data System (ADS)

Hernandez-Leal, Pablo; Munoz de Cote, Enrique; Sucar, L. Enrique

2014-04-01

Intelligent agents, human or artificial, often change their behaviour as they interact with other agents. For an agent to optimise its performance when interacting with such agents, it must be capable of detecting and adapting according to such changes. This work presents an approach on how to effectively deal with non-stationary switching opponents in a repeated game context. Our main contribution is a framework for online learning and planning against opponents that switch strategies. We present how two opponent modelling techniques work within the framework and prove the usefulness of the approach experimentally in the iterated prisoner's dilemma, when the opponent is modelled as an agent that switches between different strategies (e.g. TFT, Pavlov and Bully). The results of both models were compared against each other and against a state-of-the-art non-stationary reinforcement learning technique. Results reflect that our approach obtains competitive results without needing an offline training phase, as opposed to the state-of-the-art techniques.

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.

The evolution of gadolinium based contrast agents: from single-modality to multi-modality

NASA Astrophysics Data System (ADS)

Zhang, Li; Liu, Ruiqing; Peng, Hui; Li, Penghui; Xu, Zushun; Whittaker, Andrew K.

2016-05-01

Gadolinium-based contrast agents are extensively used as magnetic resonance imaging (MRI) contrast agents due to their outstanding signal enhancement and ease of chemical modification. However, it is increasingly recognized that information obtained from single modal molecular imaging cannot satisfy the higher requirements on the efficiency and accuracy for clinical diagnosis and medical research, due to its limitation and default rooted in single molecular imaging technique itself. To compensate for the deficiencies of single function magnetic resonance imaging contrast agents, the combination of multi-modality imaging has turned to be the research hotpot in recent years. This review presents an overview on the recent developments of the functionalization of gadolinium-based contrast agents, and their application in biomedicine applications.

A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

NASA Technical Reports Server (NTRS)

Josephson, John R.

1989-01-01

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation.

Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange

PubMed Central

Hula, Andreas; Montague, P. Read; Dayan, Peter

2015-01-01

Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent’s preference for equity with their partner, beliefs about the partner’s appetite for equity, beliefs about the partner’s model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference. PMID:26053429

Leveraging Citizen Science and Information Technology for Population Physical Activity Promotion.

PubMed

King, Abby C; Winter, Sandra J; Sheats, Jylana L; Rosas, Lisa G; Buman, Matthew P; Salvo, Deborah; Rodriguez, Nicole M; Seguin, Rebecca A; Moran, Mika; Garber, Randi; Broderick, Bonnie; Zieff, Susan G; Sarmiento, Olga Lucia; Gonzalez, Silvia A; Banchoff, Ann; Dommarco, Juan Rivera

2016-05-15

While technology is a major driver of many of society's comforts, conveniences, and advances, it has been responsible, in a significant way, for engineering regular physical activity and a number of other positive health behaviors out of people's daily lives. A key question concerns how to harness information and communication technologies (ICT) to bring about positive changes in the health promotion field. One such approach involves community-engaged "citizen science," in which local residents leverage the potential of ICT to foster data-driven consensus-building and mobilization efforts that advance physical activity at the individual, social, built environment, and policy levels. The history of citizen science in the research arena is briefly described and an evidence-based method that embeds citizen science in a multi-level, multi-sectoral community-based participatory research framework for physical activity promotion is presented. Several examples of this citizen science-driven community engagement framework for promoting active lifestyles, called "Our Voice", are discussed, including pilot projects from diverse communities in the U.S. as well as internationally. The opportunities and challenges involved in leveraging citizen science activities as part of a broader population approach to promoting regular physical activity are explored. The strategic engagement of citizen scientists from socio-demographically diverse communities across the globe as both assessment as well as change agents provides a promising, potentially low-cost and scalable strategy for creating more active, healthful, and equitable neighborhoods and communities worldwide.

Predicting kinetic nanocrystal shapes through multi-scale theory and simulation: Polyvinylpyrrolidone-mediated growth of Ag nanocrystals

NASA Astrophysics Data System (ADS)

Balankura, Tonnam; Qi, Xin; Zhou, Ya; Fichthorn, Kristen A.

2016-10-01

In the shape-controlled synthesis of colloidal Ag nanocrystals, structure-directing agents, particularly polyvinylpyrrolidone (PVP), are known to be a key additive in making nanostructures with well-defined shapes. Although many Ag nanocrystals have been successfully synthesized using PVP, the mechanism by which PVP actuates shape control remains elusive. Here, we present a multi-scale theoretical framework for kinetic Wulff shape predictions that accounts for the chemical environment, which we used to probe the kinetic influence of the adsorbed PVP film. Within this framework, we use umbrella-sampling molecular dynamics simulations to calculate the potential of mean force and diffusion coefficient profiles of Ag atom deposition onto Ag(100) and Ag(111) in ethylene glycol solution with surface-adsorbed PVP. We use these profiles to calculate the mean-first passage times and implement extensive Brownian dynamics simulations, which allows the kinetic effects to be quantitatively evaluated. Our results show that PVP films can regulate the flux of Ag atoms to be greater towards Ag(111) than Ag(100). PVP's preferential binding towards Ag(100) over Ag(111) gives PVP its flux-regulating capabilities through the lower free-energy barrier of Ag atoms to cross the lower-density PVP film on Ag(111) and enhanced Ag trapping by the extended PVP film on Ag(111). Under kinetic control, {100}-faceted nanocrystals will be formed when the Ag flux is greater towards Ag(111). The predicted kinetic Wulff shapes are in agreement with the analogous experimental system.

Leveraging Citizen Science and Information Technology for Population Physical Activity Promotion

PubMed Central

King, Abby C.; Winter, Sandra J.; Sheats, Jylana L.; Rosas, Lisa G.; Buman, Matthew P.; Salvo, Deborah; Rodriguez, Nicole M.; Seguin, Rebecca A.; Moran, Mika; Garber, Randi; Broderick, Bonnie; Zieff, Susan G.; Sarmiento, Olga Lucia; Gonzalez, Silvia A.; Banchoff, Ann; Dommarco, Juan Rivera

2016-01-01

PURPOSE While technology is a major driver of many of society’s comforts, conveniences, and advances, it has been responsible, in a significant way, for engineering regular physical activity and a number of other positive health behaviors out of people’s daily lives. A key question concerns how to harness information and communication technologies (ICT) to bring about positive changes in the health promotion field. One such approach involves community-engaged “citizen science,” in which local residents leverage the potential of ICT to foster data-driven consensus-building and mobilization efforts that advance physical activity at the individual, social, built environment, and policy levels. METHOD The history of citizen science in the research arena is briefly described and an evidence-based method that embeds citizen science in a multi-level, multi-sectoral community-based participatory research framework for physical activity promotion is presented. RESULTS Several examples of this citizen science-driven community engagement framework for promoting active lifestyles, called “Our Voice”, are discussed, including pilot projects from diverse communities in the U.S. as well as internationally. CONCLUSIONS The opportunities and challenges involved in leveraging citizen science activities as part of a broader population approach to promoting regular physical activity are explored. The strategic engagement of citizen scientists from socio-demographically diverse communities across the globe as both assessment as well as change agents provides a promising, potentially low-cost and scalable strategy for creating more active, healthful, and equitable neighborhoods and communities worldwide. PMID:27525309

Emotional Multiagent Reinforcement Learning in Spatial Social Dilemmas.

PubMed

Yu, Chao; Zhang, Minjie; Ren, Fenghui; Tan, Guozhen

2015-12-01

Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. In particular, the spatial version of social dilemmas is considered to study the impact of local interactions on the emergence of cooperation in the whole system. A double-layered emotional multiagent reinforcement learning framework is proposed to endow agents with internal cognitive and emotional capabilities that can drive these agents to learn cooperative behaviors. Experimental results reveal that various network topologies and agent heterogeneities have significant impacts on agent learning behaviors in the proposed framework, and under certain circumstances, high levels of cooperation can be achieved among the agents.

Prescribed performance distributed consensus control for nonlinear multi-agent systems with unknown dead-zone input

NASA Astrophysics Data System (ADS)

Cui, Guozeng; Xu, Shengyuan; Ma, Qian; Li, Yongmin; Zhang, Zhengqiang

2018-05-01

In this paper, the problem of prescribed performance distributed output consensus for higher-order non-affine nonlinear multi-agent systems with unknown dead-zone input is investigated. Fuzzy logical systems are utilised to identify the unknown nonlinearities. By introducing prescribed performance, the transient and steady performance of synchronisation errors are guaranteed. Based on Lyapunov stability theory and the dynamic surface control technique, a new distributed consensus algorithm for non-affine nonlinear multi-agent systems is proposed, which ensures cooperatively uniformly ultimately boundedness of all signals in the closed-loop systems and enables the output of each follower to synchronise with the leader within predefined bounded error. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Finite-time synchronization for second-order nonlinear multi-agent system via pinning exponent sliding mode control.

PubMed

Hou, Huazhou; Zhang, Qingling

2016-11-01

In this paper we investigate the finite-time synchronization for second-order multi-agent system via pinning exponent sliding mode control. Firstly, for the nonlinear multi-agent system, differential mean value theorem is employed to transfer the nonlinear system into linear system, then, by pinning only one node in the system with novel exponent sliding mode control, we can achieve synchronization in finite time. Secondly, considering the 3-DOF helicopter system with nonlinear dynamics and disturbances, the novel exponent sliding mode control protocol is applied to only one node to achieve the synchronization. Finally, the simulation results show the effectiveness and the advantages of the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Research on e-commerce transaction networks using multi-agent modelling and open application programming interface

NASA Astrophysics Data System (ADS)

Piao, Chunhui; Han, Xufang; Wu, Harris

2010-08-01

We provide a formal definition of an e-commerce transaction network. Agent-based modelling is used to simulate e-commerce transaction networks. For real-world analysis, we studied the open application programming interfaces (APIs) from eBay and Taobao e-commerce websites and captured real transaction data. Pajek is used to visualise the agent relationships in the transaction network. We derived one-mode networks from the transaction network and analysed them using degree and betweenness centrality. Integrating multi-agent modelling, open APIs and social network analysis, we propose a new way to study large-scale e-commerce systems.

Robust adaptive fault-tolerant control for leader-follower flocking of uncertain multi-agent systems with actuator failure.

PubMed

Yazdani, Sahar; Haeri, Mohammad

2017-11-01

In this work, we study the flocking problem of multi-agent systems with uncertain dynamics subject to actuator failure and external disturbances. By considering some standard assumptions, we propose a robust adaptive fault tolerant protocol for compensating of the actuator bias fault, the partial loss of actuator effectiveness fault, the model uncertainties, and external disturbances. Under the designed protocol, velocity convergence of agents to that of virtual leader is guaranteed while the connectivity preservation of network and collision avoidance among agents are ensured as well. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Algorithms of walking and stability for an anthropomorphic robot

NASA Astrophysics Data System (ADS)

Sirazetdinov, R. T.; Devaev, V. M.; Nikitina, D. V.; Fadeev, A. Y.; Kamalov, A. R.

2017-09-01

Autonomous movement of an anthropomorphic robot is considered as a superposition of a set of typical elements of movement - so-called patterns, each of which can be considered as an agent of some multi-agent system [ 1 ]. To control the AP-601 robot, an information and communication infrastructure has been created that represents some multi-agent system that allows the development of algorithms for individual patterns of moving and run them in the system as a set of independently executed and interacting agents. The algorithms of lateral movement of the anthropomorphic robot AP-601 series with active stability due to the stability pattern are presented.

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.

Pattern-oriented modeling of agent-based complex systems: Lessons from ecology

USGS Publications Warehouse

Grimm, Volker; Revilla, Eloy; Berger, Uta; Jeltsch, Florian; Mooij, Wolf M.; Railsback, Steven F.; Thulke, Hans-Hermann; Weiner, Jacob; Wiegand, Thorsten; DeAngelis, Donald L.

2005-01-01

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology

NASA Astrophysics Data System (ADS)

Grimm, Volker; Revilla, Eloy; Berger, Uta; Jeltsch, Florian; Mooij, Wolf M.; Railsback, Steven F.; Thulke, Hans-Hermann; Weiner, Jacob; Wiegand, Thorsten; DeAngelis, Donald L.

2005-11-01

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

Space-Time Processing for Tactical Mobile Ad Hoc Networks

DTIC Science & Technology

2007-08-01

rates in mobile ad hoc networks. In addition, he has considered the design of a cross-layer multi-user resource allocation framework using a... framework for many-to-one communication. In this context, multiple nodes cooperate to transmit their packets simultaneously to a single node using multi...spatially multiplexed signals transmitted from multiple nodes. Our goal is to form a framework that activates different sets of communication links

Process synthesis involving multi-period operations by the P-graph framework

EPA Science Inventory

The P-graph (process graph) framework is an effective tool for process-network synthesis (PNS). Here we extended it to multi-period operations. The efficacy of the P-graph methodology has been demonstrated by numerous applications. The unambiguous representation of processes and ...

From research to practice: an integrative framework for the development of interventions for children with fetal alcohol spectrum disorders.

PubMed

Kodituwakku, Piyadasa W; Kodituwakku, E Louise

2011-06-01

Since fetal alcohol syndrome was first described over 35 years ago, considerable progress has been made in the delineation of the neurocognitive profile in children with prenatal alcohol exposure. Preclinical investigators have made impressive strides in elucidating the mechanisms of alcohol teratogenesis and in testing the effectiveness of pharmacological agents and dietary supplementation in the amelioration of alcohol-induced deficits. Despite these advances, only limited progress has been made in the development of evidence-based comprehensive interventions for functional impairment in alcohol-exposed children. Having performed a search in PubMed and PsycINFO using key words, interventions, treatment, fetal alcohol syndrome, prenatal alcohol exposure, and fetal alcohol spectrum disorders, we found only 12 papers on empirically-based interventions. Only two of these interventions had been replicated and none met the criteria of "well-established," as defined by Chambless and Hollon (Journal of Consulting and Clinical Psychology 66(1):7-18, 1998). There has been only limited cross-fertilization of ideas between preclinical and clinical research with regard to the development of interventions. Therefore, we propose a framework that allows integrating data from preclinical and clinical investigations to develop comprehensive intervention programs for children with fetal alcohol spectrum disorders. This framework underscores the importance of multi-level evaluations and interventions.

Designing multi-targeted agents: An emerging anticancer drug discovery paradigm.

PubMed

Fu, Rong-Geng; Sun, Yuan; Sheng, Wen-Bing; Liao, Duan-Fang

2017-08-18

The dominant paradigm in drug discovery is to design ligands with maximum selectivity to act on individual drug targets. With the target-based approach, many new chemical entities have been discovered, developed, and further approved as drugs. However, there are a large number of complex diseases such as cancer that cannot be effectively treated or cured only with one medicine to modulate the biological function of a single target. As simultaneous intervention of two (or multiple) cancer progression relevant targets has shown improved therapeutic efficacy, the innovation of multi-targeted drugs has become a promising and prevailing research topic and numerous multi-targeted anticancer agents are currently at various developmental stages. However, most multi-pharmacophore scaffolds are usually discovered by serendipity or screening, while rational design by combining existing pharmacophore scaffolds remains an enormous challenge. In this review, four types of multi-pharmacophore modes are discussed, and the examples from literature will be used to introduce attractive lead compounds with the capability of simultaneously interfering with different enzyme or signaling pathway of cancer progression, which will reveal the trends and insights to help the design of the next generation multi-targeted anticancer agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Formal Modeling of Multi-Agent Systems using the Pi-Calculus and Epistemic Logic

NASA Technical Reports Server (NTRS)

Rorie, Toinette; Esterline, Albert

1998-01-01

Multi-agent systems have become important recently in computer science, especially in artificial intelligence (AI). We allow a broad sense of agent, but require at least that an agent has some measure of autonomy and interacts with other agents via some kind of agent communication language. We are concerned in this paper with formal modeling of multi-agent systems, with emphasis on communication. We propose for this purpose to use the pi-calculus, an extension of the process algebra CCS. Although the literature on the pi-calculus refers to agents, the term is used there in the sense of a process in general. It is our contention, however, that viewing agents in the AI sense as agents in the pi-calculus sense affords significant formal insight. One formalism that has been applied to agents in the AI sense is epistemic logic, the logic of knowledge. The success of epistemic logic in computer science in general has come in large part from its ability to handle concepts of knowledge that apply to groups. We maintain that the pi-calculus affords a natural yet rigorous means by which groups that are significant to epistemic logic may be identified, encapsulated, structured into hierarchies, and restructured in a principled way. This paper is organized as follows: Section 2 introduces the pi-calculus; Section 3 takes a scenario from the classical paper on agent-oriented programming [Sh93] and translates it into a very simple subset of the n-calculus; Section 4 then shows how more sophisticated features of the pi-calculus may bc brought into play; Section 5 discusses how the pi-calculus may be used to define groups for epistemic logic; and Section 6 is the conclusion.

Computation of the target state and feedback controls for time optimal consensus in multi-agent systems

NASA Astrophysics Data System (ADS)

Mulla, Ameer K.; Patil, Deepak U.; Chakraborty, Debraj

2018-02-01

N identical agents with bounded inputs aim to reach a common target state (consensus) in the minimum possible time. Algorithms for computing this time-optimal consensus point, the control law to be used by each agent and the time taken for the consensus to occur, are proposed. Two types of multi-agent systems are considered, namely (1) coupled single-integrator agents on a plane and, (2) double-integrator agents on a line. At the initial time instant, each agent is assumed to have access to the state information of all the other agents. An algorithm, using convexity of attainable sets and Helly's theorem, is proposed, to compute the final consensus target state and the minimum time to achieve this consensus. Further, parts of the computation are parallelised amongst the agents such that each agent has to perform computations of O(N2) run time complexity. Finally, local feedback time-optimal control laws are synthesised to drive each agent to the target point in minimum time. During this part of the operation, the controller for each agent uses measurements of only its own states and does not need to communicate with any neighbouring agents.

A Biologically Inspired Cooperative Multi-Robot Control Architecture

NASA Technical Reports Server (NTRS)

Howsman, Tom; Craft, Mike; ONeil, Daniel; Howell, Joe T. (Technical Monitor)

2002-01-01

A prototype cooperative multi-robot control architecture suitable for the eventual construction of large space structures has been developed. In nature, there are numerous examples of complex architectures constructed by relatively simple insects, such as termites and wasps, which cooperatively assemble their nests. The prototype control architecture emulates this biological model. Actions of each of the autonomous robotic construction agents are only indirectly coordinated, thus mimicking the distributed construction processes of various social insects. The robotic construction agents perform their primary duties stigmergically i.e., without direct inter-agent communication and without a preprogrammed global blueprint of the final design. Communication and coordination between individual agents occurs indirectly through the sensed modifications that each agent makes to the structure. The global stigmergic building algorithm prototyped during the initial research assumes that the robotic builders only perceive the current state of the structure under construction. Simulation studies have established that an idealized form of the proposed architecture was indeed capable of producing representative large space structures with autonomous robots. This paper will explore the construction simulations in order to illustrate the multi-robot control architecture.

A Stigmergic Cooperative Multi-Robot Control Architecture

NASA Technical Reports Server (NTRS)

Howsman, Thomas G.; O'Neil, Daniel; Craft, Michael A.

2004-01-01

In nature, there are numerous examples of complex architectures constructed by relatively simple insects, such as termites and wasps, which cooperatively assemble their nests. A prototype cooperative multi-robot control architecture which may be suitable for the eventual construction of large space structures has been developed which emulates this biological model. Actions of each of the autonomous robotic construction agents are only indirectly coordinated, thus mimicking the distributed construction processes of various social insects. The robotic construction agents perform their primary duties stigmergically, i.e., without direct inter-agent communication and without a preprogrammed global blueprint of the final design. Communication and coordination between individual agents occurs indirectly through the sensed modifications that each agent makes to the structure. The global stigmergic building algorithm prototyped during the initial research assumes that the robotic builders only perceive the current state of the structure under construction. Simulation studies have established that an idealized form of the proposed architecture was indeed capable of producing representative large space structures with autonomous robots. This paper will explore the construction simulations in order to illustrate the multi-robot control architecture.

a Simulation-As Framework Facilitating Webgis Based Installation Planning

NASA Astrophysics Data System (ADS)

Zheng, Z.; Chang, Z. Y.; Fei, Y. F.

2017-09-01

Installation Planning is constrained by both natural and social conditions, especially for spatially sparse but functionally connected facilities. Simulation is important for proper deploy in space and configuration in function of facilities to make them a cohesive and supportive system to meet users' operation needs. Based on requirement analysis, we propose a framework to combine GIS and Agent simulation to overcome the shortness in temporal analysis and task simulation of traditional GIS. In this framework, Agent based simulation runs as a service on the server, exposes basic simulation functions, such as scenario configuration, simulation control, and simulation data retrieval to installation planners. At the same time, the simulation service is able to utilize various kinds of geoprocessing services in Agents' process logic to make sophisticated spatial inferences and analysis. This simulation-as-a-service framework has many potential benefits, such as easy-to-use, on-demand, shared understanding, and boosted performances. At the end, we present a preliminary implement of this concept using ArcGIS javascript api 4.0 and ArcGIS for server, showing how trip planning and driving can be carried out by agents.

Designing Agent Utilities for Coordinated, Scalable and Robust Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Tumer, Kagan

2005-01-01

Coordinating the behavior of a large number of agents to achieve a system level goal poses unique design challenges. In particular, problems of scaling (number of agents in the thousands to tens of thousands), observability (agents have limited sensing capabilities), and robustness (the agents are unreliable) make it impossible to simply apply methods developed for small multi-agent systems composed of reliable agents. To address these problems, we present an approach based on deriving agent goals that are aligned with the overall system goal, and can be computed using information readily available to the agents. Then, each agent uses a simple reinforcement learning algorithm to pursue its own goals. Because of the way in which those goals are derived, there is no need to use difficult to scale external mechanisms to force collaboration or coordination among the agents, or to ensure that agents actively attempt to appropriate the tasks of agents that suffered failures. To present these results in a concrete setting, we focus on the problem of finding the sub-set of a set of imperfect devices that results in the best aggregate device. This is a large distributed agent coordination problem where each agent (e.g., device) needs to determine whether to be part of the aggregate device. Our results show that the approach proposed in this work provides improvements of over an order of magnitude over both traditional search methods and traditional multi-agent methods. Furthermore, the results show that even in extreme cases of agent failures (i.e., half the agents failed midway through the simulation) the system's performance degrades gracefully and still outperforms a failure-free and centralized search algorithm. The results also show that the gains increase as the size of the system (e.g., number of agents) increases. This latter result is particularly encouraging and suggests that this method is ideally suited for domains where the number of agents is currently in the thousands and will reach tens or hundreds of thousands in the near future.

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.

Multi-level multi-task learning for modeling cross-scale interactions in nested geospatial data

USGS Publications Warehouse

Yuan, Shuai; Zhou, Jiayu; Tan, Pang-Ning; Fergus, Emi; Wagner, Tyler; Sorrano, Patricia

2017-01-01

Predictive modeling of nested geospatial data is a challenging problem as the models must take into account potential interactions among variables defined at different spatial scales. These cross-scale interactions, as they are commonly known, are particularly important to understand relationships among ecological properties at macroscales. In this paper, we present a novel, multi-level multi-task learning framework for modeling nested geospatial data in the lake ecology domain. Specifically, we consider region-specific models to predict lake water quality from multi-scaled factors. Our framework enables distinct models to be developed for each region using both its local and regional information. The framework also allows information to be shared among the region-specific models through their common set of latent factors. Such information sharing helps to create more robust models especially for regions with limited or no training data. In addition, the framework can automatically determine cross-scale interactions between the regional variables and the local variables that are nested within them. Our experimental results show that the proposed framework outperforms all the baseline methods in at least 64% of the regions for 3 out of 4 lake water quality datasets evaluated in this study. Furthermore, the latent factors can be clustered to obtain a new set of regions that is more aligned with the response variables than the original regions that were defined a priori from the ecology domain.

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.

Towards a Bio-inspired Security Framework for Mission-Critical Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Ren, Wei; Song, Jun; Ma, Zhao; Huang, Shiyong

Mission-critical wireless sensor networks (WSNs) have been found in numerous promising applications in civil and military fields. However, the functionality of WSNs extensively relies on its security capability for detecting and defending sophisticated adversaries, such as Sybil, worm hole and mobile adversaries. In this paper, we propose a bio-inspired security framework to provide intelligence-enabled security mechanisms. This scheme is composed of a middleware, multiple agents and mobile agents. The agents monitor the network packets, host activities, make decisions and launch corresponding responses. Middleware performs an infrastructure for the communication between various agents and corresponding mobility. Certain cognitive models and intelligent algorithms such as Layered Reference Model of Brain and Self-Organizing Neural Network with Competitive Learning are explored in the context of sensor networks that have resource constraints. The security framework and implementation are also described in details.

Application of a Genetic Algorithm and Multi Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the Workplace

DTIC Science & Technology

2008-06-01

postponed the fulfillment of her own Masters Degree by at least 18 months so that I would have the opportunity to earn mine. She is smart , lovely...GENETIC ALGORITHM AND MULTI AGENT SYSTEM TO EXPLORE EMERGENT PATTERNS OF SOCIAL RATIONALITY AND A DISTRESS-BASED MODEL FOR DECEIT IN THE WORKPLACE...of a Genetic Algorithm and Mutli Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the

Finite-time consensus for multi-agent systems with globally bounded convergence time under directed communication graphs

NASA Astrophysics Data System (ADS)

Fu, Junjie; Wang, Jin-zhi

2017-09-01

In this paper, we study the finite-time consensus problems with globally bounded convergence time also known as fixed-time consensus problems for multi-agent systems subject to directed communication graphs. Two new distributed control strategies are proposed such that leaderless and leader-follower consensus are achieved with convergence time independent on the initial conditions of the agents. Fixed-time formation generation and formation tracking problems are also solved as the generalizations. Simulation examples are provided to demonstrate the performance of the new controllers.

Cultural Geography Model Validation

DTIC Science & Technology

2010-03-01

the Cultural Geography Model (CGM), a government owned, open source multi - agent system utilizing Bayesian networks, queuing systems, the Theory of...referent determined either from theory or SME opinion. 4. CGM Overview The CGM is a government-owned, open source, data driven multi - agent social...HSCB, validation, social network analysis ABSTRACT: In the current warfighting environment , the military needs robust modeling and simulation (M&S

Information-Theoretic Approach May Shed a Light to a Better Understanding and Sustaining the Integrity of Ecological-Societal Systems under Changing Climate

NASA Astrophysics Data System (ADS)

Kim, J.

2016-12-01

Considering high levels of uncertainty, epistemological conflicts over facts and values, and a sense of urgency, normal paradigm-driven science will be insufficient to mobilize people and nation toward sustainability. The conceptual framework to bridge the societal system dynamics with that of natural ecosystems in which humanity operates remains deficient. The key to understanding their coevolution is to understand `self-organization.' Information-theoretic approach may shed a light to provide a potential framework which enables not only to bridge human and nature but also to generate useful knowledge for understanding and sustaining the integrity of ecological-societal systems. How can information theory help understand the interface between ecological systems and social systems? How to delineate self-organizing processes and ensure them to fulfil sustainability? How to evaluate the flow of information from data through models to decision-makers? These are the core questions posed by sustainability science in which visioneering (i.e., the engineering of vision) is an essential framework. Yet, visioneering has neither quantitative measure nor information theoretic framework to work with and teach. This presentation is an attempt to accommodate the framework of self-organizing hierarchical open systems with visioneering into a common information-theoretic framework. A case study is presented with the UN/FAO's communal vision of climate-smart agriculture (CSA) which pursues a trilemma of efficiency, mitigation, and resilience. Challenges of delineating and facilitating self-organizing systems are discussed using transdisciplinary toold such as complex systems thinking, dynamic process network analysis and multi-agent systems modeling. Acknowledgments: This study was supported by the Korea Meteorological Administration Research and Development Program under Grant KMA-2012-0001-A (WISE project).

Multi-Scale Multi-Domain Model | Transportation Research | NREL

Science.gov Websites

framework for NREL's MSMD model. NREL's MSMD model quantifies the impacts of electrical/thermal pathway : NREL Macroscopic design factors and highly dynamic environmental conditions significantly influence the design of affordable, long-lasting, high-performing, and safe large battery systems. The MSMD framework

A multi-agent approach to intelligent monitoring in smart grids

NASA Astrophysics Data System (ADS)

Vallejo, D.; Albusac, J.; Glez-Morcillo, C.; Castro-Schez, J. J.; Jiménez, L.

2014-04-01

In this paper, we propose a scalable multi-agent architecture to give support to smart grids, paying special attention to the intelligent monitoring of distribution substations. The data gathered by multiple sensors are used by software agents that are responsible for monitoring different aspects or events of interest, such as normal voltage values or unbalanced intensity values that can end up blowing fuses and decreasing the quality of service of end consumers. The knowledge bases of these agents have been built by means of a formal model for normality analysis that has been successfully used in other surveillance domains. The architecture facilitates the integration of new agents and can be easily configured and deployed to monitor different environments. The experiments have been conducted over a power distribution network.

Bipartite flocking for multi-agent systems

NASA Astrophysics Data System (ADS)

Fan, Ming-Can; Zhang, Hai-Tao; Wang, Miaomiao

2014-09-01

This paper addresses the bipartite flock control problem where a multi-agent system splits into two clusters upon internal or external excitations. Using structurally balanced signed graph theory, LaSalle's invariance principle and Barbalat's Lemma, we prove that the proposed algorithm guarantees a bipartite flocking behavior. In each of the two disjoint clusters, all individuals move with the same direction. Meanwhile, every pair of agents in different clusters moves with opposite directions. Moreover, all agents in the two separated clusters approach a common velocity magnitude, and collision avoidance among all agents is ensured as well. Finally, the proposed bipartite flock control method is examined by numerical simulations. The bipartite flocking motion addressed by this paper has its references in both natural collective motions and human group behaviors such as predator-prey and panic escaping scenarios.

Swarming behaviors in multi-agent systems with nonlinear dynamics

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

Yu, Wenwu, E-mail: wenwuyu@gmail.com; School of Electrical and Computer Engineering, RMIT University, Melbourne VIC 3001; Chen, Guanrong

2013-12-15

The dynamic analysis of a continuous-time multi-agent swarm model with nonlinear profiles is investigated in this paper. It is shown that, under mild conditions, all agents in a swarm can reach cohesion within a finite time, where the upper bounds of the cohesion are derived in terms of the parameters of the swarm model. The results are then generalized by considering stochastic noise and switching between nonlinear profiles. Furthermore, swarm models with limited sensing range inducing changing communication topologies and unbounded repulsive interactions between agents are studied by switching system and nonsmooth analysis. Here, the sensing range of each agentmore » is limited and the possibility of collision among nearby agents is high. Finally, simulation results are presented to demonstrate the validity of the theoretical analysis.« less

Pattern search in multi-structure data: a framework for the next-generation evidence-based medicine

NASA Astrophysics Data System (ADS)

Sukumar, Sreenivas R.; Ainsworth, Keela C.

2014-03-01

With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. Addressing this need, we pose and answer the following questions: (i) How can we jointly analyze and explore measurement data in context with qualitative domain knowledge? (ii) How can we search and hypothesize patterns (not known apriori) from such multi-structure data? (iii) How can we build predictive models by integrating weakly-associated multi-relational multi-structure data? We propose a framework towards answering these questions. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.

Learning in engineered multi-agent systems

NASA Astrophysics Data System (ADS)

Menon, Anup

Consider the problem of maximizing the total power produced by a wind farm. Due to aerodynamic interactions between wind turbines, each turbine maximizing its individual power---as is the case in present-day wind farms---does not lead to optimal farm-level power capture. Further, there are no good models to capture the said aerodynamic interactions, rendering model based optimization techniques ineffective. Thus, model-free distributed algorithms are needed that help turbines adapt their power production on-line so as to maximize farm-level power capture. Motivated by such problems, the main focus of this dissertation is a distributed model-free optimization problem in the context of multi-agent systems. The set-up comprises of a fixed number of agents, each of which can pick an action and observe the value of its individual utility function. An individual's utility function may depend on the collective action taken by all agents. The exact functional form (or model) of the agent utility functions, however, are unknown; an agent can only measure the numeric value of its utility. The objective of the multi-agent system is to optimize the welfare function (i.e. sum of the individual utility functions). Such a collaborative task requires communications between agents and we allow for the possibility of such inter-agent communications. We also pay attention to the role played by the pattern of such information exchange on certain aspects of performance. We develop two algorithms to solve this problem. The first one, engineered Interactive Trial and Error Learning (eITEL) algorithm, is based on a line of work in the Learning in Games literature and applies when agent actions are drawn from finite sets. While in a model-free setting, we introduce a novel qualitative graph-theoretic framework to encode known directed interactions of the form "which agents' action affect which others' payoff" (interaction graph). We encode explicit inter-agent communications in a directed graph (communication graph) and, under certain conditions, prove convergence of agent joint action (under eITEL) to the welfare optimizing set. The main condition requires that the union of interaction and communication graphs be strongly connected; thus the algorithm combines an implicit form of communication (via interactions through utility functions) with explicit inter-agent communications to achieve the given collaborative goal. This work has kinship with certain evolutionary computation techniques such as Simulated Annealing; the algorithm steps are carefully designed such that it describes an ergodic Markov chain with a stationary distribution that has support over states where agent joint actions optimize the welfare function. The main analysis tool is perturbed Markov chains and results of broader interest regarding these are derived as well. The other algorithm, Collaborative Extremum Seeking (CES), uses techniques from extremum seeking control to solve the problem when agent actions are drawn from the set of real numbers. In this case, under the assumption of existence of a local minimizer for the welfare function and a connected undirected communication graph between agents, a result regarding convergence of joint action to a small neighborhood of a local optimizer of the welfare function is proved. Since extremum seeking control uses a simultaneous gradient estimation-descent scheme, gradient information available in the continuous action space formulation is exploited by the CES algorithm to yield improved convergence speeds. The effectiveness of this algorithm for the wind farm power maximization problem is evaluated via simulations. Lastly, we turn to a different question regarding role of the information exchange pattern on performance of distributed control systems by means of a case study for the vehicle platooning problem. In the vehicle platoon control problem, the objective is to design distributed control laws for individual vehicles in a platoon (or a road-train) that regulate inter-vehicle distances at a specified safe value while the entire platoon follows a leader-vehicle. While most of the literature on the problem deals with some inadequacy in control performance when the information exchange is of the nearest neighbor-type, we consider an arbitrary graph serving as information exchange pattern and derive a relationship between how a certain indicator of control performance is related to the information pattern. Such analysis helps in understanding qualitative features of the `right' information pattern for this problem.

A new class of finite-time nonlinear consensus protocols for multi-agent systems

NASA Astrophysics Data System (ADS)

Zuo, Zongyu; Tie, Lin

2014-02-01

This paper is devoted to investigating the finite-time consensus problem for a multi-agent system in networks with undirected topology. A new class of global continuous time-invariant consensus protocols is constructed for each single-integrator agent dynamics with the aid of Lyapunov functions. In particular, it is shown that the settling time of the proposed new class of finite-time consensus protocols is upper bounded for arbitrary initial conditions. This makes it possible for network consensus problems that the convergence time is designed and estimated offline for a given undirected information flow and a group volume of agents. Finally, a numerical simulation example is presented as a proof of concept.

Real-time path planning in dynamic virtual environments using multiagent navigation graphs.

PubMed

Sud, Avneesh; Andersen, Erik; Curtis, Sean; Lin, Ming C; Manocha, Dinesh

2008-01-01

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for local dynamics computation of each agent by extending a social force model [Helbing05]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multi-agent planning in pursuit-evasion, terrain exploration and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.

Multi-agent autonomous system

NASA Technical Reports Server (NTRS)

Fink, Wolfgang (Inventor); Dohm, James (Inventor); Tarbell, Mark A. (Inventor)

2010-01-01

A multi-agent autonomous system for exploration of hazardous or inaccessible locations. The multi-agent autonomous system includes simple surface-based agents or craft controlled by an airborne tracking and command system. The airborne tracking and command system includes an instrument suite used to image an operational area and any craft deployed within the operational area. The image data is used to identify the craft, targets for exploration, and obstacles in the operational area. The tracking and command system determines paths for the surface-based craft using the identified targets and obstacles and commands the craft using simple movement commands to move through the operational area to the targets while avoiding the obstacles. Each craft includes its own instrument suite to collect information about the operational area that is transmitted back to the tracking and command system. The tracking and command system may be further coupled to a satellite system to provide additional image information about the operational area and provide operational and location commands to the tracking and command system.

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements.

PubMed

Xiong, Chunbao; Lu, Huali; Zhu, Jinsong

2017-02-23

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements

PubMed Central

Xiong, Chunbao; Lu, Huali; Zhu, Jinsong

2017-01-01

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation. PMID:28241472

Exchanging large data object in multi-agent systems

NASA Astrophysics Data System (ADS)

Al-Yaseen, Wathiq Laftah; Othman, Zulaiha Ali; Nazri, Mohd Zakree Ahmad

2016-08-01

One of the Business Intelligent solutions that is currently in use is the Multi-Agent System (MAS). Communication is one of the most important elements in MAS, especially for exchanging large low level data between distributed agents (physically). The Agent Communication Language in JADE has been offered as a secure method for sending data, whereby the data is defined as an object. However, the object cannot be used to send data to another agent in a different location. Therefore, the aim of this paper was to propose a method for the exchange of large low level data as an object by creating a proxy agent known as a Delivery Agent, which temporarily imitates the Receiver Agent. The results showed that the proposed method is able to send large-sized data. The experiments were conducted using 16 datasets ranging from 100,000 to 7 million instances. However, for the proposed method, the RAM and the CPU machine had to be slightly increased for the Receiver Agent, but the latency time was not significantly different compared to the use of the Java Socket method (non-agent and less secure). With such results, it was concluded that the proposed method can be used to securely send large data between agents.

Market-Based Coordination and Auditing Mechanisms for Self-Interested Multi-Robot Systems

ERIC Educational Resources Information Center

Ham, MyungJoo

2009-01-01

We propose market-based coordinated task allocation mechanisms, which allocate complex tasks that require synchronized and collaborated services of multiple robot agents to robot agents, and an auditing mechanism, which ensures proper behaviors of robot agents by verifying inter-agent activities, for self-interested, fully-distributed, and…

77 FR 56845 - Multi-Agency Informational Meeting Concerning Compliance With the Federal Select Agent Program...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-14

... Informational Meeting Concerning Compliance With the Federal Select Agent Program; Public Webcast AGENCY... with the Federal Select Agent Program. The purpose of this notice is to notify all interested parties... changes to the select agent regulations; occupational health, information and physical security; personnel...

78 FR 56234 - Multi-Agency Informational Meeting Concerning Compliance with the Select Agent Regulations...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-12

... Informational Meeting Concerning Compliance with the Select Agent Regulations; Public Webcast AGENCY: Centers... purpose of the webcast is to provide guidance related to the select agent regulations established under... Justice Information Services. Changes to Section 11(Security) of the select agent regulations including...

Clustering recommendations to compute agent reputation

NASA Astrophysics Data System (ADS)

Bedi, Punam; Kaur, Harmeet

2005-03-01

Traditional centralized approaches to security are difficult to apply to multi-agent systems which are used nowadays in e-commerce applications. Developing a notion of trust that is based on the reputation of an agent can provide a softer notion of security that is sufficient for many multi-agent applications. Our paper proposes a mechanism for computing reputation of the trustee agent for use by the trustier agent. The trustier agent computes the reputation based on its own experience as well as the experience the peer agents have with the trustee agents. The trustier agents intentionally interact with the peer agents to get their experience information in the form of recommendations. We have also considered the case of unintentional encounters between the referee agents and the trustee agent, which can be directly between them or indirectly through a set of interacting agents. The clustering is done to filter off the noise in the recommendations in the form of outliers. The trustier agent clusters the recommendations received from referee agents on the basis of the distances between recommendations using the hierarchical agglomerative method. The dendogram hence obtained is cut at the required similarity level which restricts the maximum distance between any two recommendations within a cluster. The cluster with maximum number of elements denotes the views of the majority of recommenders. The center of this cluster represents the reputation of the trustee agent which can be computed using c-means algorithm.

Implementation of a multi-threaded framework for large-scale scientific applications

DOE PAGES

Sexton-Kennedy, E.; Gartung, Patrick; Jones, C. D.; ...

2015-05-22

The CMS experiment has recently completed the development of a multi-threaded capable application framework. In this paper, we will discuss the design, implementation and application of this framework to production applications in CMS. For the 2015 LHC run, this functionality is particularly critical for both our online and offline production applications, which depend on faster turn-around times and a reduced memory footprint relative to before. These applications are complex codes, each including a large number of physics-driven algorithms. While the framework is capable of running a mix of thread-safe and 'legacy' modules, algorithms running in our production applications need tomore » be thread-safe for optimal use of this multi-threaded framework at a large scale. Towards this end, we discuss the types of changes, which were necessary for our algorithms to achieve good performance of our multithreaded applications in a full-scale application. Lastly performance numbers for what has been achieved for the 2015 run are presented.« less

Robust Architectures for Complex Multi-Agent Heterogeneous Systems

DTIC Science & Technology

2014-07-23

establish the tradeoff between the control performance and the QoS of the communications network . We also derived the performance bound on the difference...accomplished within this time period leveraged the prior accomplishments in the area of networked multi-agent systems. The past work (prior to 2011...distributed control of uncertain networked systems [3]. Additionally, a preliminary collision avoidance algorithm has been developed for a team of

Towards Symbolic Model Checking for Multi-Agent Systems via OBDDs

NASA Technical Reports Server (NTRS)

Raimondi, Franco; Lomunscio, Alessio

2004-01-01

We present an algorithm for model checking temporal-epistemic properties of multi-agent systems, expressed in the formalism of interpreted systems. We first introduce a technique for the translation of interpreted systems into boolean formulae, and then present a model-checking algorithm based on this translation. The algorithm is based on OBDD's, as they offer a compact and efficient representation for boolean formulae.

A multi-agent intelligent environment for medical knowledge.

PubMed

Vicari, Rosa M; Flores, Cecilia D; Silvestre, André M; Seixas, Louise J; Ladeira, Marcelo; Coelho, Helder

2003-03-01

AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian network for a problem the system will present. The construction of a network involves qualitative and quantitative aspects. The qualitative part concerns the network topology, that is, causal relations among the domain variables. After it is ready, the quantitative part is specified. It is composed of the distribution of conditional probability of the variables represented. A negotiation process (managed by an intelligent MediatorAgent) will treat the differences of topology and probability distribution between the model the learner built and the one built-in in the system. That negotiation process occurs between the agents that represent the expert knowledge domain (DomainAgent) and the agent that represents the learner knowledge (LearnerAgent).

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.

Energy Logic (EL): a novel fusion engine of multi-modality multi-agent data/information fusion for intelligent surveillance systems

NASA Astrophysics Data System (ADS)

Rababaah, Haroun; Shirkhodaie, Amir

2009-04-01

The rapidly advancing hardware technology, smart sensors and sensor networks are advancing environment sensing. One major potential of this technology is Large-Scale Surveillance Systems (LS3) especially for, homeland security, battlefield intelligence, facility guarding and other civilian applications. The efficient and effective deployment of LS3 requires addressing number of aspects impacting the scalability of such systems. The scalability factors are related to: computation and memory utilization efficiency, communication bandwidth utilization, network topology (e.g., centralized, ad-hoc, hierarchical or hybrid), network communication protocol and data routing schemes; and local and global data/information fusion scheme for situational awareness. Although, many models have been proposed to address one aspect or another of these issues but, few have addressed the need for a multi-modality multi-agent data/information fusion that has characteristics satisfying the requirements of current and future intelligent sensors and sensor networks. In this paper, we have presented a novel scalable fusion engine for multi-modality multi-agent information fusion for LS3. The new fusion engine is based on a concept we call: Energy Logic. Experimental results of this work as compared to a Fuzzy logic model strongly supported the validity of the new model and inspired future directions for different levels of fusion and different applications.

Designing Multi-Channel Web Frameworks for Cultural Tourism Applications: The MUSE Case Study.

ERIC Educational Resources Information Center

Garzotto, Franca; Salmon, Tullio; Pigozzi, Massimiliano

A framework for the design of multi-channel (MC) applications in the cultural tourism domain is presented. Several heterogeneous interface devices are supported including location-sensitive mobile units, on-site stationary devices, and personalized CDs that extend the on-site experience beyond the visit time thanks to personal memories gathered…

Learning Natural Selection in 4th Grade with Multi-Agent-Based Computational Models

NASA Astrophysics Data System (ADS)

Dickes, Amanda Catherine; Sengupta, Pratim

2013-06-01

In this paper, we investigate how elementary school students develop multi-level explanations of population dynamics in a simple predator-prey ecosystem, through scaffolded interactions with a multi-agent-based computational model (MABM). The term "agent" in an MABM indicates individual computational objects or actors (e.g., cars), and these agents obey simple rules assigned or manipulated by the user (e.g., speeding up, slowing down, etc.). It is the interactions between these agents, based on the rules assigned by the user, that give rise to emergent, aggregate-level behavior (e.g., formation and movement of the traffic jam). Natural selection is such an emergent phenomenon, which has been shown to be challenging for novices (K16 students) to understand. Whereas prior research on learning evolutionary phenomena with MABMs has typically focused on high school students and beyond, we investigate how elementary students (4th graders) develop multi-level explanations of some introductory aspects of natural selection—species differentiation and population change—through scaffolded interactions with an MABM that simulates predator-prey dynamics in a simple birds-butterflies ecosystem. We conducted a semi-clinical interview based study with ten participants, in which we focused on the following: a) identifying the nature of learners' initial interpretations of salient events or elements of the represented phenomena, b) identifying the roles these interpretations play in the development of their multi-level explanations, and c) how attending to different levels of the relevant phenomena can make explicit different mechanisms to the learners. In addition, our analysis also shows that although there were differences between high- and low-performing students (in terms of being able to explain population-level behaviors) in the pre-test, these differences disappeared in the post-test.

Distributed containment control of heterogeneous fractional-order multi-agent systems with communication delays

NASA Astrophysics Data System (ADS)

Yang, Hongyong; Han, Fujun; Zhao, Mei; Zhang, Shuning; Yue, Jun

2017-08-01

Because many networked systems can only be characterized with fractional-order dynamics in complex environments, fractional-order calculus has been studied deeply recently. When diverse individual features are shown in different agents of networked systems, heterogeneous fractional-order dynamics will be used to describe the complex systems. Based on the distinguishing properties of agents, heterogeneous fractional-order multi-agent systems (FOMAS) are presented. With the supposition of multiple leader agents in FOMAS, distributed containment control of FOMAS is studied in directed weighted topologies. By applying Laplace transformation and frequency domain theory of the fractional-order operator, an upper bound of delays is obtained to ensure containment consensus of delayed heterogenous FOMAS. Consensus results of delayed FOMAS in this paper can be extended to systems with integer-order models. Finally, numerical examples are used to verify our results.

Distributed event-triggered consensus strategy for multi-agent systems under limited resources

NASA Astrophysics Data System (ADS)

Noorbakhsh, S. Mohammad; Ghaisari, Jafar

2016-01-01

The paper proposes a distributed structure to address an event-triggered consensus problem for multi-agent systems which aims at concurrent reduction in inter-agent communication, control input actuation and energy consumption. Following the proposed approach, asymptotic convergence of all agents to consensus requires that each agent broadcasts its sampled-state to the neighbours and updates its control input only at its own triggering instants, unlike the existing related works. Obviously, it decreases the network bandwidth usage, sensor energy consumption, computation resources usage and actuator wears. As a result, it facilitates the implementation of the proposed consensus protocol in the real-world applications with limited resources. The stability of the closed-loop system under an event-based protocol is proved analytically. Some numerical results are presented which confirm the analytical discussion on the effectiveness of the proposed design.

Observer-based output consensus of a class of heterogeneous multi-agent systems with unmatched disturbances

NASA Astrophysics Data System (ADS)

Zhang, Jiancheng; Zhu, Fanglai

2018-03-01

In this paper, the output consensus of a class of linear heterogeneous multi-agent systems with unmatched disturbances is considered. Firstly, based on the relative output information among neighboring agents, we propose an asymptotic sliding-mode based consensus control scheme, under which, the output consensus error can converge to zero by removing the disturbances from output channels. Secondly, in order to reach the consensus goal, we design a novel high-order unknown input observer for each agent. It can estimate not only each agent's states and disturbances, but also the disturbances' high-order derivatives, which are required in the control scheme aforementioned above. The observer-based consensus control laws and the convergence analysis of the consensus error dynamics are given. Finally, a simulation example is provided to verify the validity of our methods.

Multi-threaded Event Processing with DANA

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

David Lawrence; Elliott Wolin

2007-05-14

The C++ data analysis framework DANA has been written to support the next generation of Nuclear Physics experiments at Jefferson Lab commensurate with the anticipated 12GeV upgrade. The DANA framework was designed to allow multi-threaded event processing with a minimal impact on developers of reconstruction software. This document describes how DANA implements multi-threaded event processing and compares it to simply running multiple instances of a program. Also presented are relative reconstruction rates for Pentium4, Xeon, and Opteron based machines.

Multi-Agent Design and Implementation for an Online Peer Help System

ERIC Educational Resources Information Center

Meng, Anbo

2014-01-01

With the rapid advance of e-learning, the online peer help is playing increasingly important role. This paper explores the application of MAS to an online peer help system (MAPS). In the design phase, the architecture of MAPS is proposed, which consists of a set of agents including the personal agent, the course agent, the diagnosis agent, the DF…

Teamwork Reasoning and Multi-Satellite Missions

NASA Technical Reports Server (NTRS)

Marsella, Stacy C.; Plaunt, Christian (Technical Monitor)

2002-01-01

NASA is rapidly moving towards the use of spatially distributed multiple satellites operating in near Earth orbit and Deep Space. Effective operation of such multi-satellite constellations raises many key research issues. In particular, the satellites will be required to cooperate with each other as a team that must achieve common objectives with a high degree of autonomy from ground based operations. The multi-agent research community has made considerable progress in investigating the challenges of realizing such teamwork. In this report, we discuss some of the teamwork issues that will be faced by multi-satellite operations. The basis of the discussion is a particular proposed mission, the Magnetospheric MultiScale mission to explore Earth's magnetosphere. We describe this mission and then consider how multi-agent technologies might be applied in the design and operation of these missions. We consider the potential benefits of these technologies as well as the research challenges that will be raised in applying them to NASA multi-satellite missions. We conclude with some recommendations for future work.

An Automated End-To Multi-Agent Qos Based Architecture for Selection of Geospatial Web Services

NASA Astrophysics Data System (ADS)

Shah, M.; Verma, Y.; Nandakumar, R.

2012-07-01

Over the past decade, Service-Oriented Architecture (SOA) and Web services have gained wide popularity and acceptance from researchers and industries all over the world. SOA makes it easy to build business applications with common services, and it provides like: reduced integration expense, better asset reuse, higher business agility, and reduction of business risk. Building of framework for acquiring useful geospatial information for potential users is a crucial problem faced by the GIS domain. Geospatial Web services solve this problem. With the help of web service technology, geospatial web services can provide useful geospatial information to potential users in a better way than traditional geographic information system (GIS). A geospatial Web service is a modular application designed to enable the discovery, access, and chaining of geospatial information and services across the web that are often both computation and data-intensive that involve diverse sources of data and complex processing functions. With the proliferation of web services published over the internet, multiple web services may provide similar functionality, but with different non-functional properties. Thus, Quality of Service (QoS) offers a metric to differentiate the services and their service providers. In a quality-driven selection of web services, it is important to consider non-functional properties of the web service so as to satisfy the constraints or requirements of the end users. The main intent of this paper is to build an automated end-to-end multi-agent based solution to provide the best-fit web service to service requester based on QoS.

On deception detection in multi-agent systems and deception intent

NASA Astrophysics Data System (ADS)

Santos, Eugene, Jr.; Li, Deqing; Yuan, Xiuqing

2008-04-01

Deception detection plays an important role in the military decision-making process, but detecting deception is a challenging task. The deception planning process involves a number of human factors. It is intent-driven where intentions are usually hidden or not easily observable. As a result, in order to detect deception, any adversary model must have the capability to capture the adversary's intent. This paper discusses deception detection in multi-agent systems and in adversary modeling. We examined psychological and cognitive science research on deception and implemented various theories of deception within our approach. First, in multi-agent expert systems, one detection method uses correlations between agents to predict reasonable opinions/responses of other agents (Santos & Johnson, 2004). We further explore this idea and present studies that show the impact of different factors on detection success rate. Second, from adversary modeling, our detection method focuses on inferring adversary intent. By combining deception "branches" with intent inference models, we can estimate an adversary's deceptive activities and at the same time enhance intent inference. Two major kinds of deceptions are developed in this approach in different fashions. Simulative deception attempts to find inconsistency in observables, while dissimulative deception emphasizes the inference of enemy intentions.

A Conceptual Framework for Determining Training Needs of Extension Agents Applied to Dairy Science. The Findings from Extension Studies.

ERIC Educational Resources Information Center

Verma, Satish

A summary of an Extension Education dissertation on a study to develop a framework of curriculum and learning theory features, to determine needs of Extension agents, and to show its application to dairy science is presented. Tyler's rationale for deriving educational objectives (curriculum theory) and Bloom's taxonomy of cognitive behavior…

Possibility of designing catalysts beyond the traditional volcano curve: a theoretical framework for multi-phase surfaces.

PubMed

Wang, Ziyun; Wang, Hai-Feng; Hu, P

2015-10-01

The current theory of catalyst activity in heterogeneous catalysis is mainly obtained from the study of catalysts with mono-phases, while most catalysts in real systems consist of multi-phases, the understanding of which is far short of chemists' expectation. Density functional theory (DFT) and micro-kinetics simulations are used to investigate the activities of six mono-phase and nine bi-phase catalysts, using CO hydrogenation that is arguably the most typical reaction in heterogeneous catalysis. Excellent activities that are beyond the activity peak of traditional mono-phase volcano curves are found on some bi-phase surfaces. By analyzing these results, a new framework to understand the unexpected activities of bi-phase surfaces is proposed. Based on the framework, several principles for the design of multi-phase catalysts are suggested. The theoretical framework extends the traditional catalysis theory to understand more complex systems.

Combining engineered cell-sensors with multi-agent systems to realize smart environment

NASA Astrophysics Data System (ADS)

Chen, Mei

2013-03-01

The connection of everything in a sensory and an intelligent way is a pursuit in smart environment. This paper introduces the engineered cell-sensors into the multi-agent systems to realize the smart environment. The seamless interface with the natural environment and strong information-processing ability of cell with the achievements of synthetic biology make the construction of engineered cell-sensors possible. However, the engineered cell-sensors are only simple-functional and unreliable computational entities. Therefore how to combine engineered cell-sensors with digital device is a key problem in order to realize the smart environment. We give the abstract structure and interaction modes of the engineered cell-sensors in order to introduce engineered cell-sensors into multi-agent systems. We believe that the introduction of engineered cell-sensors will push forward the development of the smart environment.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

NASA Astrophysics Data System (ADS)

Yang, Hong-Yong; Lu, Lan; Cao, Ke-Cai; Zhang, Si-Ying

2010-04-01

In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration.

A practical approach for active camera coordination based on a fusion-driven multi-agent system

NASA Astrophysics Data System (ADS)

Bustamante, Alvaro Luis; Molina, José M.; Patricio, Miguel A.

2014-04-01

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.

Evaluation of stormwater harvesting sites using multi criteria decision methodology

NASA Astrophysics Data System (ADS)

Inamdar, P. M.; Sharma, A. K.; Cook, Stephen; Perera, B. J. C.

2018-07-01

Selection of suitable urban stormwater harvesting sites and associated project planning are often complex due to spatial, temporal, economic, environmental and social factors, and related various other variables. This paper is aimed at developing a comprehensive methodology framework for evaluating of stormwater harvesting sites in urban areas using Multi Criteria Decision Analysis (MCDA). At the first phase, framework selects potential stormwater harvesting (SWH) sites using spatial characteristics in a GIS environment. In second phase, MCDA methodology is used for evaluating and ranking of SWH sites in multi-objective and multi-stakeholder environment. The paper briefly describes first phase of framework and focuses chiefly on the second phase of framework. The application of the methodology is also demonstrated over a case study comprising of the local government area, City of Melbourne (CoM), Australia for the benefit of wider water professionals engaged in this area. Nine performance measures (PMs) were identified to characterise the objectives and system performance related to the eight alternative SWH sites for the demonstration of the application of developed methodology. To reflect the stakeholder interests in the current study, four stakeholder participant groups were identified, namely, water authorities (WA), academics (AC), consultants (CS), and councils (CL). The decision analysis methodology broadly consisted of deriving PROMETHEE II rankings of eight alternative SWH sites in the CoM case study, under two distinct group decision making scenarios. The major innovation of this work is the development and application of comprehensive methodology framework that assists in the selection of potential sites for SWH, and facilitates the ranking in multi-objective and multi-stakeholder environment. It is expected that the proposed methodology will assist the water professionals and managers with better knowledge that will reduce the subjectivity in the selection and evaluation of SWH sites.

A Multi-Level Model of Information Seeking in the Clinical Domain

PubMed Central

Hung, Peter W.; Johnson, Stephen B.; Kaufman, David R.; Mendonça, Eneida A.

2008-01-01

Objective: Clinicians often have difficulty translating information needs into effective search strategies to find appropriate answers. Information retrieval systems employing an intelligent search agent that generates adaptive search strategies based on human search expertise could be helpful in meeting clinician information needs. A prerequisite for creating such systems is an information seeking model that facilitates the representation of human search expertise. The purpose of developing such a model is to provide guidance to information seeking system development and to shape an empirical research program. Design: The information seeking process was modeled as a complex problem-solving activity. After considering how similarly complex activities had been modeled in other domains, we determined that modeling context-initiated information seeking across multiple problem spaces allows the abstraction of search knowledge into functionally consistent layers. The knowledge layers were identified in the information science literature and validated through our observations of searches performed by health science librarians. Results: A hierarchical multi-level model of context-initiated information seeking is proposed. Each level represents (1) a problem space that is traversed during the online search process, and (2) a distinct layer of knowledge that is required to execute a successful search. Grand strategy determines what information resources will be searched, for what purpose, and in what order. The strategy level represents an overall approach for searching a single resource. Tactics are individual moves made to further a strategy. Operations are mappings of abstract intentions to information resource-specific concrete input. Assessment is the basis of interaction within the strategic hierarchy, influencing the direction of the search. Conclusion: The described multi-level model provides a framework for future research and the foundation for development of an automated information retrieval system that uses an intelligent search agent to bridge clinician information needs and human search expertise. PMID:18006383

PADF RF localization experiments with multi-agent caged-MAV platforms

NASA Astrophysics Data System (ADS)

Barber, Christopher; Gates, Miguel; Selmic, Rastko; Al-Issa, Huthaifa; Ordonez, Raul; Mitra, Atindra

2011-06-01

This paper provides a summary of preliminary RF direction finding results generated within an AFOSR funded testbed facility recently developed at Louisiana Tech University. This facility, denoted as the Louisiana Tech University Micro- Aerial Vehicle/Wireless Sensor Network (MAVSeN) Laboratory, has recently acquired a number of state-of-the-art MAV platforms that enable us to analyze, design, and test some of our recent results in the area of multiplatform position-adaptive direction finding (PADF) [1] [2] for localization of RF emitters in challenging embedded multipath environments. Discussions within the segmented sections of this paper include a description of the MAVSeN Laboratory and the preliminary results from the implementation of mobile platforms with the PADF algorithm. This novel approach to multi-platform RF direction finding is based on the investigation of iterative path-loss based (i.e. path loss exponent) metrics estimates that are measured across multiple platforms in order to develop a control law that robotically/intelligently positionally adapt (i.e. self-adjust) the location of each distributed/cooperative platform. The body of this paper provides a summary of our recent results on PADF and includes a discussion on state-of-the-art Sensor Mote Technologies as applied towards the development of sensor-integrated caged-MAV platform for PADF applications. Also, a discussion of recent experimental results that incorporate sample approaches to real-time singleplatform data pruning is included as part of a discussion on potential approaches to refining a basic PADF technique in order to integrate and perform distributed self-sensitivity and self-consistency analysis as part of a PADF technique with distributed robotic/intelligent features. These techniques are extracted in analytical form from a parallel study denoted as "PADF RF Localization Criteria for Multi-Model Scattering Environments". The focus here is on developing and reporting specific approaches to self-sensitivity and self-consistency within this experimental PADF framework via the exploitation of specific single-agent caged-MAV trajectories that are unique to this experiment set.

Efficient Evaluation Functions for Multi-Rover Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian; Tumer, Kagan

2004-01-01

Evolutionary computation can be a powerful tool in cresting a control policy for a single agent receiving local continuous input. This paper extends single-agent evolutionary computation to multi-agent systems, where a collection of agents strives to maximize a global fitness evaluation function that rates the performance of the entire system. This problem is solved in a distributed manner, where each agent evolves its own population of neural networks that are used as the control policies for the agent. Each agent evolves its population using its own agent-specific fitness evaluation function. We propose to create these agent-specific evaluation functions using the theory of collectives to avoid the coordination problem where each agent evolves a population that maximizes its own fitness function, yet the system has a whole achieves low values of the global fitness function. Instead we will ensure that each fitness evaluation function is both "aligned" with the global evaluation function and is "learnable," i.e., the agents can readily see how their behavior affects their evaluation function. We then show how these agent-specific evaluation functions outperform global evaluation methods by up to 600% in a domain where a set of rovers attempt to maximize the amount of information observed while navigating through a simulated environment.

Proceedings 3rd NASA/IEEE Workshop on Formal Approaches to Agent-Based Systems (FAABS-III)

NASA Technical Reports Server (NTRS)

Hinchey, Michael (Editor); Rash, James (Editor); Truszkowski, Walt (Editor); Rouff, Christopher (Editor)

2004-01-01

These preceedings contain 18 papers and 4 poster presentation, covering topics such as: multi-agent systems, agent-based control, formalism, norms, as well as physical and biological models of agent-based systems. Some applications presented in the proceedings include systems analysis, software engineering, computer networks and robot control.

76 FR 62312 - Multi-Agency Informational Meeting Concerning Compliance With the Federal Select Agent Program...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-07

... Select Agent Program; Public Meeting AGENCIES: Animal and Plant Health Inspection Service, USDA. ACTION... will be held to provide specific regulatory guidance related to the Federal Select Agent Program... Select Agent Program, APHIS, 4700 River Road, Unit 2, Riverdale, MD 20737; (301) 734-5960. CDC: Dr...

Synchronization of multi-agent systems with metric-topological interactions.

PubMed

Wang, Lin; Chen, Guanrong

2016-09-01

A hybrid multi-agent systems model integrating the advantages of both metric interaction and topological interaction rules, called the metric-topological model, is developed. This model describes planar motions of mobile agents, where each agent can interact with all the agents within a circle of a constant radius, and can furthermore interact with some distant agents to reach a pre-assigned number of neighbors, if needed. Some sufficient conditions imposed only on system parameters and agent initial states are presented, which ensure achieving synchronization of the whole group of agents. It reveals the intrinsic relationships among the interaction range, the speed, the initial heading, and the density of the group. Moreover, robustness against variations of interaction range, density, and speed are investigated by comparing the motion patterns and performances of the hybrid metric-topological interaction model with the conventional metric-only and topological-only interaction models. Practically in all cases, the hybrid metric-topological interaction model has the best performance in the sense of achieving highest frequency of synchronization, fastest convergent rate, and smallest heading difference.

Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

PubMed

Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve

2017-12-01

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

A comparative analysis of dynamic grids vs. virtual grids using the A3pviGrid framework.

PubMed

Shankaranarayanan, Avinas; Amaldas, Christine

2010-11-01

With the proliferation of Quad/Multi-core micro-processors in mainstream platforms such as desktops and workstations; a large number of unused CPU cycles can be utilized for running virtual machines (VMs) as dynamic nodes in distributed environments. Grid services and its service oriented business broker now termed cloud computing could deploy image based virtualization platforms enabling agent based resource management and dynamic fault management. In this paper we present an efficient way of utilizing heterogeneous virtual machines on idle desktops as an environment for consumption of high performance grid services. Spurious and exponential increases in the size of the datasets are constant concerns in medical and pharmaceutical industries due to the constant discovery and publication of large sequence databases. Traditional algorithms are not modeled at handing large data sizes under sudden and dynamic changes in the execution environment as previously discussed. This research was undertaken to compare our previous results with running the same test dataset with that of a virtual Grid platform using virtual machines (Virtualization). The implemented architecture, A3pviGrid utilizes game theoretic optimization and agent based team formation (Coalition) algorithms to improve upon scalability with respect to team formation. Due to the dynamic nature of distributed systems (as discussed in our previous work) all interactions were made local within a team transparently. This paper is a proof of concept of an experimental mini-Grid test-bed compared to running the platform on local virtual machines on a local test cluster. This was done to give every agent its own execution platform enabling anonymity and better control of the dynamic environmental parameters. We also analyze performance and scalability of Blast in a multiple virtual node setup and present our findings. This paper is an extension of our previous research on improving the BLAST application framework using dynamic Grids on virtualization platforms such as the virtual box.

Adaptive consensus of scale-free multi-agent system by randomly selecting links

NASA Astrophysics Data System (ADS)

Mou, Jinping; Ge, Huafeng

2016-06-01

This paper investigates an adaptive consensus problem for distributed scale-free multi-agent systems (SFMASs) by randomly selecting links, where the degree of each node follows a power-law distribution. The randomly selecting links are based on the assumption that every agent decides to select links among its neighbours according to the received data with a certain probability. Accordingly, a novel consensus protocol with the range of the received data is developed, and each node updates its state according to the protocol. By the iterative method and Cauchy inequality, the theoretical analysis shows that all errors among agents converge to zero, and in the meanwhile, several criteria of consensus are obtained. One numerical example shows the reliability of the proposed methods.

A multi agent model for the limit order book dynamics

NASA Astrophysics Data System (ADS)

Bartolozzi, M.

2010-11-01

In the present work we introduce a novel multi-agent model with the aim to reproduce the dynamics of a double auction market at microscopic time scale through a faithful simulation of the matching mechanics in the limit order book. The agents follow a noise decision making process where their actions are related to a stochastic variable, the market sentiment, which we define as a mixture of public and private information. The model, despite making just few basic assumptions over the trading strategies of the agents, is able to reproduce several empirical features of the high-frequency dynamics of the market microstructure not only related to the price movements but also to the deposition of the orders in the book.

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

Metal-organic frameworks for the removal of toxic industrial chemicals and chemical warfare agents.

PubMed

Bobbitt, N Scott; Mendonca, Matthew L; Howarth, Ashlee J; Islamoglu, Timur; Hupp, Joseph T; Farha, Omar K; Snurr, Randall Q

2017-06-06

Owing to the vast diversity of linkers, nodes, and topologies, metal-organic frameworks can be tailored for specific tasks, such as chemical separations or catalysis. Accordingly, these materials have attracted significant interest for capture and/or detoxification of toxic industrial chemicals and chemical warfare agents. In this paper, we review recent experimental and computational work pertaining to the capture of several industrially-relevant toxic chemicals, including NH 3 , SO 2 , NO 2 , H 2 S, and some volatile organic compounds, with particular emphasis on the challenging issue of designing materials that selectively adsorb these chemicals in the presence of water. We also examine recent research on the capture and catalytic degradation of chemical warfare agents such as sarin and sulfur mustard using metal-organic frameworks.

Strategy Space Exploration of a Multi-Agent Model for the Labor Market

NASA Astrophysics Data System (ADS)

de Grande, Pablo; Eguia, Manuel

We present a multi-agent system where typical labor market mechanisms emerge. Based on a few simple rules, our model allows for different interpretative paradigms to be represented and for different scenarios to be tried out. We thoroughly explore the space of possible strategies both for those unemployed and for companies and analyze the trade-off between these strategies regarding global social and economical indicators.

Multi-agent robotic systems and applications for satellite missions

NASA Astrophysics Data System (ADS)

Nunes, Miguel A.

A revolution in the space sector is happening. It is expected that in the next decade there will be more satellites launched than in the previous sixty years of space exploration. Major challenges are associated with this growth of space assets such as the autonomy and management of large groups of satellites, in particular with small satellites. There are two main objectives for this work. First, a flexible and distributed software architecture is presented to expand the possibilities of spacecraft autonomy and in particular autonomous motion in attitude and position. The approach taken is based on the concept of distributed software agents, also referred to as multi-agent robotic system. Agents are defined as software programs that are social, reactive and proactive to autonomously maximize the chances of achieving the set goals. Part of the work is to demonstrate that a multi-agent robotic system is a feasible approach for different problems of autonomy such as satellite attitude determination and control and autonomous rendezvous and docking. The second main objective is to develop a method to optimize multi-satellite configurations in space, also known as satellite constellations. This automated method generates new optimal mega-constellations designs for Earth observations and fast revisit times on large ground areas. The optimal satellite constellation can be used by researchers as the baseline for new missions. The first contribution of this work is the development of a new multi-agent robotic system for distributing the attitude determination and control subsystem for HiakaSat. The multi-agent robotic system is implemented and tested on the satellite hardware-in-the-loop testbed that simulates a representative space environment. The results show that the newly proposed system for this particular case achieves an equivalent control performance when compared to the monolithic implementation. In terms on computational efficiency it is found that the multi-agent robotic system has a consistent lower CPU load of 0.29 +/- 0.03 compared to 0.35 +/- 0.04 for the monolithic implementation, a 17.1 % reduction. The second contribution of this work is the development of a multi-agent robotic system for the autonomous rendezvous and docking of multiple spacecraft. To compute the maneuvers guidance, navigation and control algorithms are implemented as part of the multi-agent robotic system. The navigation and control functions are implemented using existing algorithms, but one important contribution of this section is the introduction of a new six degrees of freedom guidance method which is part of the guidance, navigation and control architecture. This new method is an explicit solution to the guidance problem, and is particularly useful for real time guidance for attitude and position, as opposed to typical guidance methods which are based on numerical solutions, and therefore are computationally intensive. A simulation scenario is run for docking four CubeSats deployed radially from a launch vehicle. Considering fully actuated CubeSats, the simulations show docking maneuvers that are successfully completed within 25 minutes which is approximately 30% of a full orbital period in low earth orbit. The final section investigates the problem of optimization of satellite constellations for fast revisit time, and introduces a new method to generate different constellation configurations that are evaluated with a genetic algorithm. Two case studies are presented. The first is the optimization of a constellation for rapid coverage of the oceans of the globe in 24 hours or less. Results show that for an 80 km sensor swath width 50 satellites are required to cover the oceans with a 24 hour revisit time. The second constellation configuration study focuses on the optimization for the rapid coverage of the North Atlantic Tracks for air traffic monitoring in 3 hours or less. The results show that for a fixed swath width of 160 km and for a 3 hour revisit time 52 satellites are required.

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…

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.

Towards a first implementation of the WLIMES approach in living system studies advancing the diagnostics and therapy in augmented personalized medicine.

PubMed

Simeonov, Plamen L

2017-12-01

The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation, man-machine interface and creative design. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life. Copyright © 2017 Elsevier B.V. All rights reserved.

Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

NASA Astrophysics Data System (ADS)

Yin, Y.; Zhang, X.; Anderson, D. D.; Brown, T. D.; Hofwegen, C. Van; Sonka, M.

2009-02-01

We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and reproducible segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object segmentation problems.

Developing a Multi-Dimensional Evaluation Framework for Faculty Teaching and Service Performance

ERIC Educational Resources Information Center

Baker, Diane F.; Neely, Walter P.; Prenshaw, Penelope J.; Taylor, Patrick A.

2015-01-01

A task force was created in a small, AACSB-accredited business school to develop a more comprehensive set of standards for faculty performance. The task force relied heavily on faculty input to identify and describe key dimensions that capture effective teaching and service performance. The result is a multi-dimensional framework that will be used…

The Effects of Routing and Scoring within a Computer Adaptive Multi-Stage Framework

ERIC Educational Resources Information Center

Dallas, Andrew

2014-01-01

This dissertation examined the overall effects of routing and scoring within a computer adaptive multi-stage framework (ca-MST). Testing in a ca-MST environment has become extremely popular in the testing industry. Testing companies enjoy its efficiency benefits as compared to traditionally linear testing and its quality-control features over…

Two-phase framework for near-optimal multi-target Lambert rendezvous

NASA Astrophysics Data System (ADS)

Bang, Jun; Ahn, Jaemyung

2018-03-01

This paper proposes a two-phase framework to obtain a near-optimal solution of multi-target Lambert rendezvous problem. The objective of the problem is to determine the minimum-cost rendezvous sequence and trajectories to visit a given set of targets within a maximum mission duration. The first phase solves a series of single-target rendezvous problems for all departure-arrival object pairs to generate the elementary solutions, which provides candidate rendezvous trajectories. The second phase formulates a variant of traveling salesman problem (TSP) using the elementary solutions prepared in the first phase and determines the final rendezvous sequence and trajectories of the multi-target rendezvous problem. The validity of the proposed optimization framework is demonstrated through an asteroid exploration case study.

An agent based architecture for high-risk neonate management at neonatal intensive care unit.

PubMed

Malak, Jaleh Shoshtarian; Safdari, Reza; Zeraati, Hojjat; Nayeri, Fatemeh Sadat; Mohammadzadeh, Niloofar; Farajollah, Seide Sedighe Seied

2018-01-01

In recent years, the use of new tools and technologies has decreased the neonatal mortality rate. Despite the positive effect of using these technologies, the decisions are complex and uncertain in critical conditions when the neonate is preterm or has a low birth weight or malformations. There is a need to automate the high-risk neonate management process by creating real-time and more precise decision support tools. To create a collaborative and real-time environment to manage neonates with critical conditions at the NICU (Neonatal Intensive Care Unit) and to overcome high-risk neonate management weaknesses by applying a multi agent based analysis and design methodology as a new solution for NICU management. This study was a basic research for medical informatics method development that was carried out in 2017. The requirement analysis was done by reviewing articles on NICU Decision Support Systems. PubMed, Science Direct, and IEEE databases were searched. Only English articles published after 1990 were included; also, a needs assessment was done by reviewing the extracted features and current processes at the NICU environment where the research was conducted. We analyzed the requirements and identified the main system roles (agents) and interactions by a comparative study of existing NICU decision support systems. The Universal Multi Agent Platform (UMAP) was applied to implement a prototype of our multi agent based high-risk neonate management architecture. Local environment agents interacted inside a container and each container interacted with external resources, including other NICU systems and consultation centers. In the NICU container, the main identified agents were reception, monitoring, NICU registry, and outcome prediction, which interacted with human agents including nurses and physicians. Managing patients at the NICU units requires online data collection, real-time collaboration, and management of many components. Multi agent systems are applied as a well-known solution for management, coordination, modeling, and control of NICU processes. We are currently working on an outcome prediction module using artificial intelligence techniques for neonatal mortality risk prediction. The full implementation of the proposed architecture and evaluation is considered the future work.

A framework for interactive visual analysis of heterogeneous marine data in an integrated problem solving environment

NASA Astrophysics Data System (ADS)

Liu, Shuai; Chen, Ge; Yao, Shifeng; Tian, Fenglin; Liu, Wei

2017-07-01

This paper presents a novel integrated marine visualization framework which focuses on processing, analyzing the multi-dimension spatiotemporal marine data in one workflow. Effective marine data visualization is needed in terms of extracting useful patterns, recognizing changes, and understanding physical processes in oceanography researches. However, the multi-source, multi-format, multi-dimension characteristics of marine data pose a challenge for interactive and feasible (timely) marine data analysis and visualization in one workflow. And, global multi-resolution virtual terrain environment is also needed to give oceanographers and the public a real geographic background reference and to help them to identify the geographical variation of ocean phenomena. This paper introduces a data integration and processing method to efficiently visualize and analyze the heterogeneous marine data. Based on the data we processed, several GPU-based visualization methods are explored to interactively demonstrate marine data. GPU-tessellated global terrain rendering using ETOPO1 data is realized and the video memory usage is controlled to ensure high efficiency. A modified ray-casting algorithm for the uneven multi-section Argo volume data is also presented and the transfer function is designed to analyze the 3D structure of ocean phenomena. Based on the framework we designed, an integrated visualization system is realized. The effectiveness and efficiency of the framework is demonstrated. This system is expected to make a significant contribution to the demonstration and understanding of marine physical process in a virtual global environment.

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.

Implementation of a Web-Based Collaborative Process Planning System

NASA Astrophysics Data System (ADS)

Wang, Huifen; Liu, Tingting; Qiao, Li; Huang, Shuangxi

Under the networked manufacturing environment, all phases of product manufacturing involving design, process planning, machining and assembling may be accomplished collaboratively by different enterprises, even different manufacturing stages of the same part may be finished collaboratively by different enterprises. Based on the self-developed networked manufacturing platform eCWS(e-Cooperative Work System), a multi-agent-based system framework for collaborative process planning is proposed. In accordance with requirements of collaborative process planning, share resources provided by cooperative enterprises in the course of collaboration are classified into seven classes. Then a reconfigurable and extendable resource object model is built. Decision-making strategy is also studied in this paper. Finally a collaborative process planning system e-CAPP is developed and applied. It provides strong support for distributed designers to collaboratively plan and optimize product process though network.

Stochastic approximation methods-Powerful tools for simulation and optimization: A survey of some recent work on multi-agent systems and cyber-physical systems

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

Yin, George; Wang, Le Yi; Zhang, Hongwei

2014-12-10

Stochastic approximation methods have found extensive and diversified applications. Recent emergence of networked systems and cyber-physical systems has generated renewed interest in advancing stochastic approximation into a general framework to support algorithm development for information processing and decisions in such systems. This paper presents a survey on some recent developments in stochastic approximation methods and their applications. Using connected vehicles in platoon formation and coordination as a platform, we highlight some traditional and new methodologies of stochastic approximation algorithms and explain how they can be used to capture essential features in networked systems. Distinct features of networked systems with randomlymore » switching topologies, dynamically evolving parameters, and unknown delays are presented, and control strategies are provided.« less

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.

Fuzzy decision-making framework for treatment selection based on the combined QUALIFLEX-TODIM method

NASA Astrophysics Data System (ADS)

Ji, Pu; Zhang, Hong-yu; Wang, Jian-qiang

2017-10-01

Treatment selection is a multi-criteria decision-making problem of significant concern in the medical field. In this study, a fuzzy decision-making framework is established for treatment selection. The framework mitigates information loss by introducing single-valued trapezoidal neutrosophic numbers to denote evaluation information. Treatment selection has multiple criteria that remarkably exceed the alternatives. In consideration of this characteristic, the framework utilises the idea of the qualitative flexible multiple criteria method. Furthermore, it considers the risk-averse behaviour of a decision maker by employing a concordance index based on TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method. A sensitivity analysis is performed to illustrate the robustness of the framework. Finally, a comparative analysis is conducted to compare the framework with several extant methods. Results indicate the advantages of the framework and its better performance compared with the extant methods.

Many-Objective Reservoir Policy Identification and Refinement to Reduce Institutional Myopia in Water Management

NASA Astrophysics Data System (ADS)

Giuliani, M.; Herman, J. D.; Castelletti, A.; Reed, P. M.

2013-12-01

Institutional inertia strongly limits our ability to adapt water reservoir operations to better manage growing water demands as well as their associated uncertainties in a changing climate. Although it has long been recognized that these systems are generally framed in heterogeneous socio-economic contexts involving a myriad of conflicting, non-commensurable operating objectives, our broader understanding of the multiobjective consequences of current operating rules as well as their vulnerability to hydroclimatic uncertainties is severely limited. This study proposes a decision analytic framework to overcome policy inertia and myopia in complex river basin management contexts. The framework combines reservoir policy identification and many-objective optimization under uncertainty to characterize current operations and discover key tradeoffs between alternative policies for balancing evolving demands and system uncertainties. The approach is demonstrated on the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to the system's competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. Initially our proposed framework uses available streamflow observations to implicitly identify the Conowingo Dam's current but unknown operating policy. This baseline policy is identified by fitting radial basis functions to existing system dynamics. Our assumption in the baseline policy is that the dam operator is represented as a rational agent seeking to maximize primary operational objectives (i.e., guaranteeing the public water supply and maximizing the hydropower revenue). The quality of the identified baseline policy is evaluated by its ability to replicate historical release dynamics. Once identified, the historical baseline policy then provides a means of representing the decision preferences guiding current operations. Our results show that the estimated policy closely captures the dynamics of current releases and flows for the Lower Susquehanna. After identifying the historical baseline policy, our proposed decision analytic framework then combines evolutionary many-objective optimization with visual analytics to discover improved operating policies. Our Lower Susquehanna results confirm that the system's current history-based operations are negatively biased to overestimate the reliability of the reservoir's multi-sector services. Moreover, our proposed framework has successfully identified alternative reservoir policies that are more robust to hydroclimatic uncertainties while being capable of better addressing the tradeoffs across the Conowingo Dam's multi-sector services.

Digital case-based learning system in school.

PubMed

Gu, Peipei; Guo, Jiayang

2017-01-01

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework.

Digital case-based learning system in school

PubMed Central

Gu, Peipei

2017-01-01

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework. PMID:29107965

Suitability of Agent Technology for Military Command and Control in the Future Combat System Environment

DTIC Science & Technology

2003-06-01

and Multi-Agent Systems 1 no. 1 (1998): 7-38. [23] K. Sycara, A. Pannu , M. Williamson, and D. Zeng, “Distributed Intelligent Agents,” IEEE Expert 11...services that include support for mobility, security, management, persistence, and naming of agents. [i] K. Sycara, A. Pannu , M. Williamson, and D

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…

Many-Agent Controlled Teleportation of Multi-qubit Quantum Information via Quantum Entanglement Swapping

NASA Astrophysics Data System (ADS)

Zhang, Zhan-Jun; Liu, Yi-Min; Man, Zhong-Xiao

2005-11-01

We present a method to teleport multi-qubit quantum information in an easy way from a sender to a receiver via the control of many agents in a network. Only when all the agents collaborate with the quantum information receiver can the unknown states in the sender's qubits be fully reconstructed in the receiver's qubits. In our method, agents's control parameters are obtained via quantum entanglement swapping. As the realization of the many-agent controlled teleportation is concerned, compared to the recent method [C.P. Yang, et al., Phys. Rev. A 70 (2004) 022329], our present method considerably reduces the preparation difficulty of initial states and the identification difficulty of entangled states, moreover, it does not need local Hadamard operations and it is more feasible in technology. The project supported by National Natural Science Foundation of China under Grant No. 10304022

Discovery of multi-target receptor tyrosine kinase inhibitors as novel anti-angiogenesis agents

NASA Astrophysics Data System (ADS)

Wang, Jinfeng; Zhang, Lin; Pan, Xiaoyan; Dai, Bingling; Sun, Ying; Li, Chuansheng; Zhang, Jie

2017-03-01

Recently, we have identified a biphenyl-aryl urea incorporated with salicylaldoxime (BPS-7) as an anti-angiogenesis agent. Herein, we disclosed a series of novel anti-angiogenesis agents with BPS-7 as lead compound through combining diarylureas with N-pyridin-2-ylcyclopropane carboxamide. Several title compounds exhibited simultaneous inhibition effects against three pro-angiogenic RTKs (VEGFR-2, TIE-2 and EphB4). Some of them displayed potent anti-proliferative activity against human vascular endothelial cell (EA.hy926). In particular, two potent compounds (CDAU-1 and CDAU-2) could be considered as promising anti-angiogenesis agents with triplet inhibition profile. The biological evaluation and molecular docking results indicate that N-pyridin-2-ylcyclopropane carboxamide could serve as a hinge-binding group (HBG) for the discovery of multi-target anti-angiogenesis agents. CDAU-2 also exhibited promising anti-angiogenic potency in a tissue model for angiogenesis.

A Demand-Driven Approach for a Multi-Agent System in Supply Chain Management

NASA Astrophysics Data System (ADS)

Kovalchuk, Yevgeniya; Fasli, Maria

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit.

Discovery of multi-target receptor tyrosine kinase inhibitors as novel anti-angiogenesis agents

PubMed Central

Wang, Jinfeng; Zhang, Lin; Pan, Xiaoyan; Dai, Bingling; Sun, Ying; Li, Chuansheng; Zhang, Jie

2017-01-01

Recently, we have identified a biphenyl-aryl urea incorporated with salicylaldoxime (BPS-7) as an anti-angiogenesis agent. Herein, we disclosed a series of novel anti-angiogenesis agents with BPS-7 as lead compound through combining diarylureas with N-pyridin-2-ylcyclopropane carboxamide. Several title compounds exhibited simultaneous inhibition effects against three pro-angiogenic RTKs (VEGFR-2, TIE-2 and EphB4). Some of them displayed potent anti-proliferative activity against human vascular endothelial cell (EA.hy926). In particular, two potent compounds (CDAU-1 and CDAU-2) could be considered as promising anti-angiogenesis agents with triplet inhibition profile. The biological evaluation and molecular docking results indicate that N-pyridin-2-ylcyclopropane carboxamide could serve as a hinge-binding group (HBG) for the discovery of multi-target anti-angiogenesis agents. CDAU-2 also exhibited promising anti-angiogenic potency in a tissue model for angiogenesis. PMID:28332573

Bridging the gap between biologic, individual, and macroenvironmental factors in cancer: a multilevel approach.

PubMed

Lynch, Shannon M; Rebbeck, Timothy R

2013-04-01

To address the complex nature of cancer occurrence and outcomes, approaches have been developed to simultaneously assess the role of two or more etiologic agents within hierarchical levels including the: (i) macroenvironment level (e.g., health care policy, neighborhood, or family structure); (ii) individual level (e.g., behaviors, carcinogenic exposures, socioeconomic factors, and psychologic responses); and (iii) biologic level (e.g., cellular biomarkers and inherited susceptibility variants). Prior multilevel approaches tend to focus on social and environmental hypotheses, and are thus limited in their ability to integrate biologic factors into a multilevel framework. This limited integration may be related to the limited translation of research findings into the clinic. We propose a "Multi-level Biologic and Social Integrative Construct" (MBASIC) to integrate macroenvironment and individual factors with biology. The goal of this framework is to help researchers identify relationships among factors that may be involved in the multifactorial, complex nature of cancer etiology, to aid in appropriate study design, to guide the development of statistical or mechanistic models to study these relationships, and to position the results of these studies for improved intervention, translation, and implementation. MBASIC allows researchers from diverse fields to develop hypotheses of interest under a common conceptual framework, to guide transdisciplinary collaborations, and to optimize the value of multilevel studies for clinical and public health activities.

Efficient Agent-Based Models for Non-Genomic Evolution

NASA Technical Reports Server (NTRS)

Gupta, Nachi; Agogino, Adrian; Tumer, Kagan

2006-01-01

Modeling dynamical systems composed of aggregations of primitive proteins is critical to the field of astrobiological science involving early evolutionary structures and the origins of life. Unfortunately traditional non-multi-agent methods either require oversimplified models or are slow to converge to adequate solutions. This paper shows how to address these deficiencies by modeling the protein aggregations through a utility based multi-agent system. In this method each agent controls the properties of a set of proteins assigned to that agent. Some of these properties determine the dynamics of the system, such as the ability for some proteins to join or split other proteins, while additional properties determine the aggregation s fitness as a viable primitive cell. We show that over a wide range of starting conditions, there are mechanisins that allow protein aggregations to achieve high values of overall fitness. In addition through the use of agent-specific utilities that remain aligned with the overall global utility, we are able to reach these conclusions with 50 times fewer learning steps.

Construction of multi-agent mobile robots control system in the problem of persecution with using a modified reinforcement learning method based on neural networks

NASA Astrophysics Data System (ADS)

Patkin, M. L.; Rogachev, G. N.

2018-02-01

A method for constructing a multi-agent control system for mobile robots based on training with reinforcement using deep neural networks is considered. Synthesis of the management system is proposed to be carried out with reinforcement training and the modified Actor-Critic method, in which the Actor module is divided into Action Actor and Communication Actor in order to simultaneously manage mobile robots and communicate with partners. Communication is carried out by sending partners at each step a vector of real numbers that are added to the observation vector and affect the behaviour. Functions of Actors and Critic are approximated by deep neural networks. The Critics value function is trained by using the TD-error method and the Actor’s function by using DDPG. The Communication Actor’s neural network is trained through gradients received from partner agents. An environment in which a cooperative multi-agent interaction is present was developed, computer simulation of the application of this method in the control problem of two robots pursuing two goals was carried out.

Consensus for second-order multi-agent systems with position sampled data

NASA Astrophysics Data System (ADS)

Wang, Rusheng; Gao, Lixin; Chen, Wenhai; Dai, Dameng

2016-10-01

In this paper, the consensus problem with position sampled data for second-order multi-agent systems is investigated. The interaction topology among the agents is depicted by a directed graph. The full-order and reduced-order observers with position sampled data are proposed, by which two kinds of sampled data-based consensus protocols are constructed. With the provided sampled protocols, the consensus convergence analysis of a continuous-time multi-agent system is equivalently transformed into that of a discrete-time system. Then, by using matrix theory and a sampled control analysis method, some sufficient and necessary consensus conditions based on the coupling parameters, spectrum of the Laplacian matrix and sampling period are obtained. While the sampling period tends to zero, our established necessary and sufficient conditions are degenerated to the continuous-time protocol case, which are consistent with the existing result for the continuous-time case. Finally, the effectiveness of our established results is illustrated by a simple simulation example. Project supported by the Natural Science Foundation of Zhejiang Province, China (Grant No. LY13F030005) and the National Natural Science Foundation of China (Grant No. 61501331).

The Design of a Multi-Agent NDE Inspection Qualification System

NASA Astrophysics Data System (ADS)

McLean, N.; McKenna, J. P.; Gachagan, A.; McArthur, S.; Hayward, G.

2007-03-01

A novel Multi-Agent system (MAS) for NDE inspection qualification is being developed to facilitate a scalable environment allowing integration and automation of new and existing inspection qualification tools. This paper discusses the advantages of using a MAS approach to integrate the large number of disparate NDE software tools. The design and implementation of the system architecture is described, including the development of an ontology to describe the NDE domain.

Enhanced risk management by an emerging multi-agent architecture

NASA Astrophysics Data System (ADS)

Lin, Sin-Jin; Hsu, Ming-Fu

2014-07-01

Classification in imbalanced datasets has attracted much attention from researchers in the field of machine learning. Most existing techniques tend not to perform well on minority class instances when the dataset is highly skewed because they focus on minimising the forecasting error without considering the relative distribution of each class. This investigation proposes an emerging multi-agent architecture, grounded on cooperative learning, to solve the class-imbalanced classification problem. Additionally, this study deals further with the obscure nature of the multi-agent architecture and expresses comprehensive rules for auditors. The results from this study indicate that the presented model performs satisfactorily in risk management and is able to tackle a highly class-imbalanced dataset comparatively well. Furthermore, the knowledge visualised process, supported by real examples, can assist both internal and external auditors who must allocate limited detecting resources; they can take the rules as roadmaps to modify the auditing programme.

Multi-agent electricity market modeling with EMCAS.

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

North, M.; Macal, C.; Conzelmann, G.

2002-09-05

Electricity systems are a central component of modern economies. Many electricity markets are transitioning from centrally regulated systems to decentralized markets. Furthermore, several electricity markets that have recently undergone this transition have exhibited extremely unsatisfactory results, most notably in California. These high stakes transformations require the introduction of largely untested regulatory structures. Suitable tools that can be used to test these regulatory structures before they are applied to real systems are required. Multi-agent models can provide such tools. To better understand the requirements such as tool, a live electricity market simulation was created. This experience helped to shape the developmentmore » of the multi-agent Electricity Market Complex Adaptive Systems (EMCAS) model. To explore EMCAS' potential, several variations of the live simulation were created. These variations probed the possible effects of changing power plant outages and price setting rules on electricity market prices.« less

Pharmacophore based design of some multi-targeted compounds targeted against pathways of diabetic complications.

PubMed

Chadha, Navriti; Silakari, Om

2017-09-01

Diabetic complications is a complex metabolic disorder developed primarily due to prolonged hyperglycemia in the body. The complexity of the disease state as well as the unifying pathophysiology discussed in the literature reports exhibited that the use of multi-targeted agents with multiple complementary biological activities may offer promising therapy for the intervention of the disease over the single-target drugs. In the present study, novel thiazolidine-2,4-dione analogues were designed as multi-targeted agents implicated against the molecular pathways involved in diabetic complications using knowledge based as well as in-silico approaches such as pharmacophore mapping, molecular docking etc. The hit molecules were duly synthesized and biochemical estimation of these molecules against aldose reductase (ALR2), protein kinase Cβ (PKCβ) and poly (ADP-ribose) polymerase 1 (PARP-1) led to identification of compound 2 that showed good potency against PARP-1 and ALR2 enzymes. These positive results support the progress of a low cost multi-targeted agent with putative roles in diabetic complications. Copyright © 2017 Elsevier Inc. All rights reserved.

An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition.

PubMed

Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

2018-02-01

In this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of training samples (i.e., the source domain) is often considerably different from that of testing samples (i.e., the target domain), so the performance of the classifiers learnt on the source domain may drop significantly on the target domain. Moreover, the testing data are often unseen during the training procedure. Second, when the training data are associated with multi-view features, the recognition performance can be further improved by exploiting the relation among multiple types of features. To address the first issue, considering that it has been shown that fusing multiple SVM classifiers can enhance the domain generalization ability, we build our EMVDG framework upon exemplar SVMs (ESVMs), in which a set of ESVM classifiers are learnt with each one trained based on one positive training sample and all the negative training samples. When the source domain contains multiple latent domains, the learnt ESVM classifiers are expected to be grouped into multiple clusters. To address the second issue, we propose two approaches under the EMVDG framework based on the consensus principle and the complementary principle, respectively. Specifically, we propose an EMVDG_CO method by adding a co-regularizer to enforce the cluster structures of ESVM classifiers on different views to be consistent based on the consensus principle. Inspired by multiple kernel learning, we also propose another EMVDG_MK method by fusing the ESVM classifiers from different views based on the complementary principle. In addition, we further extend our EMVDG framework to exemplar-based multi-view domain adaptation (EMVDA) framework when the unlabeled target domain data are available during the training procedure. The effectiveness of our EMVDG and EMVDA frameworks for visual recognition is clearly demonstrated by comprehensive experiments on three benchmark data sets.

Building occupancy simulation and data assimilation using a graph-based agent-oriented model

NASA Astrophysics Data System (ADS)

Rai, Sanish; Hu, Xiaolin

2018-07-01

Building occupancy simulation and estimation simulates the dynamics of occupants and estimates their real-time spatial distribution in a building. It requires a simulation model and an algorithm for data assimilation that assimilates real-time sensor data into the simulation model. Existing building occupancy simulation models include agent-based models and graph-based models. The agent-based models suffer high computation cost for simulating large numbers of occupants, and graph-based models overlook the heterogeneity and detailed behaviors of individuals. Recognizing the limitations of existing models, this paper presents a new graph-based agent-oriented model which can efficiently simulate large numbers of occupants in various kinds of building structures. To support real-time occupancy dynamics estimation, a data assimilation framework based on Sequential Monte Carlo Methods is also developed and applied to the graph-based agent-oriented model to assimilate real-time sensor data. Experimental results show the effectiveness of the developed model and the data assimilation framework. The major contributions of this work are to provide an efficient model for building occupancy simulation that can accommodate large numbers of occupants and an effective data assimilation framework that can provide real-time estimations of building occupancy from sensor data.

Acute side effects of three commonly used gadolinium contrast agents in the paediatric population.

PubMed

Neeley, Chris; Moritz, Michael; Brown, Jeffrey J; Zhou, Yihua

2016-07-01

To determine the incidence of acute side effects of three commonly used gadolinium contrast agents in the paediatric population. A retrospective review of medical records was performed to determine the incidence of acute adverse side effects of i.v. gadolinium contrast agents [MultiHance(®) (Bracco Diagnostics Inc., Princeton, NJ), Magnevist(®) (Bayer Healthcare Pharmaceuticals, Wayne, NJ) or Gadavist(®) (Bayer HealthCare Pharmaceuticals)] in paediatric patients. 40 of the 2393 patients who received gadolinium contrast agents experienced acute side effects, representing an incidence of 1.7%. The majority of the acute side effects (in 30 patients) were nausea and vomiting. The incidence was significantly higher in non-sedated patients (2.37% vs 0.7%; p = 0.0018). Furthermore, without sedation, the incidence of both nausea and vomiting was significantly higher in children receiving MultiHance, with a 4.48% incidence of nausea when compared with Magnevist (0.33%, p < 0.0001) and Gadavist (0.28%, p < 0.0001) and a 2.36% incidence of vomiting compared with those for Magnevist (0.50%, p = 0.0054) and Gadavist (0.28%, p = 0.014), whereas no difference was observed between Magnevist and Gadavist within the power of the study. In addition, there was no apparent difference between any of the three contrast agents for the incidence of allergy or other acute side effects detected, given the sample size. The gadolinium contrast agents MultiHance, Magnevist and Gadavist have a low incidence of acute side effects in the paediatric population, a rate that is further reduced in moderately sedated patients. MultiHance demonstrated significantly increased incidence of gastrointestinal symptoms compared with Magnevist and Gadavist. The incidence of acute side effects of three commonly used gadolinium contrast agents was determined in the paediatric population, which can have clinical implications.

Multi-agent based control of large-scale complex systems employing distributed dynamic inference engine

NASA Astrophysics Data System (ADS)

Zhang, Daili

Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algorithm in MSDBNs makes state estimations robust to partial damage in the whole system. Combining the distributed control architecture design and the distributed inference engine design leads to a process of control system design for a general large-scale complex system. As applications of the proposed methodology, the control system design of a simplified ship chilled water system and a notional ship chilled water system have been demonstrated step by step. Simulation results not only show that the proposed methodology gives a clear guideline for control system design for general large-scale complex systems with dynamic and uncertain environment, but also indicate that the combination of MSDBNs and HyMABC can provide excellent performance for controlling general large-scale complex systems.

Output Consensus of Heterogeneous Linear Multi-Agent Systems by Distributed Event-Triggered/Self-Triggered Strategy.

PubMed

Hu, Wenfeng; Liu, Lu; Feng, Gang

2016-09-02

This paper addresses the output consensus problem of heterogeneous linear multi-agent systems. We first propose a novel distributed event-triggered control scheme. It is shown that, with the proposed control scheme, the output consensus problem can be solved if two matrix equations are satisfied. Then, we further propose a novel self-triggered control scheme, with which continuous monitoring is avoided. By introducing a fixed timer into both event- and self-triggered control schemes, Zeno behavior can be ruled out for each agent. The effectiveness of the event- and self-triggered control schemes is illustrated by an example.

How Multi-Levels of Individual and Team Learning Interact in a Public Healthcare Organisation: A Conceptual Framework

ERIC Educational Resources Information Center

Doyle, Louise; Kelliher, Felicity; Harrington, Denis

2016-01-01

The aim of this paper is to review the relevant literature on organisational learning and offer a preliminary conceptual framework as a basis to explore how the multi-levels of individual learning and team learning interact in a public healthcare organisation. The organisational learning literature highlights a need for further understanding of…

Multi-wall carbon nanotube@zeolite imidazolate framework composite from a nanoscale zinc oxide precursor

DOE PAGES

Yue, Yanfeng; Guo, Bingkun; Qiao, Zhenan; ...

2014-07-24

Nanocomposite of multi-walled carbon nanotube@zeolite imidazolate frameworks (MWNT@ZIF) was prepared through a nanotube-facilitated growth based on a nanosized ZnO precursor. The electrically conductive nanocomposite displays a capacity of 380 mAh/g at 0.1 °C in Li–sulfur battery, transforming electrically inactive ZIF into the active one for battery applications.

The ASCA Model and a Multi-Tiered System of Supports: A Framework to Support Students of Color with Problem Behavior

ERIC Educational Resources Information Center

Belser, Christopher T.; Shillingford, M. Ann; Joe, J. Richelle

2016-01-01

The American School Counselor Association (ASCA) National Model and a multi-tiered system of supports (MTSS) both provide frameworks for systematically solving problems in schools, including student behavior concerns. The authors outline a model that integrates overlapping elements of the National Model and MTSS as a support for marginalized…

Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

NASA Astrophysics Data System (ADS)

Chen, Jiaxi; Li, Junmin

2018-02-01

In this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T-S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

Multi-hierarchical movements in self-avoiding walks

NASA Astrophysics Data System (ADS)

Sakiyama, Tomoko; Gunji, Yukio-Pegio

2017-07-01

A self-avoiding walk (SAW) is a series of moves on a lattice that visit the same place only once. Several studies reported that repellent reactions of foragers to previously visited sites induced power-law tailed SAWs in animals. In this paper, we show that modelling the agent's multi-avoidance reactions to its trails enables it to show ballistic movements which result in heavy-tailed movements. There is no literature showing emergent ballistic movements in SAWs. While following SAWs, the agent in my model changed its reactions to marked patches (visited sites) by considering global trail patterns based on local trail patterns when the agent was surrounded by previously visited sites. As a result, we succeeded in producing ballistic walks by the agents which exhibited emergent power-law tailed movements.

A review of event processing frameworks used in HEP

DOE PAGES

Sexton-Kennedy, E.

2015-12-23

Today there are many different experimental event processing frameworks in use by running or about to be running experiments. This talk will discuss the different components of these frameworks. In the past there have been attempts at shared framework projects for example the collaborations on the BaBar framework (between BaBar, CDF, and CLEO), on the Gaudi framework (between LHCb and ATLAS), on AliROOT/FairROOT (between Alice and GSI/Fair), and in some ways on art (Fermilab based experiments) and CMS’ framework. However, for reasons that will be discussed, these collaborations did not result in common frameworks shared among the intended experiments. Thoughmore » importantly, two of the resulting projects have succeeded in providing frameworks that are shared among many customer experiments: Fermilab's art framework and GSI/Fair's FairROOT. Interestingly, several projects are considering remerging their frameworks after many years apart. I'll report on an investigation and analysis of these realities. In addition, with the advent of the need for multi-threaded frameworks and the scarce available manpower, it is important to collaborate in the future, however it is also important to understand why previous attempts at multi-experiment frameworks either worked or didn't work.« less

40 CFR 180.1149 - Inclusion bodies of the multi-nuclear polyhedrosis virus of Anagrapha falcifera; exemption from...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Inclusion bodies of the multi-nuclear... Inclusion bodies of the multi-nuclear polyhedrosis virus of Anagrapha falcifera; exemption from the requirement of a tolerance. The microbial pest control agent inclusion bodies of the multi-nuclear...

A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

NASA Astrophysics Data System (ADS)

Alderliesten, Tanja; Bosman, Peter A. N.; Sonke, Jan-Jakob; Bel, Arjan

2014-03-01

Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes.

Nondestructive Intervention to Multi-Agent Systems through an Intelligent Agent

PubMed Central

Han, Jing; Wang, Lin

2013-01-01

For a given multi-agent system where the local interaction rule of the existing agents can not be re-designed, one way to intervene the collective behavior of the system is to add one or a few special agents into the group which are still treated as normal agents by the existing ones. We study how to lead a Vicsek-like flocking model to reach synchronization by adding special agents. A popular method is to add some simple leaders (fixed-headings agents). However, we add one intelligent agent, called ‘shill’, which uses online feedback information of the group to decide the shill's moving direction at each step. A novel strategy for the shill to coordinate the group is proposed. It is strictly proved that a shill with this strategy and a limited speed can synchronize every agent in the group. The computer simulations show the effectiveness of this strategy in different scenarios, including different group sizes, shill speed, and with or without noise. Compared to the method of adding some fixed-heading leaders, our method can guarantee synchronization for any initial configuration in the deterministic scenario and improve the synchronization level significantly in low density groups, or model with noise. This suggests the advantage and power of feedback information in intervention of collective behavior. PMID:23658695

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.

Can Moral Hazard Be Resolved by Common-Knowledge in S4n-Knowledge?

NASA Astrophysics Data System (ADS)

Matsuhisa, Takashi

This article investigates the relationship between common-knowledge and agreement in multi-agent system, and to apply the agreement result by common-knowledge to the principal-agent model under non-partition information. We treat the two problems: (1) how we capture the fact that the agents agree on an event or they get consensus on it from epistemic point of view, and (2) how the agreement theorem will be able to make progress to settle a moral hazard problem in the principal-agents model under non-partition information. We shall propose a solution program for the moral hazard in the principal-agents model under non-partition information by common-knowledge. Let us start that the agents have the knowledge structure induced from a reflexive and transitive relation associated with the multi-modal logic S4n. Each agent obtains the membership value of an event under his/her private information, so he/she considers the event as fuzzy set. Specifically consider the situation that the agents commonly know all membership values of the other agents. In this circumstance we shall show the agreement theorem that consensus on the membership values among all agents can still be guaranteed. Furthermore, under certain assumptions we shall show that the moral hazard can be resolved in the principal-agent model when all the expected marginal costs are common-knowledge among the principal and agents.

Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

PubMed

Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

2015-10-30

Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.

Characterizing the Nash equilibria of three-player Bayesian quantum games

NASA Astrophysics Data System (ADS)

Solmeyer, Neal; Balu, Radhakrishnan

2017-05-01

Quantum games with incomplete information can be studied within a Bayesian framework. We analyze games quantized within the EWL framework [Eisert, Wilkens, and Lewenstein, Phys Rev. Lett. 83, 3077 (1999)]. We solve for the Nash equilibria of a variety of two-player quantum games and compare the results to the solutions of the corresponding classical games. We then analyze Bayesian games where there is uncertainty about the player types in two-player conflicting interest games. The solutions to the Bayesian games are found to have a phase diagram-like structure where different equilibria exist in different parameter regions, depending both on the amount of uncertainty and the degree of entanglement. We find that in games where a Pareto-optimal solution is not a Nash equilibrium, it is possible for the quantized game to have an advantage over the classical version. In addition, we analyze the behavior of the solutions as the strategy choices approach an unrestricted operation. We find that some games have a continuum of solutions, bounded by the solutions of a simpler restricted game. A deeper understanding of Bayesian quantum game theory could lead to novel quantum applications in a multi-agent setting.

Agent-based modeling of the spread of influenza-like illness in an emergency department: a simulation study.

PubMed

Laskowski, Marek; Demianyk, Bryan C P; Witt, Julia; Mukhi, Shamir N; Friesen, Marcia R; McLeod, Robert D

2011-11-01

The objective of this paper was to develop an agent-based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent-based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C++ using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that became infected in one day within the simulation was the dependent variable. The results suggest that within the given instance context, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.

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.

Benchmark Intelligent Agent Systems for Distributed Battle Tracking

DTIC Science & Technology

2008-06-20

services in the military and other domains, each entity in the benchmark system exposes a standard set of Web services. Jess ( Java Expert Shell...System) is a rule engine for the Java platform and is an interpreter for the Jess rule language. It is used here to implement policies that maintain...battle tracking system (DBTS), maintaining distributed situation awareness. The Java Agent DEvelopment (JADE) framework is a software framework

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

Agent Reward Shaping for Alleviating Traffic Congestion

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Agogino, Adrian

2006-01-01

Traffic congestion problems provide a unique environment to study how multi-agent systems promote desired system level behavior. What is particularly interesting in this class of problems is that no individual action is intrinsically "bad" for the system but that combinations of actions among agents lead to undesirable outcomes, As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of "good" actions. This problem is ubiquitous in various traffic problems, including selecting departure times for commuters, routes for airlines, and paths for data routers. In this paper we present a multi-agent approach to two traffic problems, where far each driver, an agent selects the most suitable action using reinforcement learning. The agent rewards are based on concepts from collectives and aim to provide the agents with rewards that are both easy to learn and that if learned, lead to good system level behavior. In the first problem, we study how agents learn the best departure times of drivers in a daily commuting environment and how following those departure times alleviates congestion. In the second problem, we study how agents learn to select desirable routes to improve traffic flow and minimize delays for. all drivers.. In both sets of experiments,. agents using collective-based rewards produced near optimal performance (93-96% of optimal) whereas agents using system rewards (63-68%) barely outperformed random action selection (62-64%) and agents using local rewards (48-72%) performed worse than random in some instances.

Simulating the conversion of rural settlements to town land based on multi-agent systems and cellular automata.

PubMed

Liu, Yaolin; Kong, Xuesong; Liu, Yanfang; Chen, Yiyun

2013-01-01

Rapid urbanization in China has triggered the conversion of land from rural to urban use, particularly the conversion of rural settlements to town land. This conversion is the result of the joint effects of the geographic environment and agents involving the government, investors, and farmers. To understand the dynamic interaction dominated by agents and to predict the future landscape of town expansion, a small town land-planning model is proposed based on the integration of multi-agent systems (MAS) and cellular automata (CA). The MAS-CA model links the decision-making behaviors of agents with the neighbor effect of CA. The interaction rules are projected by analyzing the preference conflicts among agents. To better illustrate the effects of the geographic environment, neighborhood, and agent behavior, a comparative analysis between the CA and MAS-CA models in three different towns is presented, revealing interesting patterns in terms of quantity, spatial characteristics, and the coordinating process. The simulation of rural settlements conversion to town land through modeling agent decision and human-environment interaction is very useful for understanding the mechanisms of rural-urban land-use change in developing countries. This process can assist town planners in formulating appropriate development plans.

Multi-agent integrated password management (MIPM) application secured with encryption

NASA Astrophysics Data System (ADS)

Awang, Norkhushaini; Zukri, Nurul Hidayah Ahmad; Rashid, Nor Aimuni Md; Zulkifli, Zuhri Arafah; Nazri, Nor Afifah Mohd

2017-10-01

Users use weak passwords and reuse them on different websites and applications. Password managers are a solution to store login information for websites and help users log in automatically. This project developed a system that acts as an agent managing passwords. Multi-Agent Integrated Password Management (MIPM) is an application using encryption that provides users with secure storage of their login account information such as their username, emails and passwords. This project was developed on an Android platform with an encryption agent using Java Agent Development Environment (JADE). The purpose of the embedded agents is to act as a third-party software to ease the encryption process, and in the future, the developed encryption agents can form part of the security system. This application can be used by the computer and mobile users. Currently, users log into many applications causing them to use unique passwords to prevent password leaking. The crypto agent handles the encryption process using an Advanced Encryption Standard (AES) 128-bit encryption algorithm. As a whole, MIPM is developed on the Android application to provide a secure platform to store passwords and has high potential to be commercialised for public use.

A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application.

PubMed

Mostafa, Salama A; Mustapha, Aida; Mohammed, Mazin Abed; Ahmad, Mohd Sharifuddin; Mahmoud, Moamin A

2018-04-01

Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls. Copyright © 2018 Elsevier B.V. All rights reserved.

A Complex Systems Approach to More Resilient Multi-Layered Security Systems

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

Brown, Nathanael J. K.; Jones, Katherine A.; Bandlow, Alisa

In July 2012, protestors cut through security fences and gained access to the Y-12 National Security Complex. This was believed to be a highly reliable, multi-layered security system. This report documents the results of a Laboratory Directed Research and Development (LDRD) project that created a consistent, robust mathematical framework using complex systems analysis algorithms and techniques to better understand the emergent behavior, vulnerabilities and resiliency of multi-layered security systems subject to budget constraints and competing security priorities. Because there are several dimensions to security system performance and a range of attacks that might occur, the framework is multi-objective for amore » performance frontier to be estimated. This research explicitly uses probability of intruder interruption given detection (P I) as the primary resilience metric. We demonstrate the utility of this framework with both notional as well as real-world examples of Physical Protection Systems (PPSs) and validate using a well-established force-on-force simulation tool, Umbra.« less

A novel framework for change detection in bi-temporal polarimetric SAR images

NASA Astrophysics Data System (ADS)

Pirrone, Davide; Bovolo, Francesca; Bruzzone, Lorenzo

2016-10-01

Last years have seen relevant increase of polarimetric Synthetic Aperture Radar (SAR) data availability, thanks to satellite sensors like Sentinel-1 or ALOS-2 PALSAR-2. The augmented information lying in the additional polarimetric channels represents a possibility for better discriminate different classes of changes in change detection (CD) applications. This work aims at proposing a framework for CD in multi-temporal multi-polarization SAR data. The framework includes both a tool for an effective visual representation of the change information and a method for extracting the multiple-change information. Both components are designed to effectively handle the multi-dimensionality of polarimetric data. In the novel representation, multi-temporal intensity SAR data are employed to compute a polarimetric log-ratio. The multitemporal information of the polarimetric log-ratio image is represented in a multi-dimensional features space, where changes are highlighted in terms of magnitude and direction. This representation is employed to design a novel unsupervised multi-class CD approach. This approach considers a sequential two-step analysis of the magnitude and the direction information for separating non-changed and changed samples. The proposed approach has been validated on a pair of Sentinel-1 data acquired before and after the flood in Tamil-Nadu in 2015. Preliminary results demonstrate that the representation tool is effective and that the use of polarimetric SAR data is promising in multi-class change detection applications.

Developing Multi-Agency Leadership in Education

ERIC Educational Resources Information Center

Close, Paul

2012-01-01

This article contributes to the growing debate around how we understand and develop multi-agency leadership in children and young people's services. Bringing together a range of inter-disciplinary research, it presents a framework for multi-agency leadership development, which, it argues, is well theorised, multi-level and versed in key field…

Evidencing Learning Outcomes: A Multi-Level, Multi-Dimensional Course Alignment Model

ERIC Educational Resources Information Center

Sridharan, Bhavani; Leitch, Shona; Watty, Kim

2015-01-01

This conceptual framework proposes a multi-level, multi-dimensional course alignment model to implement a contextualised constructive alignment of rubric design that authentically evidences and assesses learning outcomes. By embedding quality control mechanisms at each level for each dimension, this model facilitates the development of an aligned…

Toward a consistent modeling framework to assess multi-sectoral climate impacts.

PubMed

Monier, Erwan; Paltsev, Sergey; Sokolov, Andrei; Chen, Y-H Henry; Gao, Xiang; Ejaz, Qudsia; Couzo, Evan; Schlosser, C Adam; Dutkiewicz, Stephanie; Fant, Charles; Scott, Jeffery; Kicklighter, David; Morris, Jennifer; Jacoby, Henry; Prinn, Ronald; Haigh, Martin

2018-02-13

Efforts to estimate the physical and economic impacts of future climate change face substantial challenges. To enrich the currently popular approaches to impact analysis-which involve evaluation of a damage function or multi-model comparisons based on a limited number of standardized scenarios-we propose integrating a geospatially resolved physical representation of impacts into a coupled human-Earth system modeling framework. Large internationally coordinated exercises cannot easily respond to new policy targets and the implementation of standard scenarios across models, institutions and research communities can yield inconsistent estimates. Here, we argue for a shift toward the use of a self-consistent integrated modeling framework to assess climate impacts, and discuss ways the integrated assessment modeling community can move in this direction. We then demonstrate the capabilities of such a modeling framework by conducting a multi-sectoral assessment of climate impacts under a range of consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations.

Conversational Agents for Academically Productive Talk: A Comparison of Directed and Undirected Agent Interventions

ERIC Educational Resources Information Center

Tegos, Stergios; Demetriadis, Stavros; Papadopoulos, Pantelis M.; Weinberger, Armin

2016-01-01

Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order…

Hierarchical control and performance evaluation of multi-vehicle autonomous systems

NASA Astrophysics Data System (ADS)

Balakirsky, Stephen; Scrapper, Chris; Messina, Elena

2005-05-01

This paper will describe how the Mobility Open Architecture Tools and Simulation (MOAST) framework can facilitate performance evaluations of RCS compliant multi-vehicle autonomous systems. This framework provides an environment that allows for simulated and real architectural components to function seamlessly together. By providing repeatable environmental conditions, this framework allows for the development of individual components as well as component performance metrics. MOAST is composed of high-fidelity and low-fidelity simulation systems, a detailed model of real-world terrain, actual hardware components, a central knowledge repository, and architectural glue to tie all of the components together. This paper will describe the framework"s components in detail and provide an example that illustrates how the framework can be utilized to develop and evaluate a single architectural component through the use of repeatable trials and experimentation that includes both virtual and real components functioning together

Efficacy Comparison of Six Chemotherapeutic Combinations for Osteosarcoma and Ewing's Sarcoma Treatment: A Network Meta-Analysis.

PubMed

Zhang, Tao; Zhang, Song; Yang, Feifei; Wang, Lili; Zhu, Sigang; Qiu, Bing; Li, Shunhua; Deng, Zhongliang

2018-01-01

This study aimed to address the insufficiency of traditional meta-analysis and provide improved guidelines for the clinical practice of osteosarcoma treatment. The heterogeneity of the fixed-effect model was calculated, and when necessary, a random-effect model was adopted. Furthermore, the direct and indirect evidence was pooled together and exhibited in the forest plot and slash table. The surface under the cumulative ranking curve (SUCRA) value was also measured to rank each intervention. Finally, heat plot was introduced to demonstrate the contribution of each intervention and the inconsistency between direct and indirect comparisons. This network meta-analysis included 32 trials, involving a total of 5,626 subjects reported by 28 articles. All the treatments were classified into six chemotherapeutic combinations: dual agent with or without ifosfamide (IFO), multi-agent with or without IFO, and dual agent or multi-agent with IFO and etoposide. For the primary outcomes, both overall survival (OS) and event-free survival (EFS) rates were considered. The multi-agent integrated with IFO and etoposide showed an optimal performance for 5-year OS, 10-year OS, 3-year EFS, 5-year EFS, and 10-year EFS when compared with placebo. The SUCRA value of this treatment was also the highest of these six interventions. However, multi-drug with IFO alone had the highest SUCRA value of 0.652 and 0.516 when it came to relapse and lung-metastasis. It was efficient to some extent, but no significant difference was observed in both outcomes. Chemotherapy, applied as induction or adjuvant treatment with radiation therapy or surgery, is able to increase the survival rate of patients, especially by combining multi-drug with IFO and etoposide, which demonstrated the best performance in both OS and EFS. As for relapse and the lung-metastasis, multiple agents with IFO alone seemed to have the optimal efficiency, although no significant difference was observed here. J. Cell. Biochem. 119: 250-259, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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.

Minimal Representation and Decision Making for Networked Autonomous Agents

DTIC Science & Technology

2015-08-27

to a multi-vehicle version of the Travelling Salesman Problem (TSP). We further provided a direct formula for computing the number of robots...the sensor. As a first stab at this, the two-agent rendezvous problem is considered where one agent (the target) is equipped with no sensors and is...by the total distance traveled by all agents. For agents with limited sensing and communication capabilities, we give a formula that computes the

DEVELOP MULTI-STRESSOR, OPEN ARCHITECTURE MODELING FRAMEWORK FOR ECOLOGICAL EXPOSURE FROM SITE TO WATERSHED SCALE

EPA Science Inventory

A number of multimedia modeling frameworks are currently being developed. The Multimedia Integrated Modeling System (MIMS) is one of these frameworks. A framework should be seen as more of a multimedia modeling infrastructure than a single software system. This infrastructure do...

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

Reinforcement learning agents providing advice in complex video games

NASA Astrophysics Data System (ADS)

Taylor, Matthew E.; Carboni, Nicholas; Fachantidis, Anestis; Vlahavas, Ioannis; Torrey, Lisa

2014-01-01

This article introduces a teacher-student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have different effects on student learning, and that teachers can significantly affect student learning even when students use different learning methods and state representations.

Multi-Agent Flight Simulation with Robust Situation Generation

NASA Technical Reports Server (NTRS)

Johnson, Eric N.; Hansman, R. John, Jr.

1994-01-01

A robust situation generation architecture has been developed that generates multi-agent situations for human subjects. An implementation of this architecture was developed to support flight simulation tests of air transport cockpit systems. This system maneuvers pseudo-aircraft relative to the human subject's aircraft, generating specific situations for the subject to respond to. These pseudo-aircraft maneuver within reasonable performance constraints, interact in a realistic manner, and make pre-recorded voice radio communications. Use of this system minimizes the need for human experimenters to control the pseudo-agents and provides consistent interactions between the subject and the pseudo-agents. The achieved robustness of this system to typical variations in the subject's flight path was explored. It was found to successfully generate specific situations within the performance limitations of the subject-aircraft, pseudo-aircraft, and the script used.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

Detoxification of Chemical Warfare Agents Using a Zr6 -Based Metal-Organic Framework/Polymer Mixture.

PubMed

Moon, Su-Young; Proussaloglou, Emmanuel; Peterson, Gregory W; DeCoste, Jared B; Hall, Morgan G; Howarth, Ashlee J; Hupp, Joseph T; Farha, Omar K

2016-10-10

Owing to their high surface area, periodic distribution of metal sites, and water stability, zirconium-based metal-organic frameworks (Zr 6 -MOFs) have shown promising activity for the hydrolysis of nerve agents GD and VX, as well as the simulant, dimethyl 4-nitrophenylphosphate (DMNP), in buffered solutions. A hurdle to using MOFs for this application is the current need for a buffer solution. Here the destruction of the simulant DMNP, as well as the chemical warfare agents (GD and VX) through hydrolysis using a MOF catalyst mixed with a non-volatile, water-insoluble, heterogeneous buffer is reported. The hydrolysis of the simulant and nerve agents in the presence of the heterogeneous buffer was fast and effective. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network

PubMed Central

Brennan, Robert W.

2017-01-01

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network. PMID:28906452

Team Formation in Partially Observable Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Agogino, Adrian K.; Tumer, Kagan

2004-01-01

Sets of multi-agent teams often need to maximize a global utility rating the performance of the entire system where a team cannot fully observe other teams agents. Such limited observability hinders team-members trying to pursue their team utilities to take actions that also help maximize the global utility. In this article, we show how team utilities can be used in partially observable systems. Furthermore, we show how team sizes can be manipulated to provide the best compromise between having easy to learn team utilities and having them aligned with the global utility, The results show that optimally sized teams in a partially observable environments outperform one team in a fully observable environment, by up to 30%.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network.

PubMed

Taboun, Mohammed S; Brennan, Robert W

2017-09-14

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network.

Open multi-agent control architecture to support virtual-reality-based man-machine interfaces

NASA Astrophysics Data System (ADS)

Freund, Eckhard; Rossmann, Juergen; Brasch, Marcel

2001-10-01

Projective Virtual Reality is a new and promising approach to intuitively operable man machine interfaces for the commanding and supervision of complex automation systems. The user interface part of Projective Virtual Reality heavily builds on latest Virtual Reality techniques, a task deduction component and automatic action planning capabilities. In order to realize man machine interfaces for complex applications, not only the Virtual Reality part has to be considered but also the capabilities of the underlying robot and automation controller are of great importance. This paper presents a control architecture that has proved to be an ideal basis for the realization of complex robotic and automation systems that are controlled by Virtual Reality based man machine interfaces. The architecture does not just provide a well suited framework for the real-time control of a multi robot system but also supports Virtual Reality metaphors and augmentations which facilitate the user's job to command and supervise a complex system. The developed control architecture has already been used for a number of applications. Its capability to integrate sensor information from sensors of different levels of abstraction in real-time helps to make the realized automation system very responsive to real world changes. In this paper, the architecture will be described comprehensively, its main building blocks will be discussed and one realization that is built based on an open source real-time operating system will be presented. The software design and the features of the architecture which make it generally applicable to the distributed control of automation agents in real world applications will be explained. Furthermore its application to the commanding and control of experiments in the Columbus space laboratory, the European contribution to the International Space Station (ISS), is only one example which will be described.

An agent-based model of leukocyte transendothelial migration during atherogenesis.

PubMed

Bhui, Rita; Hayenga, Heather N

2017-05-01

A vast amount of work has been dedicated to the effects of hemodynamics and cytokines on leukocyte adhesion and trans-endothelial migration (TEM) and subsequent accumulation of leukocyte-derived foam cells in the artery wall. However, a comprehensive mechanobiological model to capture these spatiotemporal events and predict the growth and remodeling of an atherosclerotic artery is still lacking. Here, we present a multiscale model of leukocyte TEM and plaque evolution in the left anterior descending (LAD) coronary artery. The approach integrates cellular behaviors via agent-based modeling (ABM) and hemodynamic effects via computational fluid dynamics (CFD). In this computational framework, the ABM implements the diffusion kinetics of key biological proteins, namely Low Density Lipoprotein (LDL), Tissue Necrosis Factor alpha (TNF-α), Interlukin-10 (IL-10) and Interlukin-1 beta (IL-1β), to predict chemotactic driven leukocyte migration into and within the artery wall. The ABM also considers wall shear stress (WSS) dependent leukocyte TEM and compensatory arterial remodeling obeying Glagov's phenomenon. Interestingly, using fully developed steady blood flow does not result in a representative number of leukocyte TEM as compared to pulsatile flow, whereas passing WSS at peak systole of the pulsatile flow waveform does. Moreover, using the model, we have found leukocyte TEM increases monotonically with decreases in luminal volume. At critical plaque shapes the WSS changes rapidly resulting in sudden increases in leukocyte TEM suggesting lumen volumes that will give rise to rapid plaque growth rates if left untreated. Overall this multi-scale and multi-physics approach appropriately captures and integrates the spatiotemporal events occurring at the cellular level in order to predict leukocyte transmigration and plaque evolution.

An agent-based model of leukocyte transendothelial migration during atherogenesis

PubMed Central

Bhui, Rita; Hayenga, Heather N.

2017-01-01

A vast amount of work has been dedicated to the effects of hemodynamics and cytokines on leukocyte adhesion and trans-endothelial migration (TEM) and subsequent accumulation of leukocyte-derived foam cells in the artery wall. However, a comprehensive mechanobiological model to capture these spatiotemporal events and predict the growth and remodeling of an atherosclerotic artery is still lacking. Here, we present a multiscale model of leukocyte TEM and plaque evolution in the left anterior descending (LAD) coronary artery. The approach integrates cellular behaviors via agent-based modeling (ABM) and hemodynamic effects via computational fluid dynamics (CFD). In this computational framework, the ABM implements the diffusion kinetics of key biological proteins, namely Low Density Lipoprotein (LDL), Tissue Necrosis Factor alpha (TNF-α), Interlukin-10 (IL-10) and Interlukin-1 beta (IL-1β), to predict chemotactic driven leukocyte migration into and within the artery wall. The ABM also considers wall shear stress (WSS) dependent leukocyte TEM and compensatory arterial remodeling obeying Glagov’s phenomenon. Interestingly, using fully developed steady blood flow does not result in a representative number of leukocyte TEM as compared to pulsatile flow, whereas passing WSS at peak systole of the pulsatile flow waveform does. Moreover, using the model, we have found leukocyte TEM increases monotonically with decreases in luminal volume. At critical plaque shapes the WSS changes rapidly resulting in sudden increases in leukocyte TEM suggesting lumen volumes that will give rise to rapid plaque growth rates if left untreated. Overall this multi-scale and multi-physics approach appropriately captures and integrates the spatiotemporal events occurring at the cellular level in order to predict leukocyte transmigration and plaque evolution. PMID:28542193

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

Cross contrast multi-channel image registration using image synthesis for MR brain images.

PubMed

Chen, Min; Carass, Aaron; Jog, Amod; Lee, Junghoon; Roy, Snehashis; Prince, Jerry L

2017-02-01

Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information. Copyright © 2016 Elsevier B.V. All rights reserved.

The development of multi-level critical care competency statements for self-assessment by ICU nurses.

PubMed

Bourgault, Annette M; Smith, Sherry

2004-01-01

Multi-levelled critical care competency statements were developed based on the levels of novice to expert (Benner, 1984). These competency statements provide a framework for the development of knowledge and skills specific to critical care. The purpose of this tool is to guide personal development in critical care, facilitating the assessment of individual learning needs. Competency levels are attained through the completion of performance criteria. Multi-levelled competency statements define clear expectations for the new orientee, in addition to providing a framework for the advancement of the intermediate and experienced nurse.

CMS event processing multi-core efficiency status

NASA Astrophysics Data System (ADS)

Jones, C. D.; CMS Collaboration

2017-10-01

In 2015, CMS was the first LHC experiment to begin using a multi-threaded framework for doing event processing. This new framework utilizes Intel’s Thread Building Block library to manage concurrency via a task based processing model. During the 2015 LHC run period, CMS only ran reconstruction jobs using multiple threads because only those jobs were sufficiently thread efficient. Recent work now allows simulation and digitization to be thread efficient. In addition, during 2015 the multi-threaded framework could run events in parallel but could only use one thread per event. Work done in 2016 now allows multiple threads to be used while processing one event. In this presentation we will show how these recent changes have improved CMS’s overall threading and memory efficiency and we will discuss work to be done to further increase those efficiencies.

Formalizing Knowledge in Multi-Scale Agent-Based Simulations

PubMed Central

Somogyi, Endre; Sluka, James P.; Glazier, James A.

2017-01-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused. PMID:29338063

Formalizing Knowledge in Multi-Scale Agent-Based Simulations.

PubMed

Somogyi, Endre; Sluka, James P; Glazier, James A

2016-10-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused.