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Sample records for algorithms artificial intelligence

  1. Artificial Intelligence.

    ERIC Educational Resources Information Center

    Waltz, David L.

    1982-01-01

    Describes kinds of results achieved by computer programs in artificial intelligence. Topics discussed include heuristic searches, artificial intelligence/psychology, planning program, backward chaining, learning (focusing on Winograd's blocks to explore learning strategies), concept learning, constraint propagation, language understanding…

  2. Artificial Intelligence.

    ERIC Educational Resources Information Center

    Information Technology Quarterly, 1985

    1985-01-01

    This issue of "Information Technology Quarterly" is devoted to the theme of "Artificial Intelligence." It contains two major articles: (1) Artificial Intelligence and Law" (D. Peter O'Neill and George D. Wood); (2) "Artificial Intelligence: A Long and Winding Road" (John J. Simon, Jr.). In addition, it contains two sidebars: (1) "Calculating and…

  3. An artificial intelligent algorithm for tumor detection in screening mammogram.

    PubMed

    Zheng, L; Chan, A K

    2001-07-01

    Cancerous tumor mass is one of the major types of breast cancer. When cancerous masses are embedded in and camouflaged by varying densities of parenchymal tissue structures, they are very difficult to be visually detected on mammograms. This paper presents an algorithm that combines several artificial intelligent techniques with the discrete wavelet transform (DWT) for detection of masses in mammograms. The AI techniques include fractal dimension analysis, multiresolution markov random field, dogs-and-rabbits algorithm, and others. The fractal dimension analysis serves as a preprocessor to determine the approximate locations of the regions suspicious for cancer in the mammogram. The dogs-and-rabbits clustering algorithm is used to initiate the segmentation at the LL subband of a three-level DWT decomposition of the mammogram. A tree-type classification strategy is applied at the end to determine whether a given region is suspicious for cancer. We have verified the algorithm with 322 mammograms in the Mammographic Image Analysis Society Database. The verification results show that the proposed algorithm has a sensitivity of 97.3% and the number of false positive per image is 3.92.

  4. A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

    PubMed Central

    Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

    2015-01-01

    In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced. PMID:26690164

  5. A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.

    PubMed

    Meiring, Gys Albertus Marthinus; Myburgh, Hermanus Carel

    2015-12-04

    In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

  6. Artificial Intelligence.

    ERIC Educational Resources Information Center

    Thornburg, David D.

    1986-01-01

    Overview of the artificial intelligence (AI) field provides a definition; discusses past research and areas of future research; describes the design, functions, and capabilities of expert systems and the "Turing Test" for machine intelligence; and lists additional sources for information on artificial intelligence. Languages of AI are also briefly…

  7. Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms

    NASA Astrophysics Data System (ADS)

    Gholami, Raoof; Rasouli, Vamegh; Alimoradi, Andisheh

    2013-09-01

    Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures.

  8. Artificial intelligence

    SciTech Connect

    Firschein, O.

    1984-01-01

    This book presents papers on artificial intelligence. Topics considered include knowledge engineering, expert systems, applications of artificial intelligence to scientific reasoning, planning and problem solving, error recovery in robots through failure reason analysis, programming languages, natural language, speech recognition, map-guided interpretation of remotely-sensed imagery, and image understanding architectures.

  9. Artificial Intelligence.

    ERIC Educational Resources Information Center

    Wash, Darrel Patrick

    1989-01-01

    Making a machine seem intelligent is not easy. As a consequence, demand has been rising for computer professionals skilled in artificial intelligence and is likely to continue to go up. These workers develop expert systems and solve the mysteries of machine vision, natural language processing, and neural networks. (Editor)

  10. Artificial Intelligence

    SciTech Connect

    Shirai, Y.; Tsujii, Jun-ichi

    1985-01-01

    Based on the Japanese 5th Generation Computer Program, this volume provides coverage of the fundamental concepts and various techniques in the different applications of Artificial Intelligence. Also presented are the methods which can be used to put these concepts and techniques into practice. Explanations are presented of all the basic topics in the field, including the representation of problems; searching techniques; the control of problem solving; programming languages for Al, such as LISP, PLANNER, CONNIVER, and PROLOG; the representation and utilization of knowledge; and the approach to human intelligence.

  11. Artificial Intelligence.

    PubMed

    Lawrence, David R; Palacios-González, César; Harris, John

    2016-04-01

    It seems natural to think that the same prudential and ethical reasons for mutual respect and tolerance that one has vis-à-vis other human persons would hold toward newly encountered paradigmatic but nonhuman biological persons. One also tends to think that they would have similar reasons for treating we humans as creatures that count morally in our own right. This line of thought transcends biological boundaries-namely, with regard to artificially (super)intelligent persons-but is this a safe assumption? The issue concerns ultimate moral significance: the significance possessed by human persons, persons from other planets, and hypothetical nonorganic persons in the form of artificial intelligence (AI). This article investigates why our possible relations to AI persons could be more complicated than they first might appear, given that they might possess a radically different nature to us, to the point that civilized or peaceful coexistence in a determinate geographical space could be impossible to achieve.

  12. Artificial Intelligence.

    PubMed

    Lawrence, David R; Palacios-González, César; Harris, John

    2016-04-01

    It seems natural to think that the same prudential and ethical reasons for mutual respect and tolerance that one has vis-à-vis other human persons would hold toward newly encountered paradigmatic but nonhuman biological persons. One also tends to think that they would have similar reasons for treating we humans as creatures that count morally in our own right. This line of thought transcends biological boundaries-namely, with regard to artificially (super)intelligent persons-but is this a safe assumption? The issue concerns ultimate moral significance: the significance possessed by human persons, persons from other planets, and hypothetical nonorganic persons in the form of artificial intelligence (AI). This article investigates why our possible relations to AI persons could be more complicated than they first might appear, given that they might possess a radically different nature to us, to the point that civilized or peaceful coexistence in a determinate geographical space could be impossible to achieve. PMID:26957450

  13. Implementing embedded artificial intelligence rules within algorithmic programming languages

    NASA Technical Reports Server (NTRS)

    Feyock, Stefan

    1988-01-01

    Most integrations of artificial intelligence (AI) capabilities with non-AI (usually FORTRAN-based) application programs require the latter to execute separately to run as a subprogram or, at best, as a coroutine, of the AI system. In many cases, this organization is unacceptable; instead, the requirement is for an AI facility that runs in embedded mode; i.e., is called as subprogram by the application program. The design and implementation of a Prolog-based AI capability that can be invoked in embedded mode are described. The significance of this system is twofold: Provision of Prolog-based symbol-manipulation and deduction facilities makes a powerful symbolic reasoning mechanism available to applications programs written in non-AI languages. The power of the deductive and non-procedural descriptive capabilities of Prolog, which allow the user to describe the problem to be solved, rather than the solution, is to a large extent vitiated by the absence of the standard control structures provided by other languages. Embedding invocations of Prolog rule bases in programs written in non-AI languages makes it possible to put Prolog calls inside DO loops and similar control constructs. The resulting merger of non-AI and AI languages thus results in a symbiotic system in which the advantages of both programming systems are retained, and their deficiencies largely remedied.

  14. Trends in Artificial Intelligence.

    ERIC Educational Resources Information Center

    Hayes, Patrick

    1978-01-01

    Discusses the foundations of artificial intelligence as a science and the types of answers that may be given to the question, "What is intelligence?" The paradigms of artificial intelligence and general systems theory are compared. (Author/VT)

  15. Multi-modulus algorithm based on global artificial fish swarm intelligent optimization of DNA encoding sequences.

    PubMed

    Guo, Y C; Wang, H; Wu, H P; Zhang, M Q

    2015-12-21

    Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.

  16. Artificial intelligence in parallel

    SciTech Connect

    Waldrop, M.M.

    1984-08-10

    The current rage in the Artificial Intelligence (AI) community is parallelism: the idea is to build machines with many independent processors doing many things at once. The upshot is that about a dozen parallel machines are now under development for AI alone. As might be expected, the approaches are diverse yet there are a number of fundamental issues in common: granularity, topology, control, and algorithms.

  17. Assistance of Novel Artificial Intelligence in Optimization of Aluminum Matrix Nanocomposite by Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Mazahery, Ali; Shabani, Mohsen Ostad

    2012-12-01

    In this article, a genetic algorithm (GA) is used to predict the mechanical properties and to optimize the process conditions of Al nanocomposites. An artificial intelligence method is also implemented as an assisting tool for engineering tasks of GAs. The principle of the survival of the fittest is applied to produce successively superior approximations to a solution. A population of points at each iteration is generated. The population approaches an optimal solution. The next population by computations that involve random choices is selected. The optimal volume percentage of SiC, cooling rate, and temperature gradient are computed to be 2.84 pct, 283 K/s (10 °C/s), 1273 K/m (1000 °C/m), respectively.

  18. Scheduling algorithm for data relay satellite optical communication based on artificial intelligent optimization

    NASA Astrophysics Data System (ADS)

    Zhao, Wei-hu; Zhao, Jing; Zhao, Shang-hong; Li, Yong-jun; Wang, Xiang; Dong, Yi; Dong, Chen

    2013-08-01

    Optical satellite communication with the advantages of broadband, large capacity and low power consuming broke the bottleneck of the traditional microwave satellite communication. The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical satellite communication. Considering the resources, missions and restraints of Data Relay Satellite Optical Communication System, a model of optical communication resources scheduling is established and a scheduling algorithm based on artificial intelligent optimization is put forwarded. According to the multi-relay-satellite, multi-user-satellite, multi-optical-antenna and multi-mission with several priority weights, the resources are scheduled reasonable by the operation: "Ascertain Current Mission Scheduling Time" and "Refresh Latter Mission Time-Window". The priority weight is considered as the parameter of the fitness function and the scheduling project is optimized by the Genetic Algorithm. The simulation scenarios including 3 relay satellites with 6 optical antennas, 12 user satellites and 30 missions, the simulation result reveals that the algorithm obtain satisfactory results in both efficiency and performance and resources scheduling model and the optimization algorithm are suitable in multi-relay-satellite, multi-user-satellite, and multi-optical-antenna recourses scheduling problem.

  19. Intelligence: Real or artificial?

    PubMed Central

    Schlinger, Henry D.

    1992-01-01

    Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selectionist model instead. Such a strategic change should lead them to the science of behavior analysis. PMID:22477051

  20. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    ERIC Educational Resources Information Center

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  1. Artificial intelligence: Recent developments

    SciTech Connect

    Not Available

    1987-01-01

    This book presents the papers given at a conference on artificial intelligence. Topics considered at the conference included knowledge representation for expert systems, the use of robots in underwater vehicles for resource management, precision logic, an expert system for arc welding, data base management, a knowledge based approach to fault trees, and computer-aided manufacturing using simulation combined with artificial intelligence.

  2. Heidegger and artificial intelligence

    SciTech Connect

    Diaz, G.

    1987-01-01

    The discipline of Artificial Intelligence, in its quest for machine intelligence, showed great promise as long as its areas of application were limited to problems of a scientific and situation neutral nature. The attempts to move beyond these problems to a full simulation of man's intelligence has faltered and slowed it progress, largely because of the inability of Artificial Intelligence to deal with human characteristic, such as feelings, goals, and desires. This dissertation takes the position that an impasse has resulted because Artificial Intelligence has never been properly defined as a science: its objects and methods have never been identified. The following study undertakes to provide such a definition, i.e., the required ground for Artificial Intelligence. The procedure and methods employed in this study are based on Heidegger's philosophy and techniques of analysis as developed in Being and Time. Results of this study show that both the discipline of Artificial Intelligence and the concerns of Heidegger in Being and Time have the same object; fundamental ontology. The application of Heidegger's conclusions concerning fundamental ontology unites the various aspects of Artificial Intelligence and provides the articulation which shows the parts of this discipline and how they are related.

  3. Artificial intelligence in medicine.

    PubMed Central

    Ramesh, A. N.; Kambhampati, C.; Monson, J. R. T.; Drew, P. J.

    2004-01-01

    INTRODUCTION: Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. METHODS: Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. RESULTS: The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. DISCUSSION: Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting. PMID:15333167

  4. Introduction to artificial intelligence

    SciTech Connect

    Charniak, E.; McDermott, D.

    1985-01-01

    This book is an introduction on artificial intelligence. Topics include reasoning under uncertainty, robot plans, language understanding, and learning. The history of the field as well as intellectual ties to related disciplines are presented.

  5. Artificial intelligence in nanotechnology.

    PubMed

    Sacha, G M; Varona, P

    2013-11-15

    During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

  6. Artificial intelligence in nanotechnology

    NASA Astrophysics Data System (ADS)

    Sacha, G. M.; Varona, P.

    2013-11-01

    During the last decade there has been increasing use of artificial intelligence tools in nanotechnology research. In this paper we review some of these efforts in the context of interpreting scanning probe microscopy, the study of biological nanosystems, the classification of material properties at the nanoscale, theoretical approaches and simulations in nanoscience, and generally in the design of nanodevices. Current trends and future perspectives in the development of nanocomputing hardware that can boost artificial-intelligence-based applications are also discussed. Convergence between artificial intelligence and nanotechnology can shape the path for many technological developments in the field of information sciences that will rely on new computer architectures and data representations, hybrid technologies that use biological entities and nanotechnological devices, bioengineering, neuroscience and a large variety of related disciplines.

  7. Introducing artificial intelligence

    SciTech Connect

    Simons, G.L.

    1985-01-01

    This book is an introduction to the field of artificial intelligence. The volume sets Al in a broad context of historical attitudes, imaginative insights, and ideas about intelligence in general. The author offers a wide-ranging survey of Al concerns, including cognition, knowledge engineering, problem inference, speech understanding, and perception. He also discusses expert systems, LISP, smart robots, and other Al products, and provides a listing of all major Al systems.

  8. Documenting Liquefaction Failures Using Satellite Remote Sensing and Artificial Intelligence Algorithms

    NASA Astrophysics Data System (ADS)

    Oommen, T.; Baise, L. G.; Gens, R.; Prakash, A.; Gupta, R. P.

    2009-12-01

    bands by neighborhood correlation image analysis using an artificial intelligence algorithm called support vector machine to remotely identify and document liquefaction failures across a region; and assess the reliability and accuracy of the thermal remote sensing approach in documenting regional liquefaction failures. Finally, we present the applicability of the satellite data analyzed and appropriateness of a multisensor and multispectral approach for documenting liquefaction related failures.

  9. Web Intelligence and Artificial Intelligence in Education

    ERIC Educational Resources Information Center

    Devedzic, Vladan

    2004-01-01

    This paper surveys important aspects of Web Intelligence (WI) in the context of Artificial Intelligence in Education (AIED) research. WI explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web-related products, systems, services, and…

  10. Artificial Intelligence and Information Retrieval.

    ERIC Educational Resources Information Center

    Teodorescu, Ioana

    1987-01-01

    Compares artificial intelligence and information retrieval paradigms for natural language understanding, reviews progress to date, and outlines the applicability of artificial intelligence to question answering systems. A list of principal artificial intelligence software for database front end systems is appended. (CLB)

  11. Database in Artificial Intelligence.

    ERIC Educational Resources Information Center

    Wilkinson, Julia

    1986-01-01

    Describes a specialist bibliographic database of literature in the field of artificial intelligence created by the Turing Institute (Glasgow, Scotland) using the BRS/Search information retrieval software. The subscription method for end-users--i.e., annual fee entitles user to unlimited access to database, document provision, and printed awareness…

  12. Artificial intelligence within AFSC

    NASA Technical Reports Server (NTRS)

    Gersh, Mark A.

    1990-01-01

    Information on artificial intelligence research in the Air Force Systems Command is given in viewgraph form. Specific research that is being conducted at the Rome Air Development Center, the Space Technology Center, the Human Resources Laboratory, the Armstrong Aerospace Medical Research Laboratory, the Armamant Laboratory, and the Wright Research and Development Center is noted.

  13. Artificial Intelligence and CALL.

    ERIC Educational Resources Information Center

    Underwood, John H.

    The potential application of artificial intelligence (AI) to computer-assisted language learning (CALL) is explored. Two areas of AI that hold particular interest to those who deal with language meaning--knowledge representation and expert systems, and natural-language processing--are described and examples of each are presented. AI contribution…

  14. Artificial intelligence. Second edition

    SciTech Connect

    Winston, P.H.

    1984-01-01

    This book introduces the basic concepts of the field of artificial intelligence. It contains material covering the latest advances in control, representation, language, vision, and problem solving. Problem solving in design and analysis systems is addressed. Mitcell's version-space learning procedure, Morevec's reduced-images stereo procedure, and the Strips problem solver are covered.

  15. Is chess the drosophila of artificial intelligence? A social history of an algorithm.

    PubMed

    Ensmenger, Nathan

    2012-02-01

    Since the mid 1960s, researchers in computer science have famously referred to chess as the 'drosophila' of artificial intelligence (AI). What they seem to mean by this is that chess, like the common fruit fly, is an accessible, familiar, and relatively simple experimental technology that nonetheless can be used productively to produce valid knowledge about other, more complex systems. But for historians of science and technology, the analogy between chess and drosophila assumes a larger significance. As Robert Kohler has ably described, the decision to adopt drosophila as the organism of choice for genetics research had far-reaching implications for the development of 20th century biology. In a similar manner, the decision to focus on chess as the measure of both human and computer intelligence had important and unintended consequences for AL research. This paper explores the emergence of chess as an experimental technology, its significance in the developing research practices of the AI community, and the unique ways in which the decision to focus on chess shaped the program of AI research in the decade of the 1970s. More broadly, it attempts to open up the virtual black box of computer software--and of computer games in particular--to the scrutiny of historical and sociological analysis. PMID:22530382

  16. Is chess the drosophila of artificial intelligence? A social history of an algorithm.

    PubMed

    Ensmenger, Nathan

    2012-02-01

    Since the mid 1960s, researchers in computer science have famously referred to chess as the 'drosophila' of artificial intelligence (AI). What they seem to mean by this is that chess, like the common fruit fly, is an accessible, familiar, and relatively simple experimental technology that nonetheless can be used productively to produce valid knowledge about other, more complex systems. But for historians of science and technology, the analogy between chess and drosophila assumes a larger significance. As Robert Kohler has ably described, the decision to adopt drosophila as the organism of choice for genetics research had far-reaching implications for the development of 20th century biology. In a similar manner, the decision to focus on chess as the measure of both human and computer intelligence had important and unintended consequences for AL research. This paper explores the emergence of chess as an experimental technology, its significance in the developing research practices of the AI community, and the unique ways in which the decision to focus on chess shaped the program of AI research in the decade of the 1970s. More broadly, it attempts to open up the virtual black box of computer software--and of computer games in particular--to the scrutiny of historical and sociological analysis.

  17. Artificial intelligence at CSM

    SciTech Connect

    Braun, G.; Jones, J.E.

    1985-08-01

    The recent developments in artificial intelligence have been cited as being the most significant technological advancement in computer science in the twentieth century. Machines that can mimic human reasoning will have a great impact upon our civilization. The way we think, learn, and work will be changed in a profound way. It is for these reasons that the Colorado School of Mines, in order to maintain its reputation of quality engineering education, has entered the AI field. CSM presently is evaluating artificial intelligence for applications in the mineral industries; decision support systems, process control, machine vision, data acquisition and analysis, etc. Future plans are to move AI out of the research laboratories and into the curriculum. An understanding of the concepts and unlimited power of the application of AI will enhance the engineering methods of Mines graduates. 6 references.

  18. Instructional Applications of Artificial Intelligence.

    ERIC Educational Resources Information Center

    Halff, Henry M.

    1986-01-01

    Surveys artificial intelligence and the development of computer-based tutors and speculates on the future of artificial intelligence in education. Includes discussion of the definitions of knowledge, expert systems (computer systems that solve tough technical problems), intelligent tutoring systems (ITS), and specific ITSs such as GUIDON, MYCIN,…

  19. Artificial Intelligence Methods: Choice of algorithms, their complexity, and appropriateness within the context of hydrology and water resources. (Invited)

    NASA Astrophysics Data System (ADS)

    Bastidas, L. A.; Pande, S.

    2009-12-01

    Pattern analysis deals with the automatic detection of patterns in the data and there are a variety of algorithms available for the purpose. These algorithms are commonly called Artificial Intelligence (AI) or data driven algorithms, and have been applied lately to a variety of problems in hydrology and are becoming extremely popular. When confronting such a range of algorithms, the question of which one is the “best” arises. Some algorithms may be preferred because of the lower computational complexity; others take into account prior knowledge of the form and the amount of the data; others are chosen based on a version of the Occam’s razor principle that a simple classifier performs better. Popper has argued, however, that Occam’s razor is without operational value because there is no clear measure or criterion for simplicity. An example of measures that can be used for this purpose are: the so called algorithmic complexity - also known as Kolmogorov complexity or Kolmogorov (algorithmic) entropy; the Bayesian information criterion; or the Vapnik-Chervonenkis dimension. On the other hand, the No Free Lunch Theorem states that there is no best general algorithm, and that specific algorithms are superior only for specific problems. It should be noted also that the appropriate algorithm and the appropriate complexity are constrained by the finiteness of the available data and the uncertainties associated with it. Thus, there is compromise between the complexity of the algorithm, the data properties, and the robustness of the predictions. We discuss the above topics; briefly review the historical development of applications with particular emphasis on statistical learning theory (SLT), also known as machine learning (ML) of which support vector machines and relevant vector machines are the most commonly known algorithms. We present some applications of such algorithms for distributed hydrologic modeling; and introduce an example of how the complexity measure

  20. Artificial Intelligence and Language Comprehension.

    ERIC Educational Resources Information Center

    National Inst. of Education (DHEW), Washington, DC. Basic Skills Group. Learning Div.

    The three papers in this volume concerning artificial intelligence and language comprehension were commissioned by the National Institute of Education to further the understanding of the cognitive processes that enable people to comprehend what they read. The first paper, "Artificial Intelligence and Language Comprehension," by Terry Winograd,…

  1. In Pursuit of Artificial Intelligence.

    ERIC Educational Resources Information Center

    Watstein, Sarah; Kesselman, Martin

    1986-01-01

    Defines artificial intelligence and reviews current research in natural language processing, expert systems, and robotics and sensory systems. Discussion covers current commercial applications of artificial intelligence and projections of uses and limitations in library technical and public services, e.g., in cataloging and online information and…

  2. A Primer on Artificial Intelligence.

    ERIC Educational Resources Information Center

    Leal, Ralph A.

    A survey of literature on recent advances in the field of artificial intelligence provides a comprehensive introduction to this field for the non-technical reader. Important areas covered are: (1) definitions, (2) the brain and thinking, (3) heuristic search, and (4) programing languages used in the research of artificial intelligence. Some…

  3. The language of artificial intelligence

    SciTech Connect

    Berk, A.A.

    1984-01-01

    This is a guide to LISP. It clarifies data structures and recursion via examples and practical explanations, uses BASIC as a reference point throughout (enabling comparisons with LISP), and stresses artificial intelligence applications. Contents: Introduction to Artificial Intelligence. The Fundamentals of LISP. Functions. Conditionals and Loops. Further LISP Processing. Recursion. More Advanced Uses of Functions. LISP Programming and AI. Standard LISP Functions.

  4. Artificial intelligence: Principles and applications

    SciTech Connect

    Yazdami, M.

    1985-01-01

    The book covers the principles of AI, the main areas of application, as well as considering some of the social implications. The applications chapters have a common format structured as follows: definition of the topic; approach with conventional computing techniques; why 'intelligence' would provide a better approach; and how AI techniques would be used and the limitations. The contents discussed are: Principles of artificial intelligence; AI programming environments; LISP, list processing and pattern-making; AI programming with POP-11; Computer processing of natural language; Speech synthesis and recognition; Computer vision; Artificial intelligence and robotics; The anatomy of expert systems - Forsyth; Machine learning; Memory models of man and machine; Artificial intelligence and cognitive psychology; Breaking out of the chinese room; Social implications of artificial intelligence; and Index.

  5. Artificial Intelligence and Science Education.

    ERIC Educational Resources Information Center

    Good, Ron

    1987-01-01

    Defines artificial intelligence (AI) in relation to intelligent computer-assisted instruction (ICAI) and science education. Provides a brief background of AI work, examples of expert systems, examples of ICAI work, and addresses problems facing AI workers that have implications for science education. Proposes a revised model of the Karplus/Renner…

  6. Artificial intelligence and intelligent tutoring systems

    SciTech Connect

    Livergood, N.D.

    1989-01-01

    As a species we have evolved by increasing our mental and physical powers through the deliberate development and use of instruments that amplify our inherent capabilities. Whereas hereditarily given instincts predetermine the actions of lower animal forms, human existence begins with freedom. As humans we can choose what actions we will perform. We have invented a technology called education to prepare ourselves for life. At present, our educational structures and procedures are failing to prepare us efficiently for the demands of modern life. One of the most important new technologies, in relation to human development, is the digital computer. This dissertation proposes that artificial intelligence maintain a highly critical technological awareness. Artificial intelligence, because of its origin as a politically sponsored field of investigation, must strive for constant awareness of its place within the larger political-economic world and its possible misuse by factions intent on manipulation and control. Computerized models of the human mind could be used in developing progressively more sophisticated brainwashing systems. Intelligent tutoring systems comprise an important new technology within the field of artificial intelligence. This dissertation explores specification and design procedures, functions and issues in developing intelligent tutoring systems.

  7. Artificial Intelligence and Its Importance in Education.

    ERIC Educational Resources Information Center

    Tilmann, Martha J.

    Artificial intelligence, or the study of ideas that enable computers to be intelligent, is discussed in terms of what it is, what it has done, what it can do, and how it may affect the teaching of tomorrow. An extensive overview of artificial intelligence examines its goals and applications and types of artificial intelligence including (1) expert…

  8. Network reconfiguration algorithm for automated distribution systems based on artificial intelligence approach

    SciTech Connect

    Kyunghee Jung; Hoyong Kim; Yunseok Ko . Dept. of Distribution System)

    1993-10-01

    This study develops an expert system to solve the problem of the main transformer (MTr) or feeder overload and the feeder constraint violation in automated distribution systems, where each feeder is subject to the thermal overload and voltage drop limits. Then, the objective is to perform the network reconfiguration by switching the tie and sectionalizing switches so that the system violation is removed, while achieving the load balance of the MTrs and feeders with a fewer number of switching operations. Since the switching operation in the practical system does not cause the large change in the voltage, an approximation method is used in order to check the voltage violation, instead of a full ac load flow solution. To reduce the search space, an expert system based on heuristic rules is presented, and implemented in Prolog. This system adopts the best-first tree search technique. List processing and recursive programming techniques are then utilized to solve the combinatorial type optimization problem. The computational results are also prepared to show the performance of the heuristic algorithms developed.

  9. Computational aerodynamics and artificial intelligence

    NASA Technical Reports Server (NTRS)

    Kutler, P.; Mehta, U. B.

    1984-01-01

    Some aspects of artificial intelligence are considered and questions are speculated on, including how knowledge-based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use 'expert' systems and how expert systems may speed the design and development process. The anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements are examined for using artificial intelligence in computational fluid dynamics and aerodynamics. Considering two of the essentials of computational aerodynamics - reasoniing and calculating - it is believed that a substantial part of the reasoning can be achieved with artificial intelligence, with computers being used as reasoning machines to set the stage for calculating. Expert systems will probably be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

  10. Computational aerodynamics and artificial intelligence

    NASA Technical Reports Server (NTRS)

    Mehta, U. B.; Kutler, P.

    1984-01-01

    The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

  11. Artificial Intelligence Databases: A Survey and Comparison.

    ERIC Educational Resources Information Center

    Stern, David

    1990-01-01

    Identifies and describes online databases containing references to materials on artificial intelligence, robotics, and expert systems, and compares them in terms of scope and usage. Recommendations for conducting online searches on artificial intelligence and related fields are offered. (CLB)

  12. Introducing artificial intelligence

    SciTech Connect

    Simons, G.L.

    1984-01-01

    This book describes the background to AI, explores some characteristic objectives and methods, and indicates some of the practical ramifications for expert, robotic and other types of systems. Following a brief discussion of the nature of intelligence, the recent history of AI is outlined. Characteristic activities of AI systems are explored in Part II. Here it is emphasized that AI systems are not only concerned with ''thought'' but with ''action''-it is an obvious requirement of intelligent commercial and other systems that they behave with competence in a real-world environment. Finally some of the current and future uses of AI systems are explored.

  13. Artificial Intelligence: A Selected Bibliography.

    ERIC Educational Resources Information Center

    Smith, Linda C., Comp.

    1984-01-01

    This 19-item annotated bibliography introducing the literature of artificial intelligence (AI) is arranged by type of material--handbook, books (general interest, textbooks, collected readings), journals and newsletters, and conferences and workshops. The availability of technical reports from AI laboratories at universities and private companies…

  14. Artificial Intelligence and Expert Systems.

    ERIC Educational Resources Information Center

    Lawlor, Joseph

    Artificial intelligence (AI) is the field of scientific inquiry concerned with designing machine systems that can simulate human mental processes. The field draws upon theoretical constructs from a wide variety of disciplines, including mathematics, psychology, linguistics, neurophysiology, computer science, and electronic engineering. Some of the…

  15. Thinking, Creativity, and Artificial Intelligence.

    ERIC Educational Resources Information Center

    DeSiano, Michael; DeSiano, Salvatore

    This document provides an introduction to the relationship between the current knowledge of focused and creative thinking and artificial intelligence. A model for stages of focused and creative thinking gives: problem encounter/setting, preparation, concentration/incubation, clarification/generation and evaluation/judgment. While a computer can…

  16. Artificial Intelligence Assists Ultrasonic Inspection

    NASA Technical Reports Server (NTRS)

    Schaefer, Lloyd A.; Willenberg, James D.

    1992-01-01

    Subtle indications of flaws extracted from ultrasonic waveforms. Ultrasonic-inspection system uses artificial intelligence to help in identification of hidden flaws in electron-beam-welded castings. System involves application of flaw-classification logic to analysis of ultrasonic waveforms.

  17. Accelerating artificial intelligence with reconfigurable computing

    NASA Astrophysics Data System (ADS)

    Cieszewski, Radoslaw

    Reconfigurable computing is emerging as an important area of research in computer architectures and software systems. Many algorithms can be greatly accelerated by placing the computationally intense portions of an algorithm into reconfigurable hardware. Reconfigurable computing combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be changed over the lifetime of the system. Similar to an ASIC, reconfigurable systems provide a method to map circuits into hardware. Reconfigurable systems therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Such a field, where there is many different algorithms which can be accelerated, is an artificial intelligence. This paper presents example hardware implementations of Artificial Neural Networks, Genetic Algorithms and Expert Systems.

  18. Artificial symbols and the essence of intelligent computing

    NASA Astrophysics Data System (ADS)

    Magnus, Amy L.; Oxley, Mark E.

    2003-08-01

    A challenge for intelligent computing is translating the skills of innovation into mathematical theory and persistent learning algorithms. Computational intelligence differs from artificial intelligence in that artificial intelligence reasons over symbols while computational intelligence reasons over sub-symbolic data and information. Natural symbos arise from shared human experiences. The creative quality of human interaction suggests symbol generation involves a collection of cooperative agents capable of representing relative experience, negotiating innovation, and---finally---building consensus. As hybrids of sub-symbolic and symbolic reasoning become the norm, it is necessary to formalize the design and evaluation of artificial symbols. In this paper, we delineate the difference between sub-symbolic patterns and symbolic experience. Further, we propose fundamental theory supporting the autonomous construction of artificial symbols which---we assert---is the ultimate culmination of an intelligent computation. We apply this theory to model selection among neural networks.

  19. Artificial intelligence and simulation

    SciTech Connect

    Holmes, W.M.

    1985-01-01

    The research and development of AI are discussed. Papers are presented on an expert system for chemical process control, an ocean surveillance information fusion expert system, a distributed intelligence system and aircraft pilotage, a procedure for speeding innovation by transferring scientific knowledge more quickly, and syntax programming, expert systems, and real-time fault diagnosis. Consideration is given to an expert system for modeling NASA flight control room usage, simulating aphasia, a method for single neuron recognition of letters, numbers, faces, and certain types of concepts, integrating AI and control system approach, testing an expert system for manufacturing, and the human memory.

  20. Artificial intelligence: Principles and applications

    SciTech Connect

    Yazdani, M.

    1986-01-01

    Following the Japanese announcement that they intend to devise, make, and market, in the 1990s, computers incorporating a level of intelligence, a vast amount of energy and expense has been diverted at the field of Artificial Intelligence. Workers for the past 25 years in this discipline have tried to reproduce human behavior on computers and this book presents their achievements and the problems. Subjects include: computer vision, speech processing, robotics, natural language processing expert systems and machine learning. The book also attempts to show the general principles behind the various applications and finally attempts to show their implications for other human endeavors such as philosophy, psychology, and the development of modern society.

  1. Automated Scheduling Via Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Biefeld, Eric W.; Cooper, Lynne P.

    1991-01-01

    Artificial-intelligence software that automates scheduling developed in Operations Mission Planner (OMP) research project. Software used in both generation of new schedules and modification of existing schedules in view of changes in tasks and/or available resources. Approach based on iterative refinement. Although project focused upon scheduling of operations of scientific instruments and other equipment aboard spacecraft, also applicable to such terrestrial problems as scheduling production in factory.

  2. Artificial intelligence and science education

    NASA Astrophysics Data System (ADS)

    Good, Ron

    Artificial intelligence (AI) is defined and related to intelligent computer-assisted instruction (ICAI) and science education. Modeling the student, the teacher, and the natural environment are discussed as important parts of ICAI and the concept of microworlds as a powerful tool for science education is presented. Optimistic predictions about ICAI are tempered with the complex, persistent problems of: 1) teaching and learning as a soft or fuzzy knowledge base, 2) natural language processing, and 3) machine learning. The importance of accurate diagnosis of a student's learning state, including misconceptions and naive theories about nature, is stressed and related to the importance of accurate diagnosis by a physician. Based on the cognitive science/AI paradigm, a revised model of the well-known Karplus/Renner learning cycle is proposed.

  3. Innovative applications of artificial intelligence

    NASA Astrophysics Data System (ADS)

    Schorr, Herbert; Rappaport, Alain

    Papers concerning applications of artificial intelligence are presented, covering applications in aerospace technology, banking and finance, biotechnology, emergency services, law, media planning, music, the military, operations management, personnel management, retail packaging, and manufacturing assembly and design. Specific topics include Space Shuttle telemetry monitoring, an intelligent training system for Space Shuttle flight controllers, an expert system for the diagnostics of manufacturing equipment, a logistics management system, a cooling systems design assistant, and a knowledge-based integrated circuit design critic. Additional topics include a hydraulic circuit design assistant, the use of a connector assembly specification expert system to harness detailed assembly process knowledge, a mixed initiative approach to airlift planning, naval battle management decision aids, an inventory simulation tool, a peptide synthesis expert system, and a system for planning the discharging and loading of container ships.

  4. Innovative applications of artificial intelligence

    SciTech Connect

    Schorr, H.; Rappaport, A.

    1989-01-01

    Papers concerning applications of artificial intelligence are presented, covering applications in aerospace technology, banking and finance, biotechnology, emergency services, law, media planning, music, the military, operations management, personnel management, retail packaging, and manufacturing assembly and design. Specific topics include Space Shuttle telemetry monitoring, an intelligent training system for Space Shuttle flight controllers, an expert system for the diagnostics of manufacturing equipment, a logistics management system, a cooling systems design assistant, and a knowledge-based integrated circuit design critic. Additional topics include a hydraulic circuit design assistant, the use of a connector assembly specification expert system to harness detailed assembly process knowledge, a mixed initiative approach to airlift planning, naval battle management decision aids, an inventory simulation tool, a peptide synthesis expert system, and a system for planning the discharging and loading of container ships.

  5. Application of artificial intelligence to robotic vision

    SciTech Connect

    Chao, P.S.; Frick, P.A.

    1983-01-01

    A brief introduction to artificial intelligence (AI) and the general vision process is provided. Two samples of AI researchers' work toward general computer vision are given. The first is a model-based vision system while the second is based on results of studies on human vision. The current state of machine vision in industrial robotics is demonstrated using a well known vision algorithm developed at SRI International. A part of a prototype robotic assembly project with vision is sketched to show the application of some AI tools to practical work. 8 references.

  6. Research and applications: Artificial intelligence

    NASA Technical Reports Server (NTRS)

    Raphael, B.; Duda, R. O.; Fikes, R. E.; Hart, P. E.; Nilsson, N. J.; Thorndyke, P. W.; Wilber, B. M.

    1971-01-01

    Research in the field of artificial intelligence is discussed. The focus of recent work has been the design, implementation, and integration of a completely new system for the control of a robot that plans, learns, and carries out tasks autonomously in a real laboratory environment. The computer implementation of low-level and intermediate-level actions; routines for automated vision; and the planning, generalization, and execution mechanisms are reported. A scenario that demonstrates the approximate capabilities of the current version of the entire robot system is presented.

  7. Epistasis analysis using artificial intelligence.

    PubMed

    Moore, Jason H; Hill, Doug P

    2015-01-01

    Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of expert knowledge as a guide. The methodology presented here is based on computational evolution, which is a type of genetic programming. The ability to generate interesting solutions while at the same time learning how to solve the problem at hand distinguishes computational evolution from other genetic programming approaches. We provide a general overview of this approach and then present a few examples of its application to real data.

  8. Improving designer productivity. [artificial intelligence

    NASA Technical Reports Server (NTRS)

    Hill, Gary C.

    1992-01-01

    Designer and design team productivity improves with skill, experience, and the tools available. The design process involves numerous trials and errors, analyses, refinements, and addition of details. Computerized tools have greatly speeded the analysis, and now new theories and methods, emerging under the label Artificial Intelligence (AI), are being used to automate skill and experience. These tools improve designer productivity by capturing experience, emulating recognized skillful designers, and making the essence of complex programs easier to grasp. This paper outlines the aircraft design process in today's technology and business climate, presenting some of the challenges ahead and some of the promising AI methods for meeting these challenges.

  9. Advanced Artificial Intelligence Technology Testbed

    NASA Technical Reports Server (NTRS)

    Anken, Craig S.

    1993-01-01

    The Advanced Artificial Intelligence Technology Testbed (AAITT) is a laboratory testbed for the design, analysis, integration, evaluation, and exercising of large-scale, complex, software systems, composed of both knowledge-based and conventional components. The AAITT assists its users in the following ways: configuring various problem-solving application suites; observing and measuring the behavior of these applications and the interactions between their constituent modules; gathering and analyzing statistics about the occurrence of key events; and flexibly and quickly altering the interaction of modules within the applications for further study.

  10. Economic reasoning and artificial intelligence.

    PubMed

    Parkes, David C; Wellman, Michael P

    2015-07-17

    The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics. We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs. Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.

  11. Program for Development of Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Riley, Gary; Culbert, Chris; Lopez, Frank

    1987-01-01

    C Language Integrated Production System (CLIPS) computer program is shell for developing expert systems. Designed to enable research, development, and delivery of artificial intelligence on conventional computers. Primary design goals for CLIPS are portability, efficiency, and functionality. Meets or out-performs most microcomputer- and minicomputer-based artificial-intelligence tools. Written in C.

  12. Artificial Intelligence in Education: An Exploration.

    ERIC Educational Resources Information Center

    Cumming, Geoff

    1998-01-01

    Gives a brief outline of the development of Artificial Intelligence in Education (AIED) which includes psychology, education, cognitive science, computer science, and artificial intelligence. Highlights include learning environments; learner modeling; a situated approach to learning; and current examples of AIED research. (LRW)

  13. The Case for Artificial Intelligence in Medicine

    PubMed Central

    Reggia, James A.

    1983-01-01

    Current artificial intelligence (AI) technology can be viewed as producing “systematic artifacts” onto which we project an interpretation of intelligent behavior. One major benefit this technology could bring to medicine is help with handling the tremendous and growing volume of medical knowledge. The reader is led to a vision of the medical library of tomorrow, an interactive, artificially intelligent knowledge source that is fully and directly integrated with daily patient care.

  14. How artificial intelligence impacts E P productivity

    SciTech Connect

    McCormack, M.D.; Day, R. )

    1993-10-01

    Since artificial intelligence (AI) techniques became aligned with conventional computer hardware architectures in the middle 1980s, economical applications to E P needs have become available. Expert systems, fuzzy logic systems, neural networks and genetic algorithms are four AI technologies having a major impact in the petroleum industry. At present, these technologies are at different stages of maturity. Expert systems are the most mature, and genetic algorithms the least. However, all four have evolved such that practical applications have been produced. Further progress in applying AI to the petroleum industry will come through combining two or more of these techniques. In addition, other AI techniques such as case-based reasoning and database mining continue to be introduced. An introduction to these techniques and a look at their current practical applications is presented.

  15. Research and applications: Artificial intelligence

    NASA Technical Reports Server (NTRS)

    Raphael, B.; Fikes, R. E.; Chaitin, L. J.; Hart, P. E.; Duda, R. O.; Nilsson, N. J.

    1971-01-01

    A program of research in the field of artificial intelligence is presented. The research areas discussed include automatic theorem proving, representations of real-world environments, problem-solving methods, the design of a programming system for problem-solving research, techniques for general scene analysis based upon television data, and the problems of assembling an integrated robot system. Major accomplishments include the development of a new problem-solving system that uses both formal logical inference and informal heuristic methods, the development of a method of automatic learning by generalization, and the design of the overall structure of a new complete robot system. Eight appendices to the report contain extensive technical details of the work described.

  16. Artificial intelligence in medical diagnosis.

    PubMed

    Szolovits, P; Patil, R S; Schwartz, W B

    1988-01-01

    In an attempt to overcome limitations inherent in conventional computer-aided diagnosis, investigators have created programs that simulate expert human reasoning. Hopes that such a strategy would lead to clinically useful programs have not been fulfilled, but many of the problems impeding creation of effective artificial intelligence programs have been solved. Strategies have been developed to limit the number of hypotheses that a program must consider and to incorporate pathophysiologic reasoning. The latter innovation permits a program to analyze cases in which one disorder influences the presentation of another. Prototypes embodying such reasoning can explain their conclusions in medical terms that can be reviewed by the user. Despite these advances, further major research and developmental efforts will be necessary before expert performance by the computer becomes a reality.

  17. Artificial Intelligence and Information Management

    NASA Astrophysics Data System (ADS)

    Fukumura, Teruo

    After reviewing the recent popularization of the information transmission and processing technologies, which are supported by the progress of electronics, the authors describe that by the introduction of the opto-electronics into the information technology, the possibility of applying the artificial intelligence (AI) technique to the mechanization of the information management has emerged. It is pointed out that althuogh AI deals with problems in the mental world, its basic methodology relies upon the verification by evidence, so the experiment on computers become indispensable for the study of AI. The authors also describe that as computers operate by the program, the basic intelligence which is concerned in AI is that expressed by languages. This results in the fact that the main tool of AI is the logical proof and it involves an intrinsic limitation. To answer a question “Why do you employ AI in your problem solving”, one must have ill-structured problems and intend to conduct deep studies on the thinking and the inference, and the memory and the knowledge-representation. Finally the authors discuss the application of AI technique to the information management. The possibility of the expert-system, processing of the query, and the necessity of document knowledge-base are stated.

  18. Predicting asthma exacerbations using artificial intelligence.

    PubMed

    Finkelstein, Joseph; Wood, Jeffrey

    2013-01-01

    Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.

  19. Parallel computation and computers for artificial intelligence

    SciTech Connect

    Kowalik, J.S. )

    1988-01-01

    This book discusses Parallel Processing in Artificial Intelligence; Parallel Computing using Multilisp; Execution of Common Lisp in a Parallel Environment; Qlisp; Restricted AND-Parallel Execution of Logic Programs; PARLOG: Parallel Programming in Logic; and Data-driven Processing of Semantic Nets. Attention is also given to: Application of the Butterfly Parallel Processor in Artificial Intelligence; On the Range of Applicability of an Artificial Intelligence Machine; Low-level Vision on Warp and the Apply Programming Mode; AHR: A Parallel Computer for Pure Lisp; FAIM-1: An Architecture for Symbolic Multi-processing; and Overview of Al Application Oriented Parallel Processing Research in Japan.

  20. The Biological Relevance of Artificial Life: Lessons from Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Colombano, Silvano

    2000-01-01

    There is no fundamental reason why A-life couldn't simply be a branch of computer science that deals with algorithms that are inspired by, or emulate biological phenomena. However, if these are the limits we place on this field, we miss the opportunity to help advance Theoretical Biology and to contribute to a deeper understanding of the nature of life. The history of Artificial Intelligence provides a good example, in that early interest in the nature of cognition quickly was lost to the process of building tools, such as "expert systems" that, were certainly useful, but provided little insight in the nature of cognition. Based on this lesson, I will discuss criteria for increasing the biological relevance of A-life and the probability that this field may provide a theoretical foundation for Biology.

  1. New directions for Artificial Intelligence (AI) methods in optimum design

    NASA Technical Reports Server (NTRS)

    Hajela, Prabhat

    1989-01-01

    Developments and applications of artificial intelligence (AI) methods in the design of structural systems is reviewed. Principal shortcomings in the current approach are emphasized, and the need for some degree of formalism in the development environment for such design tools is underscored. Emphasis is placed on efforts to integrate algorithmic computations in expert systems.

  2. Artificial Intelligence, Robots and Education: Selected Sources.

    ERIC Educational Resources Information Center

    Kissinger, Pat

    1987-01-01

    This annotated bibliography describes 12 books, 10 ERIC publications, and 7 periodical articles about artificial intelligence and robotics that were selected by the author as resources for educators. (CLB)

  3. Artificial intelligence: Learning to see and act

    NASA Astrophysics Data System (ADS)

    Schölkopf, Bernhard

    2015-02-01

    An artificial-intelligence system uses machine learning from massive training sets to teach itself to play 49 classic computer games, demonstrating that it can adapt to a variety of tasks. See Letter p.529

  4. Artificial Intelligence and Vocational Education: An Impending Confluence.

    ERIC Educational Resources Information Center

    Roth, Gene L.; McEwing, Richard A.

    1986-01-01

    Reports on the relatively new field of artificial intelligence and its relationship to vocational education. Compares human intelligence with artificial intelligence. Discusses expert systems, natural language technology, and current trends. Lists potential applications for vocational education. (CH)

  5. An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part C: Basic AI topics

    NASA Technical Reports Server (NTRS)

    Gevarter, W. B.

    1983-01-01

    Readily understandable overviews of search oriented problem solving, knowledge representation, and computational logic are provided. Mechanization, automation and artificial intelligence are discussed as well as how they interrelate.

  6. Artificial Intelligence for Controlling Robotic Aircraft

    NASA Technical Reports Server (NTRS)

    Krishnakumar, Kalmanje

    2005-01-01

    A document consisting mostly of lecture slides presents overviews of artificial-intelligence-based control methods now under development for application to robotic aircraft [called Unmanned Aerial Vehicles (UAVs) in the paper] and spacecraft and to the next generation of flight controllers for piloted aircraft. Following brief introductory remarks, the paper presents background information on intelligent control, including basic characteristics defining intelligent systems and intelligent control and the concept of levels of intelligent control. Next, the paper addresses several concepts in intelligent flight control. The document ends with some concluding remarks, including statements to the effect that (1) intelligent control architectures can guarantee stability of inner control loops and (2) for UAVs, intelligent control provides a robust way to accommodate an outer-loop control architecture for planning and/or related purposes.

  7. Artificial intelligence approaches to astronomical observation scheduling

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.; Miller, Glenn

    1988-01-01

    Automated scheduling will play an increasing role in future ground- and space-based observatory operations. Due to the complexity of the problem, artificial intelligence technology currently offers the greatest potential for the development of scheduling tools with sufficient power and flexibility to handle realistic scheduling situations. Summarized here are the main features of the observatory scheduling problem, how artificial intelligence (AI) techniques can be applied, and recent progress in AI scheduling for Hubble Space Telescope.

  8. Artificial Intelligence and Spacecraft Power Systems

    NASA Technical Reports Server (NTRS)

    Dugel-Whitehead, Norma R.

    1997-01-01

    This talk will present the work which has been done at NASA Marshall Space Flight Center involving the use of Artificial Intelligence to control the power system in a spacecraft. The presentation will include a brief history of power system automation, and some basic definitions of the types of artificial intelligence which have been investigated at MSFC for power system automation. A video tape of one of our autonomous power systems using co-operating expert systems, and advanced hardware will be presented.

  9. Integrated mechanism design with artificial intelligence assistance

    SciTech Connect

    Bohatier, C.; Silberberg, Y.; Guillot, J.

    1996-11-01

    The proposed design methodology is developed as an integrated process in the production process. A setup of design workshop is suggested. An original functional approach is proposed. Its implementation using artificial intelligence is performed by an expert system generator. This intelligent assistance prototype is open to other aided computer engineering fields. Finally, an application to mechanism design is presented.

  10. Artificial Intelligence Applications for Education: Promise, ...Promises.

    ERIC Educational Resources Information Center

    Adams, Dennis M.; Hamm, Mary

    1988-01-01

    Surveys the current status of artificial intelligence (AI) technology. Discusses intelligent tutoring systems, robotics, and applications for educators. Likens the status of AI at present to that of aviation in the very early 1900s. States that educators need to be involved in future debates concerning AI. (CW)

  11. Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom.

    PubMed

    Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

    2012-10-22

    A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

  12. Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom

    PubMed Central

    Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

    2012-01-01

    A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students. PMID:23202042

  13. Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom.

    PubMed

    Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

    2012-01-01

    A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students. PMID:23202042

  14. Medical target prediction from genome sequence: combining different sequence analysis algorithms with expert knowledge and input from artificial intelligence approaches.

    PubMed

    Dandekar, T; Du, F; Schirmer, R H; Schmidt, S

    2001-12-01

    By exploiting the rapid increase in available sequence data, the definition of medically relevant protein targets has been improved by a combination of: (i) differential genome analysis (target list): and (ii) analysis of individual proteins (target analysis). Fast sequence comparisons, data mining, and genetic algorithms further promote these procedures. Mycobacterium tuberculosis proteins were chosen as applied examples.

  15. Application Of Artificial Intelligence To Wind Tunnels

    NASA Technical Reports Server (NTRS)

    Lo, Ching F.; Steinle, Frank W., Jr.

    1989-01-01

    Report discusses potential use of artificial-intelligence systems to manage wind-tunnel test facilities at Ames Research Center. One of goals of program to obtain experimental data of better quality and otherwise generally increase productivity of facilities. Another goal to increase efficiency and expertise of current personnel and to retain expertise of former personnel. Third goal to increase effectiveness of management through more efficient use of accumulated data. System used to improve schedules of operation and maintenance of tunnels and other equipment, assignment of personnel, distribution of electrical power, and analysis of costs and productivity. Several commercial artificial-intelligence computer programs discussed as possible candidates for use.

  16. Fundamental research in artificial intelligence at NASA

    NASA Technical Reports Server (NTRS)

    Friedland, Peter

    1990-01-01

    This paper describes basic research at NASA in the field of artificial intelligence. The work is conducted at the Ames Research Center and the Jet Propulsion Laboratory, primarily under the auspices of the NASA-wide Artificial Intelligence Program in the Office of Aeronautics, Exploration and Technology. The research is aimed at solving long-term NASA problems in missions operations, spacecraft autonomy, preservation of corporate knowledge about NASA missions and vehicles, and management/analysis of scientific and engineering data. From a scientific point of view, the research is broken into the categories of: planning and scheduling; machine learning; and design of and reasoning about large-scale physical systems.

  17. Abstraction and reformulation in artificial intelligence.

    PubMed Central

    Holte, Robert C.; Choueiry, Berthe Y.

    2003-01-01

    This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense. PMID:12903653

  18. A Primer for Problem Solving Using Artificial Intelligence.

    ERIC Educational Resources Information Center

    Schell, George P.

    1988-01-01

    Reviews the development of artificial intelligence systems and the mechanisms used, including knowledge representation, programing languages, and problem processing systems. Eleven books and 6 journals are listed as sources of information on artificial intelligence. (23 references) (CLB)

  19. Future applications of artificial intelligence to Mission Control Centers

    NASA Technical Reports Server (NTRS)

    Friedland, Peter

    1991-01-01

    Future applications of artificial intelligence to Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: basic objectives of the NASA-wide AI program; inhouse research program; constraint-based scheduling; learning and performance improvement for scheduling; GEMPLAN multi-agent planner; planning, scheduling, and control; Bayesian learning; efficient learning algorithms; ICARUS (an integrated architecture for learning); design knowledge acquisition and retention; computer-integrated documentation; and some speculation on future applications.

  20. Artificial Intelligence. LC Science Tracer Bullet.

    ERIC Educational Resources Information Center

    Rodgers, Kay, Comp.

    Desgined to serve as a guide to resources on artificial intelligence (AI) and expert systems, this Library of Congress bulletin is divided into 18 sections that contain lists of books, journal articles, periodicals, associations, and other sources of information. A brief statement of the scope of the guide introduces the sections, which are listed…

  1. A Starter's Guide to Artificial Intelligence.

    ERIC Educational Resources Information Center

    McConnell, Barry A.; McConnell, Nancy J.

    1988-01-01

    Discussion of the history and development of artificial intelligence (AI) highlights a bibliography of introductory books on various aspects of AI, including AI programing; problem solving; automated reasoning; game playing; natural language; expert systems; machine learning; robotics and vision; critics of AI; and representative software. (LRW)

  2. Artificial Intelligence, Counseling, and Cognitive Psychology.

    ERIC Educational Resources Information Center

    Brack, Greg; And Others

    With the exception of a few key writers, counselors largely ignore the benefits that Artificial Intelligence (AI) and Cognitive Psychology (CP) can bring to counseling. It is demonstrated that AI and CP can be integrated into the counseling literature. How AI and CP can offer new perspectives on information processing, cognition, and helping is…

  3. Artificial Intelligence: Underlying Assumptions and Basic Objectives.

    ERIC Educational Resources Information Center

    Cercone, Nick; McCalla, Gordon

    1984-01-01

    Presents perspectives on methodological assumptions underlying research efforts in artificial intelligence (AI) and charts activities, motivations, methods, and current status of research in each of the major AI subareas: natural language understanding; computer vision; expert systems; search, problem solving, planning; theorem proving and logic…

  4. Artificial Intelligence Applications to Videodisc Technology

    PubMed Central

    Vries, John K.; Banks, Gordon; McLinden, Sean; Moossy, John; Brown, Melanie

    1985-01-01

    Much of medical information is visual in nature. Since it is not easy to describe pictorial information in linguistic terms, it has been difficult to store and retrieve this type of information. Coupling videodisc technology with artificial intelligence programming techniques may provide a means for solving this problem.

  5. Dynamic Restructuring Of Problems In Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Schwuttke, Ursula M.

    1992-01-01

    "Dynamic tradeoff evaluation" (DTE) denotes proposed method and procedure for restructuring problem-solving strategies in artificial intelligence to satisfy need for timely responses to changing conditions. Detects situations in which optimal problem-solving strategies cannot be pursued because of real-time constraints, and effects tradeoffs among nonoptimal strategies in such way to minimize adverse effects upon performance of system.

  6. Applications of artificial intelligence to mission planning

    NASA Technical Reports Server (NTRS)

    Ford, Donnie R.; Rogers, John S.; Floyd, Stephen A.

    1990-01-01

    The scheduling problem facing NASA-Marshall mission planning is extremely difficult for several reasons. The most critical factor is the computational complexity involved in developing a schedule. The size of the search space is large along some dimensions and infinite along others. It is because of this and other difficulties that many of the conventional operation research techniques are not feasible or inadequate to solve the problems by themselves. Therefore, the purpose is to examine various artificial intelligence (AI) techniques to assist conventional techniques or to replace them. The specific tasks performed were as follows: (1) to identify mission planning applications for object oriented and rule based programming; (2) to investigate interfacing AI dedicated hardware (Lisp machines) to VAX hardware; (3) to demonstrate how Lisp may be called from within FORTRAN programs; (4) to investigate and report on programming techniques used in some commercial AI shells, such as Knowledge Engineering Environment (KEE); and (5) to study and report on algorithmic methods to reduce complexity as related to AI techniques.

  7. Artificial intelligence: Robots with instincts

    NASA Astrophysics Data System (ADS)

    Adami, Christoph

    2015-05-01

    An evolutionary algorithm has been developed that allows robots to adapt to unforeseen change. The robots learn behaviours quickly and instinctively by mining the memory of their past achievements. See Letter p.503

  8. Artificial Intelligence Application in Power Generation Industry: Initial considerations

    NASA Astrophysics Data System (ADS)

    Ismail, Rahmat Izaizi B.; Ismail Alnaimi, Firas B.; AL-Qrimli, Haidar F.

    2016-03-01

    With increased competitiveness in power generation industries, more resources are directed in optimizing plant operation, including fault detection and diagnosis. One of the most powerful tools in faults detection and diagnosis is artificial intelligence (AI). Faults should be detected early so correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary interruption and downtime. For the last few decades there has been major interest towards intelligent condition monitoring system (ICMS) application in power plant especially with AI development particularly in artificial neural network (ANN). ANN is based on quite simple principles, but takes advantage of their mathematical nature, non-linear iteration to demonstrate powerful problem solving ability. With massive possibility and room for improvement in AI, the inspiration for researching them are apparent, and literally, hundreds of papers have been published, discussing the findings of hybrid AI for condition monitoring purposes. In this paper, the studies of ANN and genetic algorithm (GA) application will be presented.

  9. Projective simulation for artificial intelligence

    PubMed Central

    Briegel, Hans J.; De las Cuevas, Gemma

    2012-01-01

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

  10. Artificial intelligence - NASA. [robotics for Space Station

    NASA Technical Reports Server (NTRS)

    Erickson, J. D.

    1985-01-01

    Artificial Intelligence (AI) represents a vital common space support element needed to enable the civil space program and commercial space program to perform their missions successfully. It is pointed out that advances in AI stimulated by the Space Station Program could benefit the U.S. in many ways. A fundamental challenge for the civil space program is to meet the needs of the customers and users of space with facilities enabling maximum productivity and having low start-up costs, and low annual operating costs. An effective way to meet this challenge may involve a man-machine system in which artificial intelligence, robotics, and advanced automation are integrated into high reliability organizations. Attention is given to the benefits, NASA strategy for AI, candidate space station systems, the Space Station as a stepping stone, and the commercialization of space.

  11. Artificial intelligence in medicine: the challenges ahead.

    PubMed Central

    Coiera, E W

    1996-01-01

    The modern study of artificial intelligence in medicine (AIM) is 25 years old. Throughout this period, the field has attracted many of the best computer scientists, and their work represents a remarkable achievement. However, AIM has not been successful-if success is judged as making an impact on the practice of medicine. Much recent work in AIM has been focused inward, addressing problems that are at the crossroads of the parent disciplines of medicine and artificial intelligence. Now, AIM must move forward with the insights that it has gained and focus on finding solutions for problems at the heart of medical practice. The growing emphasis within medicine on evidence-based practice should provide the right environment for that change. PMID:8930853

  12. Artificial intelligence: Theory, logic and application

    SciTech Connect

    Brule, J.F.

    1986-01-01

    This book discusses the principles and applications of artificial intelligence. It provides the details on the most prominent languages currently being used to develop artificial intelligence, LISP and Prolog. It is shown how the computer is made to understand knowledge and how it can retrieve and manipulate that knowledge through frames, semantic networks, state space, belief systems, and the LEAP data base. Once the fundamentals are discussed, the author delves deeper, explaining: how computers are able to make logical deductions...methods used to move the computer through a knowledge base...and even shows readers what takes place as a computer attempts to solve problems. A series of programming exercises are also provided that illustrate all the important concepts learned from each chapter.

  13. Artificial intelligence for turboprop engine maintenance

    SciTech Connect

    1995-01-01

    Long-term maintenance operations, causing the unit to out of action, may seem economical - but they result in reduced operating readiness. Offsetting that concern, careless, hurried maintenance reduces margins of safety and reliability. Any tool that improves maintenance without causing a sharp increase in cost is valuable. Artificial intelligence (AI) is one of the tools. Expert system and neural networks are two different areas of AI that show promise for turboprop engine maintenance.

  14. Flang: A language for artificial intelligence

    SciTech Connect

    Mantsivoda, A.V.

    1994-05-01

    Logic programming - an area of informatics combining mathematical logic, artificial intelligence, and programming - has undergone considerably accelerated development in the past few years. The Prolog language - the principal representative of logic programming - is gaining ever-increasing popularity for the solution of practical problems. The appearance of the Warren abstract machine has greatly increased the effectiveness of Prolog. Nevertheless, a number of serious difficulties remain in logic programming.

  15. Encyclopedia of artificial intelligence: 2 Vol. set

    SciTech Connect

    Shapiro, S.C.

    1987-01-01

    Drawing on the fields of computer science, electrical engineering, linguistics, mathematics, philosophy, psychology, and physiology, this one-volume encyclopedia brings together the core of knowledge on artificial intelligence. It provides an overview of how to program computers to emulate human behavior, offering a wide range of techniques for speech and visual generation, problem-solving and more. Over 250 entries are organized alphabetically, cross-referenced and indexed.

  16. Applications of artificial intelligence to scientific research

    NASA Technical Reports Server (NTRS)

    Prince, Mary Ellen

    1986-01-01

    Artificial intelligence (AI) is a growing field which is just beginning to make an impact on disciplines other than computer science. While a number of military and commercial applications were undertaken in recent years, few attempts were made to apply AI techniques to basic scientific research. There is no inherent reason for the discrepancy. The characteristics of the problem, rather than its domain, determines whether or not it is suitable for an AI approach. Expert system, intelligent tutoring systems, and learning programs are examples of theoretical topics which can be applied to certain areas of scientific research. Further research and experimentation should eventurally make it possible for computers to act as intelligent assistants to scientists.

  17. Teachers and artificial intelligence. The Logo connection.

    PubMed

    Merbler, J B

    1990-12-01

    This article describes a three-phase program for training special education teachers to teach Logo and artificial intelligence. Logo is derived from the LISP computer language and is relatively simple to learn and use, and it is argued that these factors make it an ideal tool for classroom experimentation in basic artificial intelligence concepts. The program trains teachers to develop simple demonstrations of artificial intelligence using Logo. The material that the teachers learn to teach is suitable as an advanced level topic for intermediate- through secondary-level students enrolled in computer competency or similar courses. The material emphasizes problem-solving and thinking skills using a nonverbal expressive medium (Logo), thus it is deemed especially appropriate for hearing-impaired children. It is also sufficiently challenging for academically talented children, whether hearing or deaf. Although the notion of teachers as programmers is controversial, Logo is relatively easy to learn, has direct implications for education, and has been found to be an excellent tool for empowerment-for both teachers and children. PMID:2091452

  18. Connectionist architectures for artificial intelligence

    SciTech Connect

    Fahlman, S.E.; Hinton, G.E.

    1987-01-01

    A number of researchers have begun exploring the use of massively parallel architectures in an attempt to get around the limitations of conventional symbol processing. Many of these parallel architectures are connectionist: The system's collection of permanent knowledge is stored as a pattern of connections or connection strengths among the processing elements, so the knowledge directly determines how the processing elements interact rather that sitting passively in a memory, waiting to be looked at by the CPU. Some connectionist schemes use formal, symbolic representations, while others use more analog approaches. Some even develop their own internal representations after seeing examples of the patterns they are to recognize or the relationships they are to store. Connectionism is somewhat controversial in the AI community. It is new, still unproven in large-scale practical applications, and very different in style from the traditional AI approach. The authors have only begun to explore the behavior and potential of connectionist networks. In this article, the authors describe some of the central issues and ideas of connectionism, and also some of the unsolved problems facing this approach. Part of the motivation for connectionist research is the possible similarity in function between connectionist networks and the neutral networks of the human cortex, but they concentrate here on connectionism's potential as a practical technology for building intelligent systems.

  19. Artificial Intelligence and Computer Assisted Instruction. CITE Report No. 4.

    ERIC Educational Resources Information Center

    Elsom-Cook, Mark

    The purpose of the paper is to outline some of the major ways in which artificial intelligence research and techniques can affect usage of computers in an educational environment. The role of artificial intelligence is defined, and the difference between Computer Aided Instruction (CAI) and Intelligent Computer Aided Instruction (ICAI) is…

  20. An artificial intelligence approach to accelerator control systems

    SciTech Connect

    Schultz, D.E.; Hurd, J.W.; Brown, S.K.

    1987-01-01

    An experiment was recently started at LAMPF to evaluate the power and limitations of using artificial intelligence techniques to solve problems in accelerator control and operation. A knowledge base was developed to describe the characteristics and the relationships of the first 30 devices in the LAMPF H+ beam line. Each device was categorized and pertinent attributes for each category defined. Specific values were assigned in the knowledge base to represent each actual device. Relationships between devices are modeled using the artificial intelligence techniques of rules, active values, and object-oriented methods. This symbolic model, built using the Knowledge Engineering Environment (KEE) system, provides a framework for analyzing faults, tutoring trainee operators, and offering suggestions to assist in beam tuning. Based on information provided by the domain expert responsible for tuning this portion of the beam line, additional rules were written to describe how he tunes, how he analyzes what is actually happening, and how he deals with failures. Initial results have shown that artificial intelligence techniques can be a useful adjunct to traditional methods of numerical simulation. Successful and efficient operation of future accelerators may depend on the proper merging of symbolic reasoning and conventional numerical control algorithms.

  1. Analysis of image thresholding segmentation algorithms based on swarm intelligence

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

    2013-03-01

    Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

  2. Non-Newtonian Aspects of Artificial Intelligence

    NASA Astrophysics Data System (ADS)

    Zak, Michail

    2016-05-01

    The challenge of this work is to connect physics with the concept of intelligence. By intelligence we understand a capability to move from disorder to order without external resources, i.e., in violation of the second law of thermodynamics. The objective is to find such a mathematical object described by ODE that possesses such a capability. The proposed approach is based upon modification of the Madelung version of the Schrodinger equation by replacing the force following from quantum potential with non-conservative forces that link to the concept of information. A mathematical formalism suggests that a hypothetical intelligent particle, besides the capability to move against the second law of thermodynamics, acquires such properties like self-image, self-awareness, self-supervision, etc. that are typical for Livings. However since this particle being a quantum-classical hybrid acquires non-Newtonian and non-quantum properties, it does not belong to the physics matter as we know it: the modern physics should be complemented with the concept of the information force that represents a bridge to intelligent particle. As a follow-up of the proposed concept, the following question is addressed: can artificial intelligence (AI) system composed only of physical components compete with a human? The answer is proven to be negative if the AI system is based only on simulations, and positive if digital devices are included. It has been demonstrated that there exists such a quantum neural net that performs simulations combined with digital punctuations. The universality of this quantum-classical hybrid is in capability to violate the second law of thermodynamics by moving from disorder to order without external resources. This advanced capability is illustrated by examples. In conclusion, a mathematical machinery of the perception that is the fundamental part of a cognition process as well as intelligence is introduced and discussed.

  3. A development framework for distributed artificial intelligence

    NASA Technical Reports Server (NTRS)

    Adler, Richard M.; Cottman, Bruce H.

    1989-01-01

    The authors describe distributed artificial intelligence (DAI) applications in which multiple organizations of agents solve multiple domain problems. They then describe work in progress on a DAI system development environment, called SOCIAL, which consists of three primary language-based components. The Knowledge Object Language defines models of knowledge representation and reasoning. The metaCourier language supplies the underlying functionality for interprocess communication and control access across heterogeneous computing environments. The metaAgents language defines models for agent organization coordination, control, and resource management. Application agents and agent organizations will be constructed by combining metaAgents and metaCourier building blocks with task-specific functionality such as diagnostic or planning reasoning. This architecture hides implementation details of communications, control, and integration in distributed processing environments, enabling application developers to concentrate on the design and functionality of the intelligent agents and agent networks themselves.

  4. Artificial intelligence and space power systems automation

    NASA Technical Reports Server (NTRS)

    Weeks, David J.

    1987-01-01

    Various applications of artificial intelligence to space electrical power systems are discussed. An overview is given of completed, on-going, and planned knowledge-based system activities. These applications include the Nickel-Cadmium Battery Expert System (NICBES) (the expert system interfaced with the Hubble Space Telescope electrical power system test bed); the early work with the Space Station Experiment Scheduler (SSES); the three expert systems under development in the space station advanced development effort in the core module power management and distribution system test bed; planned cooperation of expert systems in the Core Module Power Management and Distribution (CM/PMAD) system breadboard with expert systems for the space station at other research centers; and the intelligent data reduction expert system under development.

  5. Beyond Artificial Intelligence toward Engineered Psychology

    NASA Astrophysics Data System (ADS)

    Bozinovski, Stevo; Bozinovska, Liljana

    This paper addresses the field of Artificial Intelligence, road it went so far and possible road it should go. The paper was invited by the Conference of IT Revolutions 2008, and discusses some issues not emphasized in AI trajectory so far. The recommendations are that the main focus should be personalities rather than programs or agents, that genetic environment should be introduced in reasoning about personalities, and that limbic system should be studied and modeled. Engineered Psychology is proposed as a road to go. Need for basic principles in psychology are discussed and a mathematical equation is proposed as fundamental law of engineered and human psychology.

  6. Artificial intelligence applied to process signal analysis

    NASA Technical Reports Server (NTRS)

    Corsberg, Dan

    1988-01-01

    Many space station processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect of the human/machine interface is the analysis and display of process information. Human operators can be overwhelmed by large clusters of alarms that inhibit their ability to diagnose and respond to a disturbance. Using artificial intelligence techniques and a knowledge base approach to this problem, the power of the computer can be used to filter and analyze plant sensor data. This will provide operators with a better description of the process state. Once a process state is recognized, automatic action could be initiated and proper system response monitored.

  7. Probabilistic machine learning and artificial intelligence.

    PubMed

    Ghahramani, Zoubin

    2015-05-28

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  8. Probabilistic machine learning and artificial intelligence.

    PubMed

    Ghahramani, Zoubin

    2015-05-28

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery. PMID:26017444

  9. Probabilistic machine learning and artificial intelligence

    NASA Astrophysics Data System (ADS)

    Ghahramani, Zoubin

    2015-05-01

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  10. Computer science, artificial intelligence, and cybernetics: Applied artificial intelligence in Japan

    SciTech Connect

    Rubinger, B.

    1988-01-01

    This sourcebook provides information on the developments in artificial intelligence originating in Japan. Spanning such innovations as software productivity, natural language processing, CAD, and parallel inference machines, this volume lists leading organizations conducting research or implementing AI systems, describes AI applications being pursued, illustrates current results achieved, and highlights sources reporting progress.

  11. A Progress Report on Artificial Intelligence: Hospital Applications and a Review of the Artificial Intelligence Marketplace

    PubMed Central

    Brenkus, Lawrence M.

    1984-01-01

    Artificial intelligence applications are finally beginning to move from the university research laboratory into commercial use. Before the end of the century, this new computer technology will have profound effects on our work, economy, and lives. At present, relatively few products have appeared in the hospital, but we can anticipate significant product offerings in instrumentation and affecting hospital administration within 5 years.

  12. An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part A: The core ingredients

    NASA Technical Reports Server (NTRS)

    Gevarter, W. B.

    1983-01-01

    Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. The goal of Artificial Intelligence is focused on developing computational approaches to intelligent behavior. This goal is so broad - covering virtually all aspects of human cognitive activity - that substantial confusion has arisen as to the actual nature of AI, its current status and its future capability. This volume, the first in a series of NBS/NASA reports on the subject, attempts to address these concerns. Thus, this report endeavors to clarify what AI is, the foundations on which it rests, the techniques utilized, applications, the participants and, finally, AI's state-of-the-art and future trends. It is anticipated that this report will prove useful to government and private engineering and research managers, potential users, and others who will be affected by this field as it unfolds.

  13. Artificial intelligence. Fears of an AI pioneer.

    PubMed

    Russell, Stuart; Bohannon, John

    2015-07-17

    From the enraged robots in the 1920 play R.U.R. to the homicidal computer H.A.L. in 2001: A Space Odyssey, science fiction writers have embraced the dark side of artificial intelligence (AI) ever since the concept entered our collective imagination. Sluggish progress in AI research, especially during the “AI winter” of the 1970s and 1980s, made such worries seem far-fetched. But recent breakthroughs in machine learning and vast improvements in computational power have brought a flood of research funding— and fresh concerns about where AI may lead us. One researcher now speaking up is Stuart Russell, a computer scientist at the University of California, Berkeley, who with Peter Norvig, director of research at Google, wrote the premier AI textbook, Artificial Intelligence: A Modern Approach, now in its third edition. Last year, Russell joined the Centre for the Study of Existential Risk at Cambridge University in the United Kingdom as an AI expert focusing on “risks that could lead to human extinction.” Among his chief concerns, which he aired at an April meeting in Geneva, Switzerland, run by the United Nations, is the danger of putting military drones and weaponry under the full control of AI systems. This interview has been edited for clarity and brevity.

  14. Artificial Intelligence Research Branch future plans

    NASA Technical Reports Server (NTRS)

    Stewart, Helen (Editor)

    1992-01-01

    This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems.

  15. Artificial intelligence approaches to software engineering

    NASA Technical Reports Server (NTRS)

    Johannes, James D.; Macdonald, James R.

    1988-01-01

    Artificial intelligence approaches to software engineering are examined. The software development life cycle is a sequence of not so well-defined phases. Improved techniques for developing systems have been formulated over the past 15 years, but pressure continues to attempt to reduce current costs. Software development technology seems to be standing still. The primary objective of the knowledge-based approach to software development presented in this paper is to avoid problem areas that lead to schedule slippages, cost overruns, or software products that fall short of their desired goals. Identifying and resolving software problems early, often in the phase in which they first occur, has been shown to contribute significantly to reducing risks in software development. Software development is not a mechanical process but a basic human activity. It requires clear thinking, work, and rework to be successful. The artificial intelligence approaches to software engineering presented support the software development life cycle through the use of software development techniques and methodologies in terms of changing current practices and methods. These should be replaced by better techniques that that improve the process of of software development and the quality of the resulting products. The software development process can be structured into well-defined steps, of which the interfaces are standardized, supported and checked by automated procedures that provide error detection, production of the documentation and ultimately support the actual design of complex programs.

  16. Artificial intelligence. Fears of an AI pioneer.

    PubMed

    Russell, Stuart; Bohannon, John

    2015-07-17

    From the enraged robots in the 1920 play R.U.R. to the homicidal computer H.A.L. in 2001: A Space Odyssey, science fiction writers have embraced the dark side of artificial intelligence (AI) ever since the concept entered our collective imagination. Sluggish progress in AI research, especially during the “AI winter” of the 1970s and 1980s, made such worries seem far-fetched. But recent breakthroughs in machine learning and vast improvements in computational power have brought a flood of research funding— and fresh concerns about where AI may lead us. One researcher now speaking up is Stuart Russell, a computer scientist at the University of California, Berkeley, who with Peter Norvig, director of research at Google, wrote the premier AI textbook, Artificial Intelligence: A Modern Approach, now in its third edition. Last year, Russell joined the Centre for the Study of Existential Risk at Cambridge University in the United Kingdom as an AI expert focusing on “risks that could lead to human extinction.” Among his chief concerns, which he aired at an April meeting in Geneva, Switzerland, run by the United Nations, is the danger of putting military drones and weaponry under the full control of AI systems. This interview has been edited for clarity and brevity. PMID:26185241

  17. An Artificial Intelligence Approach for Gears Diagnostics in AUVs

    PubMed Central

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-01-01

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. PMID:27077868

  18. An Artificial Intelligence Approach for Gears Diagnostics in AUVs.

    PubMed

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-04-12

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

  19. An Artificial Intelligence Approach for Gears Diagnostics in AUVs.

    PubMed

    Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

    2016-01-01

    In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. PMID:27077868

  20. Artificial Intelligence based technique for BTS placement

    NASA Astrophysics Data System (ADS)

    Alenoghena, C. O.; Emagbetere, J. O.; Aibinu, A. M.

    2013-12-01

    The increase of the base transceiver station (BTS) in most urban areas can be traced to the drive by network providers to meet demand for coverage and capacity. In traditional network planning, the final decision of BTS placement is taken by a team of radio planners, this decision is not fool proof against regulatory requirements. In this paper, an intelligent based algorithm for optimal BTS site placement has been proposed. The proposed technique takes into consideration neighbour and regulation considerations objectively while determining cell site. The application will lead to a quantitatively unbiased evaluated decision making process in BTS placement. An experimental data of a 2km by 3km territory was simulated for testing the new algorithm, results obtained show a 100% performance of the neighbour constrained algorithm in BTS placement optimization. Results on the application of GA with neighbourhood constraint indicate that the choices of location can be unbiased and optimization of facility placement for network design can be carried out.

  1. A Research Program on Artificial Intelligence in Process Engineering.

    ERIC Educational Resources Information Center

    Stephanopoulos, George

    1986-01-01

    Discusses the use of artificial intelligence systems in process engineering. Describes a new program at the Massachusetts Institute of Technology which attempts to advance process engineering through technological advances in the areas of artificial intelligence and computers. Identifies the program's hardware facilities, software support,…

  2. The 1993 Goddard Conference on Space Applications of Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Hostetter, Carl F. (Editor)

    1993-01-01

    This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed.

  3. The Potential of Artificial Intelligence in Aids for the Disabled.

    ERIC Educational Resources Information Center

    Boyer, John J.

    The paper explores the possibilities for applying the knowledge of artificial intelligence (AI) research to aids for the disabled. Following a definition of artificial intelligence, the paper reviews areas of basic AI research, such as computer vision, machine learning, and planning and problem solving. Among application areas relevant to the…

  4. The 1994 Goddard Conference on Space Applications of Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Hostetter, Carl F. (Editor)

    1994-01-01

    This publication comprises the papers presented at the 1994 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/GSFC, Greenbelt, Maryland, on 10-12 May 1994. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed.

  5. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    PubMed

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.

  6. Artificial intelligence applications in offshore oil and gas production

    SciTech Connect

    Attia, F.G.; Tawfik, A.S.

    1994-12-31

    The field of Artificial Intelligence (AI) has gained considerable acceptance in virtually all fields, of engineering applications. Artificial intelligence is now being applied in several areas of offshore oil and gas operations, such as drilling, well testing, well logging and interpretation, reservoir engineering, planning and economic evaluation, process control, and risk analysis. Current AI techniques offer a new and exciting technology for solving problems in the oil and gas industry. Expert systems, fuzzy logic systems, neural networks and genetic algorithms are major AI technologies which have made an impact on the petroleum industry. Presently, these technologies are at different stages of maturity with expert systems being the most mature and genetic algorithms the least. However, all four technologies have evolved such that practical applications were produced. This paper describes the four major Al techniques and their many applications in offshore oil and gas production operations. A summary description of future developments in Al technology that will affect the execution and productivity of offshore operations will be also provided.

  7. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    PubMed

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. PMID:27297046

  8. Intelligent perturbation algorithms for space scheduling optimization

    NASA Technical Reports Server (NTRS)

    Kurtzman, Clifford R.

    1991-01-01

    Intelligent perturbation algorithms for space scheduling optimization are presented in the form of the viewgraphs. The following subject areas are covered: optimization of planning, scheduling, and manifesting; searching a discrete configuration space; heuristic algorithms used for optimization; use of heuristic methods on a sample scheduling problem; intelligent perturbation algorithms are iterative refinement techniques; properties of a good iterative search operator; dispatching examples of intelligent perturbation algorithm and perturbation operator attributes; scheduling implementations using intelligent perturbation algorithms; major advances in scheduling capabilities; the prototype ISF (industrial Space Facility) experiment scheduler; optimized schedule (max revenue); multi-variable optimization; Space Station design reference mission scheduling; ISF-TDRSS command scheduling demonstration; and example task - communications check.

  9. The 1990 Goddard Conference on Space Applications of Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Rash, James L. (Editor)

    1990-01-01

    The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

  10. A hybrid artificial bee colony algorithm for numerical function optimization

    NASA Astrophysics Data System (ADS)

    Alqattan, Zakaria N.; Abdullah, Rosni

    2015-02-01

    Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

  11. Learning Intelligent Genetic Algorithms Using Japanese Nonograms

    ERIC Educational Resources Information Center

    Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

    2012-01-01

    An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

  12. Artificial Intelligence In Computational Fluid Dynamics

    NASA Technical Reports Server (NTRS)

    Vogel, Alison Andrews

    1991-01-01

    Paper compares four first-generation artificial-intelligence (Al) software systems for computational fluid dynamics. Includes: Expert Cooling Fan Design System (EXFAN), PAN AIR Knowledge System (PAKS), grid-adaptation program MITOSIS, and Expert Zonal Grid Generation (EZGrid). Focuses on knowledge-based ("expert") software systems. Analyzes intended tasks, kinds of knowledge possessed, magnitude of effort required to codify knowledge, how quickly constructed, performances, and return on investment. On basis of comparison, concludes Al most successful when applied to well-formulated problems solved by classifying or selecting preenumerated solutions. In contrast, application of Al to poorly understood or poorly formulated problems generally results in long development time and large investment of effort, with no guarantee of success.

  13. Artificial intelligence in the materials processing laboratory

    NASA Technical Reports Server (NTRS)

    Workman, Gary L.; Kaukler, William F.

    1990-01-01

    Materials science and engineering provides a vast arena for applications of artificial intelligence. Advanced materials research is an area in which challenging requirements confront the researcher, from the drawing board through production and into service. Advanced techniques results in the development of new materials for specialized applications. Hand-in-hand with these new materials are also requirements for state-of-the-art inspection methods to determine the integrity or fitness for service of structures fabricated from these materials. Two problems of current interest to the Materials Processing Laboratory at UAH are an expert system to assist in eddy current inspection of graphite epoxy components for aerospace and an expert system to assist in the design of superalloys for high temperature applications. Each project requires a different approach to reach the defined goals. Results to date are described for the eddy current analysis, but only the original concepts and approaches considered are given for the expert system to design superalloys.

  14. Artificial Intelligence Software Engineering (AISE) model

    NASA Technical Reports Server (NTRS)

    Kiss, Peter A.

    1990-01-01

    The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

  15. An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part B: Applications

    NASA Technical Reports Server (NTRS)

    Gevarter, W. B.

    1983-01-01

    Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. This report, Part B of a three part report on AI, presents overviews of the key application areas: Expert Systems, Computer Vision, Natural Language Processing, Speech Interfaces, and Problem Solving and Planning. The basic approaches to such systems, the state-of-the-art, existing systems and future trends and expectations are covered.

  16. Parameter tuning of PVD process based on artificial intelligence technique

    NASA Astrophysics Data System (ADS)

    Norlina, M. S.; Diyana, M. S. Nor; Mazidah, P.; Rusop, M.

    2016-07-01

    In this study, an artificial intelligence technique is proposed to be implemented in the parameter tuning of a PVD process. Due to its previous adaptation in similar optimization problems, genetic algorithm (GA) is selected to optimize the parameter tuning of the RF magnetron sputtering process. The most optimized parameter combination obtained from GA's optimization result is expected to produce the desirable zinc oxide (ZnO) thin film from the sputtering process. The parameters involved in this study were RF power, deposition time and substrate temperature. The algorithm was tested to optimize the 25 datasets of parameter combinations. The results from the computational experiment were then compared with the actual result from the laboratory experiment. Based on the comparison, GA had shown that the algorithm was reliable to optimize the parameter combination before the parameter tuning could be done to the RF magnetron sputtering machine. In order to verify the result of GA, the algorithm was also been compared to other well known optimization algorithms, which were, particle swarm optimization (PSO) and gravitational search algorithm (GSA). The results had shown that GA was reliable in solving this RF magnetron sputtering process parameter tuning problem. GA had shown better accuracy in the optimization based on the fitness evaluation.

  17. Amplify scientific discovery with artificial intelligence

    SciTech Connect

    Gil, Yolanda; Greaves, Mark T.; Hendler, James; Hirsch, Hyam

    2014-10-10

    Computing innovations have fundamentally changed many aspects of scientific inquiry. For example, advances in robotics, high-end computing, networking, and databases now underlie much of what we do in science such as gene sequencing, general number crunching, sharing information between scientists, and analyzing large amounts of data. As computing has evolved at a rapid pace, so too has its impact in science, with the most recent computing innovations repeatedly being brought to bear to facilitate new forms of inquiry. Recently, advances in Artificial Intelligence (AI) have deeply penetrated many consumer sectors, including for example Apple’s Siri™ speech recognition system, real-time automated language translation services, and a new generation of self-driving cars and self-navigating drones. However, AI has yet to achieve comparable levels of penetration in scientific inquiry, despite its tremendous potential in aiding computers to help scientists tackle tasks that require scientific reasoning. We contend that advances in AI will transform the practice of science as we are increasingly able to effectively and jointly harness human and machine intelligence in the pursuit of major scientific challenges.

  18. Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

    PubMed

    Chen, Tinggui; Xiao, Renbin

    2014-01-01

    Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

  19. Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

    PubMed Central

    Chen, Tinggui; Xiao, Renbin

    2014-01-01

    Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

  20. Improved artificial bee colony algorithm based gravity matching navigation method.

    PubMed

    Gao, Wei; Zhao, Bo; Zhou, Guang Tao; Wang, Qiu Ying; Yu, Chun Yang

    2014-07-18

    Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.

  1. Algorithmic approach to intelligent robot mobility

    SciTech Connect

    Kauffman, S.

    1983-05-01

    This paper presents Sutherland's algorithm, plus an alternative algorithm, which allows mobile robots to move about intelligently in environments resembling the rooms and hallways in which we move around. The main hardware requirements for a robot to use the algorithms presented are mobility and an ability to sense distances with some type of non-contact scanning device. This article does not discuss the actual robot construction. The emphasis is on heuristics and algorithms. 1 reference.

  2. Artificial Intelligent Controller for a DC Motor

    NASA Astrophysics Data System (ADS)

    Delavari, Hadi; Ranjbar Noiey, Abolzafl; Minagar, Sara

    The Speed and position control of DC motors is addressed in this paper. An optimal intelligent control scheme is proposed for the system. Preliminary a PID controller is designed using Genetic Algorithms (GA). The proposed controller is implemented by using optimal integral state feedback control with GA and Kalman filter. In the proposed scheme, performance depends on choosing weighting matrices Q and R in the cost function, and accordingly GA is used to find these proper weighting matrices. In order to reduce the control performance degradation due to system parameters variation, a Kalman filter is gained. The performance of the proposed technique (ISF) is compared with PID controller. Computer simulation validates the effectiveness of the proposed scheme even in presence of uncertainties.

  3. Intelligent Tutoring System: A Tool for Testing the Research Curiosities of Artificial Intelligence Researchers

    ERIC Educational Resources Information Center

    Yaratan, Huseyin

    2003-01-01

    An ITS (Intelligent Tutoring System) is a teaching-learning medium that uses artificial intelligence (AI) technology for instruction. Roberts and Park (1983) defines AI as the attempt to get computers to perform tasks that if performed by a human-being, intelligence would be required to perform the task. The design of an ITS comprises two distinct…

  4. Three Years of Using Robots in an Artificial Intelligence Course: Lessons Learned

    ERIC Educational Resources Information Center

    Kumar, Amruth N.

    2004-01-01

    We have been using robots in our artificial intelligence course since fall 2000. We have been using the robots for open-laboratory projects. The projects are designed to emphasize high-level knowledge-based AI algorithms. After three offerings of the course, we paused to analyze the collected data and to see if we could answer the following…

  5. A Client-Server Computational Tool for Integrated Artificial Intelligence Curriculum.

    ERIC Educational Resources Information Center

    Holder, Lawrence B.; Cook, Diane J.

    2001-01-01

    Describes the development of a Web-based multimedia delivery method of increasing students' interest in artificial intelligence (AI). The course material features an integrated simulation environment that allows students to develop and test AI algorithms in a dynamic and uncertain visual environment. Evaluated the effect of the simulation on the…

  6. Third Conference on Artificial Intelligence for Space Applications, part 2

    NASA Technical Reports Server (NTRS)

    Denton, Judith S. (Compiler); Freeman, Michael S. (Compiler); Vereen, Mary (Compiler)

    1988-01-01

    Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed.

  7. Automation of neutral beam source conditioning with artificial intelligence techniques

    SciTech Connect

    Johnson, R.R.; Canales, T.W.; Lager, D.L.

    1985-01-01

    This paper describes a system that automates neutral beam source conditioning. The system achieves this with artificial intelligence techniques. The architecture of the system is presented followed by a description of its performance.

  8. Third Conference on Artificial Intelligence for Space Applications, part 1

    NASA Technical Reports Server (NTRS)

    Denton, Judith S. (Compiler); Freeman, Michael S. (Compiler); Vereen, Mary (Compiler)

    1987-01-01

    The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed.

  9. Artificial intelligence methods applied in the controlled synthesis of polydimethilsiloxane - poly (methacrylic acid) copolymer networks with imposed properties

    NASA Astrophysics Data System (ADS)

    Rusu, Teodora; Gogan, Oana Marilena

    2016-05-01

    This paper describes the use of artificial intelligence method in copolymer networks design. In the present study, we pursue a hybrid algorithm composed from two research themes in the genetic design framework: a Kohonen neural network (KNN), path (forward problem) combined with a genetic algorithm path (backward problem). The Tabu Search Method is used to improve the performance of the genetic algorithm path.

  10. N-person game playing and artificial intelligence

    SciTech Connect

    Luckhardt, C.A.

    1989-01-01

    Game playing in artificial intelligence (AI) has produced effective algorithms enabling a computer to play two-person, non-cooperative, zero-sum and perfect information games such as checkers and chess. Game theory suggests solution sets for games, but does not shed much light on how to make moves in a game. This dissertation couples AI with game theory and determines ways for a computer to play multiplayer games. The max{sup n} algorithm is defined and analyzed for playing non-cooperative, n-person games. The max{sup n} procedure finds an equilibrium point for a game and allows some pruning of calculated payoff values but not pruning of subtrees. An evaluation for representing a cooperative game is defined using the max{sup n} procedure on all possible coalitions. This evaluation is used along with an earnings function in order to define the stability of a coalition and coalition structure. Finally, a solution algorithm is developed that is based on coalition stability for a computer to use in playing cooperative n-person games using look ahead, heuristic evaluation function, and back-up techniques. The solution algorithm gives a result that is sometimes in game-theoretic solution sets such as the core, stable set, kernel, and bargaining set. Potential applications for this work are in AI, conflict resolution, economics, mathematics, and social psychology.

  11. Artificial intelligence technologies applied to terrain analysis

    SciTech Connect

    Wright, J.C. ); Powell, D.R. )

    1990-01-01

    The US Army Training and Doctrine Command is currently developing, in cooperation with Los Alamos National Laboratory, a Corps level combat simulation to support military analytical studies. This model emphasizes high resolution modeling of the command and control processes, with particular attention to architectural considerations that enable extension of the model. A planned future extension is the inclusion of an computer based planning capability for command echelons that can be dynamical invoked during the execution of then model. Command and control is the process through which the activities of military forces are directed, coordinated, and controlled to achieve the stated mission. To perform command and control the commander must understand the mission, perform terrain analysis, understand his own situation and capabilities as well as the enemy situation and his probable actions. To support computer based planning, data structures must be available to support the computer's ability to understand'' the mission, terrain, own capabilities, and enemy situation. The availability of digitized terrain makes it feasible to apply artificial intelligence technologies to emulate the terrain analysis process, producing data structures for uses in planning. The work derived thus for to support the understanding of terrain is the topic of this paper. 13 refs., 5 figs., 6 tabs.

  12. Vibration energy harvester optimization using artificial intelligence

    NASA Astrophysics Data System (ADS)

    Hadas, Z.; Ondrusek, C.; Kurfurst, J.; Singule, V.

    2011-06-01

    This paper deals with an optimization study of a vibration energy harvester. This harvester can be used as autonomous source of electrical energy for remote or wireless applications, which are placed in environment excited by ambient mechanical vibrations. The ambient energy of vibrations is usually on very low level but the harvester can be used as alternative source of energy for electronic devices with an expected low level of power consumption of several mW. The optimized design of the vibration energy harvester was based on previous development and the sensitivity of harvester design was improved for effective harvesting from mechanical vibrations in aeronautic applications. The vibration energy harvester is a mechatronic system which generates electrical energy from ambient vibrations due to precision tuning up generator parameters. The optimization study for maximization of harvested power or minimization of volume and weight are the main goals of our development. The optimization study of such complex device is complicated therefore artificial intelligence methods can be used for tuning up optimal harvester parameters.

  13. Using artificial intelligence to automate remittance processing.

    PubMed

    Adams, W T; Snow, G M; Helmick, P M

    1998-06-01

    The consolidated business office of the Allegheny Health Education Research Foundation (AHERF), a large integrated healthcare system based in Pittsburgh, Pennsylvania, sought to improve its cash-related business office activities by implementing an automated remittance processing system that uses artificial intelligence. The goal was to create a completely automated system whereby all monies it processed would be tracked, automatically posted, analyzed, monitored, controlled, and reconciled through a central database. Using a phased approach, the automated payment system has become the central repository for all of the remittances for seven of the hospitals in the AHERF system and has allowed for the complete integration of these hospitals' existing billing systems, document imaging system, and intranet, as well as the new automated payment posting, and electronic cash tracking and reconciling systems. For such new technology, which is designed to bring about major change, factors contributing to the project's success were adequate planning, clearly articulated objectives, marketing, end-user acceptance, and post-implementation plan revision. PMID:10179973

  14. Training Software in Artificial-Intelligence Computing Techniques

    NASA Technical Reports Server (NTRS)

    Howard, Ayanna; Rogstad, Eric; Chalfant, Eugene

    2005-01-01

    The Artificial Intelligence (AI) Toolkit is a computer program for training scientists, engineers, and university students in three soft-computing techniques (fuzzy logic, neural networks, and genetic algorithms) used in artificial-intelligence applications. The program promotes an easily understandable tutorial interface, including an interactive graphical component through which the user can gain hands-on experience in soft-computing techniques applied to realistic example problems. The tutorial provides step-by-step instructions on the workings of soft-computing technology, whereas the hands-on examples allow interaction and reinforcement of the techniques explained throughout the tutorial. In the fuzzy-logic example, a user can interact with a robot and an obstacle course to verify how fuzzy logic is used to command a rover traverse from an arbitrary start to the goal location. For the genetic algorithm example, the problem is to determine the minimum-length path for visiting a user-chosen set of planets in the solar system. For the neural-network example, the problem is to decide, on the basis of input data on physical characteristics, whether a person is a man, woman, or child. The AI Toolkit is compatible with the Windows 95,98, ME, NT 4.0, 2000, and XP operating systems. A computer having a processor speed of at least 300 MHz, and random-access memory of at least 56MB is recommended for optimal performance. The program can be run on a slower computer having less memory, but some functions may not be executed properly.

  15. Interactive Video and Artificial Intelligence: A Convenient Marriage.

    ERIC Educational Resources Information Center

    Midoro, V.; And Others

    1988-01-01

    Describes the theoretical framework of a research project aimed at exploring the new potentials for instructional systems offered by videodisc technology and artificial intelligence. A prototype of an intelligent tutoring system, "Earth," is described, and types of interactions in instructional systems are discussed as they relate to the learning…

  16. A solution quality assessment method for swarm intelligence optimization algorithms.

    PubMed

    Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

    2014-01-01

    Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.

  17. Conference on Artificial Intelligence for Applications, 8th, Monterey, CA, Mar. 2-6, 1992, Proceedings

    SciTech Connect

    Not Available

    1992-01-01

    The papers contained in this volume focus on various artificial intelligence applications, with attention given to integrating rule-base systems, enabling technologies, knowledge acquisition and learning, and intelligent interfaces and natural language processing. Other topics discussed include planning, scheduling, and constraint satisfaction; case-based reasoning, genetic algorithms, and fuzzy logic; design; and distributed problem solving and real-time systems. Papers are presented on representation and control of knowledge bases for support of multiple tasks, a multilevel diagnosis methodology for complex systems, intelligent assistance for the communication of information in large organizations, and qualitative/fuzzy approach to document recognition.

  18. Application of Artificial Intelligence Techniques in Uninhabited Aerial Vehicle Flight

    NASA Technical Reports Server (NTRS)

    Dufrene, Warren R., Jr.

    2004-01-01

    This paper describes the development of an application of Artificial Intelligence (AI) for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in AI at NOVA Southeastearn University and a beginning project at NASA Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an Artificial Intelligence method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed.

  19. Application of Artificial Intelligence Techniques in Uninhabitated Aerial Vehicle Flight

    NASA Technical Reports Server (NTRS)

    Dufrene, Warren R., Jr.

    2003-01-01

    This paper describes the development of an application of Artificial Intelligence (AI) for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in AI at NOVA southeastern University and a beginning project at NASA Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an Artificial Intelligence method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed.

  20. Artificial intelligence and nuclear power. Report by the Technology Transfer Artificial Intelligence Task Team

    SciTech Connect

    Not Available

    1985-06-01

    The Artificial Intelligence Task Team was organized to review the status of Artificial Intelligence (AI) technology, identify guidelines for AI work, and to identify work required to allow the nuclear industry to realize maximum benefit from this technology. The state of the nuclear industry was analyzed to determine where the application of AI technology could be of greatest benefit. Guidelines and criteria were established to focus on those particular problem areas where AI could provide the highest possible payoff to the industry. Information was collected from government, academic, and private organizations. Very little AI work is now being done to specifically support the nuclear industry. The AI Task Team determined that the establishment of a Strategic Automation Initiative (SAI) and the expansion of the DOE Technology Transfer program would ensure that AI technology could be used to develop software for the nuclear industry that would have substantial financial payoff to the industry. The SAI includes both long and short term phases. The short-term phase includes projects which would demonstrate that AI can be applied to the nuclear industry safely, and with substantial financial benefit. The long term phase includes projects which would develop AI technologies with specific applicability to the nuclear industry that would not be developed by people working in any other industry.

  1. Applications of artificial intelligence to space station: General purpose intelligent sensor interface

    NASA Technical Reports Server (NTRS)

    Mckee, James W.

    1988-01-01

    This final report describes the accomplishments of the General Purpose Intelligent Sensor Interface task of the Applications of Artificial Intelligence to Space Station grant for the period from October 1, 1987 through September 30, 1988. Portions of the First Biannual Report not revised will not be included but only referenced. The goal is to develop an intelligent sensor system that will simplify the design and development of expert systems using sensors of the physical phenomena as a source of data. This research will concentrate on the integration of image processing sensors and voice processing sensors with a computer designed for expert system development. The result of this research will be the design and documentation of a system in which the user will not need to be an expert in such areas as image processing algorithms, local area networks, image processor hardware selection or interfacing, television camera selection, voice recognition hardware selection, or analog signal processing. The user will be able to access data from video or voice sensors through standard LISP statements without any need to know about the sensor hardware or software.

  2. Intelligent perturbation algorithms to space scheduling optimization

    NASA Technical Reports Server (NTRS)

    Kurtzman, Clifford R.

    1991-01-01

    The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed.

  3. Neural networks: A versatile tool from artificial intelligence

    SciTech Connect

    Yama, B.R.; Lineberry, G.T.

    1996-12-31

    Artificial Intelligence research has produced several tools for commercial application in recent years. Artificial Neural Networks (ANNs), Fuzzy Logic, and Expert Systems are some of the techniques that are widely used today in various fields of engineering and business. Among these techniques, ANNs are gaining popularity due to their learning and other brain-like capabilities. Within the mining industry, ANN technology is being utilized with large payoffs for real-time process control applications. In this paper, a brief introduction to ANNs and the associated terminology is given. The neural network development process is outlined, followed by the back-propagation learning algorithm. Next, the development of two multi-layer, feed-forward neural networks is described and the results axe presented. One network is developed for prediction of strength of intact rock specimens, and another network is developed for prediction of mineral concentrations. Preliminary results indicate a predictive error less than 10% using cross-validation on a limited data set. The performance of the neural network for prediction of mineral concentrations was compared with kriging. It was found that the neural network performed not only satisfactorily, but in some cases performed better than, the kriging model.

  4. Application of the artificial bee colony algorithm for solving the set covering problem.

    PubMed

    Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

    2014-01-01

    The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem. PMID:24883356

  5. Space Communications Artificial Intelligence for Link Evaluation Terminal (SCAILET)

    NASA Technical Reports Server (NTRS)

    Shahidi, Anoosh

    1991-01-01

    A software application to assis end-users of the Link Evaluation Terminal (LET) for satellite communication is being developed. This software application incorporates artificial intelligence (AI) techniques and will be deployed as an interface to LET. The high burst rate (HBR) LET provides 30 GHz transmitting/20 GHz receiving, 220/110 Mbps capability for wideband communications technology experiments with the Advanced Communications Technology Satellite (ACTS). The HBR LET and ACTS are being developed at the NASA Lewis Research Center. The HBR LET can monitor and evaluate the integrity of the HBR communications uplink and downlink to the ACTS satellite. The uplink HBR transmission is performed by bursting the bit-pattern as a modulated signal to the satellite. By comparing the transmitted bit pattern with the received bit pattern, HBR LET can determine the bit error rate BER) under various atmospheric conditions. An algorithm for power augmentation is applied to enhance the system's BER performance at reduced signal strength caused by adverse conditions. Programming scripts, defined by the design engineer, set up the HBR LET terminal by programming subsystem devices through IEEE488 interfaces. However, the scripts are difficult to use, require a steep learning curve, are cryptic, and are hard to maintain. The combination of the learning curve and the complexities involved with editing the script files may discourage end-users from utilizing the full capabilities of the HBR LET system. An intelligent assistant component of SCAILET that addresses critical end-user needs in the programming of the HBR LET system as anticipated by its developers is described. A close look is taken at the various steps involved in writing ECM software for a C&P, computer and at how the intelligent assistant improves the HBR LET system and enhances the end-user's ability to perform the experiments.

  6. Artificial intelligence for the CTA Observatory scheduler

    NASA Astrophysics Data System (ADS)

    Colomé, Josep; Colomer, Pau; Campreciós, Jordi; Coiffard, Thierry; de Oña, Emma; Pedaletti, Giovanna; Torres, Diego F.; Garcia-Piquer, Alvaro

    2014-08-01

    The Cherenkov Telescope Array (CTA) project will be the next generation ground-based very high energy gamma-ray instrument. The success of the precursor projects (i.e., HESS, MAGIC, VERITAS) motivated the construction of this large infrastructure that is included in the roadmap of the ESFRI projects since 2008. CTA is planned to start the construction phase in 2015 and will consist of two arrays of Cherenkov telescopes operated as a proposal-driven open observatory. Two sites are foreseen at the southern and northern hemispheres. The CTA observatory will handle several observation modes and will have to operate tens of telescopes with a highly efficient and reliable control. Thus, the CTA planning tool is a key element in the control layer for the optimization of the observatory time. The main purpose of the scheduler for CTA is the allocation of multiple tasks to one single array or to multiple sub-arrays of telescopes, while maximizing the scientific return of the facility and minimizing the operational costs. The scheduler considers long- and short-term varying conditions to optimize the prioritization of tasks. A short-term scheduler provides the system with the capability to adapt, in almost real-time, the selected task to the varying execution constraints (i.e., Targets of Opportunity, health or status of the system components, environment conditions). The scheduling procedure ensures that long-term planning decisions are correctly transferred to the short-term prioritization process for a suitable selection of the next task to execute on the array. In this contribution we present the constraints to CTA task scheduling that helped classifying it as a Flexible Job-Shop Problem case and finding its optimal solution based on Artificial Intelligence techniques. We describe the scheduler prototype that uses a Guarded Discrete Stochastic Neural Network (GDSN), for an easy representation of the possible long- and short-term planning solutions, and Constraint

  7. Artificial intelligence and robot responsibilities: innovating beyond rights.

    PubMed

    Ashrafian, Hutan

    2015-04-01

    The enduring innovations in artificial intelligence and robotics offer the promised capacity of computer consciousness, sentience and rationality. The development of these advanced technologies have been considered to merit rights, however these can only be ascribed in the context of commensurate responsibilities and duties. This represents the discernable next-step for evolution in this field. Addressing these needs requires attention to the philosophical perspectives of moral responsibility for artificial intelligence and robotics. A contrast to the moral status of animals may be considered. At a practical level, the attainment of responsibilities by artificial intelligence and robots can benefit from the established responsibilities and duties of human society, as their subsistence exists within this domain. These responsibilities can be further interpreted and crystalized through legal principles, many of which have been conserved from ancient Roman law. The ultimate and unified goal of stipulating these responsibilities resides through the advancement of mankind and the enduring preservation of the core tenets of humanity.

  8. The coming of age of artificial intelligence in medicine.

    PubMed

    Patel, Vimla L; Shortliffe, Edward H; Stefanelli, Mario; Szolovits, Peter; Berthold, Michael R; Bellazzi, Riccardo; Abu-Hanna, Ameen

    2009-05-01

    This paper is based on a panel discussion held at the Artificial Intelligence in Medicine Europe (AIME) conference in Amsterdam, The Netherlands, in July 2007. It had been more than 15 years since Edward Shortliffe gave a talk at AIME in which he characterized artificial intelligence (AI) in medicine as being in its "adolescence" (Shortliffe EH. The adolescence of AI in medicine: will the field come of age in the '90s? Artificial Intelligence in Medicine 1993;5:93-106). In this article, the discussants reflect on medical AI research during the subsequent years and characterize the maturity and influence that has been achieved to date. Participants focus on their personal areas of expertise, ranging from clinical decision-making, reasoning under uncertainty, and knowledge representation to systems integration, translational bioinformatics, and cognitive issues in both the modeling of expertise and the creation of acceptable systems.

  9. A review of European applications of artificial intelligence to space

    NASA Technical Reports Server (NTRS)

    Drummond, Mark (Editor); Stewart, Helen (Editor)

    1993-01-01

    The purpose is to describe the applications of Artificial Intelligence (AI) to the European Space program that are being developed or have been developed. The results of a study sponsored by the Artificial Intelligence Research and Development program of NASA's Office of Advanced Concepts and Technology (OACT) are described. The report is divided into two sections. The first consists of site reports, which are descriptions of the AI applications seen at each place visited. The second section consists of two summaries which synthesize the information in the site reports by organizing this information in two different ways. The first organizes the material in terms of the type of application, e.g., data analysis, planning and scheduling, and procedure management. The second organizes the material in terms of the component technologies of Artificial Intelligence which the applications used, e.g., knowledge based systems, model based reasoning, procedural reasoning, etc.

  10. Robotics and artificial intelligence across the Atlantic and Pacific

    SciTech Connect

    Schlussel, K.

    1983-08-01

    World research and develoments in robotics and artificial intelligence have received significant attention in the past few years. While the United States has both an active and advanced program in robotics and artificial intelligence, other countries have been working and organizing their activities and are starting to produce some interesting results. The paper presents some highlights of foreign efforts in robotics and artificial intelligence research and development. It is not the purpose of this paper to be all inclusive of every foreign robotics program, but to give a sample of selective developments and briefly examine the international cooperation of many programs. Projects in western Europe, Japan, and the eastern bloc countries are discussed. 31 references.

  11. The Coming of Age of Artificial Intelligence in Medicine*

    PubMed Central

    Patel, Vimla L.; Shortliffe, Edward H.; Stefanelli, Mario; Szolovits, Peter; Berthold, Michael R.; Bellazzi, Riccardo; Abu-Hanna, Ameen

    2009-01-01

    Summary This paper is based on a panel discussion held at the Artificial Intelligence in Medicine Europe (AIME) conference in Amsterdam, The Netherlands, in July 2007. It had been more than 15 years since Edward Shortliffe gave a talk at AIME in which he characterized artificial intelligence (AI) in medicine as being in its “adolescence” (Shortliffe EH. The adolescence of AI in medicine: Will the field come of age in the ‘90s? Artificial Intelligence in Medicine 1993; 5:93–106). In this article, the discussants reflect on medical AI research during the subsequent years and attempt to characterize the maturity and influence that has been achieved to date. Participants focus on their personal areas of expertise, ranging from clinical decision making, reasoning under uncertainty, and knowledge representation to systems integration, translational bioinformatics, and cognitive issues in both the modeling of expertise and the creation of acceptable systems. PMID:18790621

  12. Fifth Conference on Artificial Intelligence for Space Applications

    NASA Technical Reports Server (NTRS)

    Odell, Steve L. (Compiler)

    1990-01-01

    The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration.

  13. Bionics: A Step toward Artificial Intelligence Systems

    ERIC Educational Resources Information Center

    Dutton, Robert E.

    1970-01-01

    Recent developments and future prospects in the borrowing of biological principles to build problem solving relationships between human intelligence and the information storage and manipulation capacities of computers. Twenty-one references. (LY)

  14. The 1988 Goddard Conference on Space Applications of Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Rash, James (Editor); Hughes, Peter (Editor)

    1988-01-01

    This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies.

  15. Computers for artificial intelligence a technology assessment and forecast

    SciTech Connect

    Miller, R.K.

    1986-01-01

    This study reviews the development and current state-of-the-art in computers for artificial intelligence, including LISP machines, AI workstations, professional and engineering workstations, minicomputers, mainframes, and supercomputers. Major computer systems for AI applications are reviewed. The use of personal computers for expert system development is discussed, and AI software for the IBM PC, Texas Instrument Professional Computer, and Apple MacIntosh is presented. Current research aimed at developing a new computer for artificial intelligence is described, and future technological developments are discussed.

  16. Two-Dimensional Grammars And Their Applications To Artificial Intelligence

    NASA Astrophysics Data System (ADS)

    Lee, Edward T.

    1987-05-01

    During the past several years, the concepts and techniques of two-dimensional grammars1,2 have attracted growing attention as promising avenues of approach to problems in picture generation as well as in picture description3 representation, recognition, transformation and manipulation. Two-dimensional grammar techniques serve the purpose of exploiting the structure or underlying relationships in a picture. This approach attempts to describe a complex picture in terms of their components and their relative positions. This resembles the way a sentence is described in terms of its words and phrases, and the terms structural picture recognition, linguistic picture recognition, or syntactic picture recognition are often used. By using this approach, the problem of picture recognition becomes similar to that of phrase recognition in a language. However, describing pictures using a string grammar (one-dimensional grammar), the only relation between sub-pictures and/or primitives is the concatenation; that is each picture or primitive can be connected only at the left or right. This one-dimensional relation has not been very effective in describing two-dimensional pictures. A natural generaliza-tion is to use two-dimensional grammars. In this paper, two-dimensional grammars and their applications to artificial intelligence are presented. Picture grammars and two-dimensional grammars are introduced and illustrated by examples. In particular, two-dimensional grammars for generating all possible squares and all possible rhombuses are presented. The applications of two-dimensional grammars to solving region filling problems are discussed. An algorithm for region filling using two-dimensional grammars is presented together with illustrative examples. The advantages of using this algorithm in terms of computation time are also stated. A high-level description of a two-level picture generation system is proposed. The first level is the picture primitive generation using two

  17. Use of artificial intelligence in supervisory control

    NASA Technical Reports Server (NTRS)

    Cohen, Aaron; Erickson, Jon D.

    1989-01-01

    Viewgraphs describing the design and testing of an intelligent decision support system called OFMspert are presented. In this expert system, knowledge about the human operator is represented through an operator/system model referred to as the OFM (Operator Function Model). OFMspert uses the blackboard model of problem solving to maintain a dynamic representation of operator goals, plans, tasks, and actions given previous operator actions and current system state. Results of an experiment to assess OFMspert's intent inferencing capability are outlined. Finally, the overall design philosophy for an intelligent tutoring system (OFMTutor) for operators of complex dynamic systems is summarized.

  18. Applications of artificial intelligence IV; Proceedings of the Meeting, Innsbruck, Austria, Apr. 15, 16, 1986

    SciTech Connect

    Gilmore, J.F.

    1986-01-01

    An expert system for roving robotics; hybrid expert systems in image analysis; the use of PROLOG in automatic speech recognition; a knowledge-based geological prospecting system; the use of AI technology in failsafe real-time systems; computer vision for artificially intelligent robotic systems; and a knowledge representation system for searching trajectories in robotics are examined. Attention is given to using contextual data in classification algorithms; real-time intelligent hardware-based image processing; a parallel intelligent system; a LISP machine; a graphical display intelligence software based on a raster scan; and the extraction of uniform regions with minimization of noise points effects. Topics discussed include an active coordinate imaging system for robot vision; the design of a smart sensor system for real-time remote sensing image processing on-board a satellite; texture defects and visual inspection; the three-dimensional modeling of industrial parts for image analysis; and a new type of cellular turing acceptor.

  19. Artificial evolution: a new path for artificial intelligence?

    PubMed

    Husbands, P; Harvey, I; Cliff, D; Miller, G

    1997-06-01

    Recently there have been a number of proposals for the use of artificial evolution as a radically new approach to the development of control systems for autonomous robots. This paper explains the artificial evolution approach, using work at Sussex to illustrate it. The paper revolves around a case study on the concurrent evolution of control networks and visual sensor morphologies for a mobile robot. Wider intellectual issues surrounding the work are discussed, as is the use of more abstract evolutionary simulations as a new potentially useful tool in theoretical biology. PMID:9209759

  20. Artificial Intelligence Approaches to Computer-Based Instruction.

    ERIC Educational Resources Information Center

    Bregar, William S.; Farley, Arthur M.

    1980-01-01

    Explores how new, operational models of cognition processing developed in Artificial Intelligence (AI) can be applied in computer assisted instruction (CAI) systems. CAI systems are surveyed in terms of their goals and formalisms, and a model for the development of a tutorial CAI system for algebra problem solving is introduced. (Author)

  1. Evolution and Revolution in Artificial Intelligence in Education

    ERIC Educational Resources Information Center

    Roll, Ido; Wylie, Ruth

    2016-01-01

    The field of Artificial Intelligence in Education (AIED) has undergone significant developments over the last twenty-five years. As we reflect on our past and shape our future, we ask two main questions: What are our major strengths? And, what new opportunities lay on the horizon? We analyse 47 papers from three years in the history of the…

  2. Artificial Intelligence in Business: Technocrat Jargon or Quantum Leap?

    ERIC Educational Resources Information Center

    Burford, Anna M.; Wilson, Harold O.

    This paper addresses the characteristics and applications of artificial intelligence (AI) as a subsection of computer science, and briefly describes the most common types of AI programs: expert systems, natural language, and neural networks. Following a brief presentation of the historical background, the discussion turns to an explanation of how…

  3. Second Conference on Artificial Intelligence for Space Applications

    NASA Technical Reports Server (NTRS)

    Dollman, Thomas (Compiler)

    1988-01-01

    The proceedings of the conference are presented. This second conference on Artificial Intelligence for Space Applications brings together a diversity of scientific and engineering work and is intended to provide an opportunity for those who employ AI methods in space applications to identify common goals and to discuss issues of general interest in the AI community.

  4. The 1992 Goddard Conference on Space Applications of Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Rash, James L. (Editor)

    1992-01-01

    The purpose of this conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers fall into the following areas: planning and scheduling, control, fault monitoring/diagnosis and recovery, information management, tools, neural networks, and miscellaneous applications.

  5. Artificial Intelligence and the High School Computer Curriculum.

    ERIC Educational Resources Information Center

    Dillon, Richard W.

    1993-01-01

    Describes a four-part curriculum that can serve as a model for incorporating artificial intelligence (AI) into the high school computer curriculum. The model includes examining questions fundamental to AI, creating and designing an expert system, language processing, and creating programs that integrate machine vision with robotics and…

  6. Robotics, Artificial Intelligence, Computer Simulation: Future Applications in Special Education.

    ERIC Educational Resources Information Center

    Moore, Gwendolyn B.; And Others

    1986-01-01

    Describes possible applications of new technologies to special education. Discusses results of a study designed to explore the use of robotics, artificial intelligence, and computer simulations to aid people with handicapping conditions. Presents several scenarios in which specific technological advances may contribute to special education…

  7. Human Factors and Computer Interfaces--Implications for Artificial Intelligence.

    ERIC Educational Resources Information Center

    Norris, Cathleen A.

    1987-01-01

    This second in a series of articles on artificial intelligence emphasizes human factors. The design of video display units and keyboards is discussed, the organizational structure of human memory is described, humans are examined as information processors using inductive and deductive reasoning, and educational implications are explored. (LRW)

  8. Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

    ERIC Educational Resources Information Center

    Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

    2003-01-01

    Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

  9. An Artificial Intelligence-Based Distance Education System: Artimat

    ERIC Educational Resources Information Center

    Nabiyev, Vasif; Karal, Hasan; Arslan, Selahattin; Erumit, Ali Kursat; Cebi, Ayca

    2013-01-01

    The purpose of this study is to evaluate the artificial intelligence-based distance education system called ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed…

  10. Artificial Intelligence: Is the Future Now for A.I.?

    ERIC Educational Resources Information Center

    Ramaswami, Rama

    2009-01-01

    In education, artificial intelligence (AI) has not made much headway. In the one area where it would seem poised to lend the most benefit--assessment--the reliance on standardized tests, intensified by the demands of the No Child Left Behind Act of 2001, which holds schools accountable for whether students pass statewide exams, precludes its use.…

  11. Artificial Intelligence: Themes in the Second Decade. Memo Number 67.

    ERIC Educational Resources Information Center

    Feigenbaum, Edward A.

    The text of an invited address on artificial intelligence (AI) research over the 1963-1968 period is presented. A survey of recent studies on the computer simulation of intellective processes emphasizes developments in heuristic programing, problem-solving and closely related learning models. Progress and problems in these areas are indicated by…

  12. Robotics, Artificial Intelligence, Computer Simulation: Future Applications in Special Education.

    ERIC Educational Resources Information Center

    Moore, Gwendolyn B.; And Others

    The report describes three advanced technologies--robotics, artificial intelligence, and computer simulation--and identifies the ways in which they might contribute to special education. A hybrid methodology was employed to identify existing technology and forecast future needs. Following this framework, each of the technologies is defined,…

  13. Massachusetts Institute of Technology Artificial Intelligence Laboratory Bibliography.

    ERIC Educational Resources Information Center

    Massachusetts Inst. of Tech., Cambridge. Artificial Intelligence Lab.

    Massachusetts Institute of Technology (MIT) presents a bibliography of more than 350 reports, theses, and papers from its artificial intelligence laboratory. Title, author, and identification number are given for all items, and most also have a date and contract number. Some items are no longer available, and others may be obtained from National…

  14. Artificial intelligence and mathematical modeling applied to wireline log interpretation

    SciTech Connect

    Khan, K.A.

    1995-08-01

    Wireline log interpretation is a powerful tool for evaluation of formation characteristics which in turn are used for identification of reservoir and estimation of recoverable hydrocarbons. The log data is stored and plotted using computer applications but manual procedures are mostly adopted for reading the logs and interpreting their signature into the subsurface formations. Keeping in view the new trend of applying artificial intelligence and neural networks to geological applications, an artificial intelligence based approach has been developed for computerized interpretation of logs. The working of this technique involves mathematical models based on curve analyzers which take into account the changing trend of each log curve and then pool out parameters to mathematical equations or directly to the artificial intelligent logic. Such logic has been developed for interpreting all major types of logs like spontaneous potential, gamma ray, sonic, density, neuron and various types of resistivity logs. Thus each log is interpreted independently, for example sand line and shale base lines are automatically marked from spontaneous potential logs and clay percentage is mapped from gamma ray logs. Finally an integrated artificial intelligent analysis takes into account all the logs, their independent interpretations and the local geological information and interprets the subsurface geological picture comprising of various formations.

  15. Reflections on the relationship between artificial intelligence and operations research

    NASA Technical Reports Server (NTRS)

    Fox, Mark S.

    1989-01-01

    Historically, part of Artificial Intelligence's (AI's) roots lie in Operations Research (OR). How AI has extended the problem solving paradigm developed in OR is explored. In particular, by examining how scheduling problems are solved using OR and AI, it is demonstrated that AI extends OR's model of problem solving through the opportunistic use of knowledge, problem reformulation and learning.

  16. Magical Stories: Blending Virtual Reality and Artificial Intelligence.

    ERIC Educational Resources Information Center

    McLellan, Hilary

    Artificial intelligence (AI) techniques and virtual reality (VR) make possible powerful interactive stories, and this paper focuses on examples of virtual characters in three dimensional (3-D) worlds. Waldern, a virtual reality game designer, has theorized about and implemented software design of virtual teammates and opponents that incorporate AI…

  17. Artificial Intelligence Models for Human Problem-Solving.

    ERIC Educational Resources Information Center

    Goldin, Gerald A.; Luger, George F.

    A theory that there is a correspondence between Piagetian conservation operations and groups of symmetry transformations, and that these symmetry transformations may be used in explaining human problem solving behaviors, is developed in this paper. Current research in artificial intelligence is briefly reviewed, then details of the symmetry…

  18. Artificial Intelligence in ADA: Pattern-Directed Processing. Final Report.

    ERIC Educational Resources Information Center

    Reeker, Larry H.; And Others

    To demonstrate to computer programmers that the programming language Ada provides superior facilities for use in artificial intelligence applications, the three papers included in this report investigate the capabilities that exist within Ada for "pattern-directed" programming. The first paper (Larry H. Reeker, Tulane University) is designed to…

  19. Integrating artificial and human intelligence into tablet production process.

    PubMed

    Gams, Matjaž; Horvat, Matej; Ožek, Matej; Luštrek, Mitja; Gradišek, Anton

    2014-12-01

    We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.

  20. Artificial Intelligence (AI) Based Tactical Guidance for Fighter Aircraft

    NASA Technical Reports Server (NTRS)

    McManus, John W.; Goodrich, Kenneth H.

    1990-01-01

    A research program investigating the use of Artificial Intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within Visual Range (WVR) air combat engagements is discussed. The application of AI programming and problem solving methods in the development and implementation of the Computerized Logic For Air-to-Air Warfare Simulations (CLAWS), a second generation TDG, is presented. The Knowledge-Based Systems used by CLAWS to aid in the tactical decision-making process are outlined in detail, and the results of tests to evaluate the performance of CLAWS versus a baseline TDG developed in FORTRAN to run in real-time in the Langley Differential Maneuvering Simulator (DMS), are presented. To date, these test results have shown significant performance gains with respect to the TDG baseline in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify and maintain than the baseline FORTRAN TDG programs. Alternate computing environments and programming approaches, including the use of parallel algorithms and heterogeneous computer networks are discussed, and the design and performance of a prototype concurrent TDG system are presented.

  1. Levels and loops: the future of artificial intelligence and neuroscience.

    PubMed Central

    Bell, A J

    1999-01-01

    In discussing artificial intelligence and neuroscience, I will focus on two themes. The first is the universality of cycles (or loops): sets of variables that affect each other in such a way that any feed-forward account of causality and control, while informative, is misleading. The second theme is based around the observation that a computer is an intrinsically dualistic entity, with its physical set-up designed so as not to interfere with its logical set-up, which executes the computation. The brain is different. When analysed empirically at several different levels (cellular, molecular), it appears that there is no satisfactory way to separate a physical brain model (or algorithm, or representation), from a physical implementational substrate. When program and implementation are inseparable and thus interfere with each other, a dualistic point-of-view is impossible. Forced by empiricism into a monistic perspective, the brain-mind appears as neither embodied by or embedded in physical reality, but rather as identical to physical reality. This perspective has implications for the future of science and society. I will approach these from a negative point-of-view, by critiquing some of our millennial culture's popular projected futures. PMID:10670021

  2. Neuroscientific implications for situated and embodied artificial intelligence

    NASA Astrophysics Data System (ADS)

    Downing, Keith

    2007-03-01

    While classic artificial intelligence systems still struggle to incorporate commonsense knowledge properly, situated and embodied artificial intelligence (SEAI) aims to build animats that acquire a common-sense understanding of the world via interactions between simulated brains, bodies and environments. Neuroscientists believe that much of this common sense involves predictive models for physical activities, but the transfer of sensorimotor skill knowledge to cognition is non-trivial, indicating that SEAI may meet a daunting challenge of its own. This paper considers the neurological bases for implicit procedural and explicit declarative common sense, and the possibilities for its transfer from the former to the latter. This helps assess the prospects for SEAI eventually to surpass GOFAI (good old-fashioned AI) in the quest for generally intelligent systems.

  3. Artificial intelligence in a mission operations and satellite test environment

    NASA Technical Reports Server (NTRS)

    Busse, Carl

    1988-01-01

    A Generic Mission Operations System using Expert System technology to demonstrate the potential of Artificial Intelligence (AI) automated monitor and control functions in a Mission Operations and Satellite Test environment will be developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Expert system techniques in a real time operation environment are being studied and applied to science and engineering data processing. Advanced decommutation schemes and intelligent display technology will be examined to develop imaginative improvements in rapid interpretation and distribution of information. The Generic Payload Operations Control Center (GPOCC) will demonstrate improved data handling accuracy, flexibility, and responsiveness in a complex mission environment. The ultimate goal is to automate repetitious mission operations, instrument, and satellite test functions by the applications of expert system technology and artificial intelligence resources and to enhance the level of man-machine sophistication.

  4. Artificial intelligence - New tools for aerospace project managers

    NASA Technical Reports Server (NTRS)

    Moja, D. C.

    1985-01-01

    Artificial Intelligence (AI) is currently being used for business-oriented, money-making applications, such as medical diagnosis, computer system configuration, and geological exploration. The present paper has the objective to assess new AI tools and techniques which will be available to assist aerospace managers in the accomplishment of their tasks. A study conducted by Brown and Cheeseman (1983) indicates that AI will be employed in all traditional management areas, taking into account goal setting, decision making, policy formulation, evaluation, planning, budgeting, auditing, personnel management, training, legal affairs, and procurement. Artificial intelligence/expert systems are discussed, giving attention to the three primary areas concerned with intelligent robots, natural language interfaces, and expert systems. Aspects of information retrieval are also considered along with the decision support system, and expert systems for project planning and scheduling.

  5. Artificial Intelligence, Education and Music: The Use of Artificial Intelligence To Encourage and Facilitate Music Composition by Novices.

    ERIC Educational Resources Information Center

    Holland, Simon

    The goal of this project was to find ways of using artificial intelligence to encourage and aid music composition by musical novices, particularly those without traditional musical skills. Two complementary approaches are presented. In the first approach, Harmony Space, a new kind of direct manipulation tool for music, is described. Two recent…

  6. Performance evaluation of artificial intelligence classifiers for the medical domain.

    PubMed

    Smith, A E; Nugent, C D; McClean, S I

    2002-01-01

    The application of artificial intelligence systems is still not widespread in the medical field, however there is an increasing necessity for these to handle the surfeit of information available. One drawback to their implementation is the lack of criteria or guidelines for the evaluation of these systems. This is the primary issue in their acceptability to clinicians, who require them for decision support and therefore need evidence that these systems meet the special safety-critical requirements of the domain. This paper shows evidence that the most prevalent form of intelligent system, neural networks, is generally not being evaluated rigorously regarding classification precision. A taxonomy of the types of evaluation tests that can be carried out, to gauge inherent performance of the outputs of intelligent systems has been assembled, and the results of this presented in a clear and concise form, which should be applicable to all intelligent classifiers for medicine.

  7. Artificial pigs in space: using artificial intelligence and artificial life techniques to design animal housing.

    PubMed

    Stricklin, W R; de Bourcier, P; Zhou, J Z; Gonyou, H W

    1998-10-01

    Computer simulations have been used by us since the early 1970s to gain an understanding of the spacing and movement patterns of confined animals. The work has progressed from the early stages, in which we used randomly positioned points, to current investigations of animats (computer-simulated animals), which show low levels of learning via artificial neural networks. We have determined that 1) pens of equal floor area but of different shape result in different spatial and movement patterns for randomly positioned and moving animats; 2) when group size increases under constant density, freedom of movement approaches an asymptote at approximately six animats; 3) matching the number of animats with the number of corners results in optimal freedom of movement for small groups of animats; and 4) perimeter positioning occurs in groups of animats that maximize their distance to first- and second-nearest neighbors. Recently, we developed animats that move, compete for social dominance, and are motivated to obtain resources (food, resting sites, etc.). We are currently developing an animat that learns its behavior from the spatial and movement data collected on live pigs. The animat model is then used to pretest pen designs, followed by new pig spatial data fed into the animat model, resulting in a new pen design to be tested, and the steps are repeated. We believe that methodologies from artificial-life and artificial intelligence can contribute to the understanding of basic animal behavior principles, as well as to the solving of problems in production agriculture in areas such as animal housing design.

  8. Autonomous operations through onboard artificial intelligence

    NASA Technical Reports Server (NTRS)

    Sherwood, R. L.; Chien, S.; Castano, R.; Rabideau, G.

    2002-01-01

    The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

  9. Air Quality Forecasting through Different Statistical and Artificial Intelligence Techniques

    NASA Astrophysics Data System (ADS)

    Mishra, D.; Goyal, P.

    2014-12-01

    Urban air pollution forecasting has emerged as an acute problem in recent years because there are sever environmental degradation due to increase in harmful air pollutants in the ambient atmosphere. In this study, there are different types of statistical as well as artificial intelligence techniques are used for forecasting and analysis of air pollution over Delhi urban area. These techniques are principle component analysis (PCA), multiple linear regression (MLR) and artificial neural network (ANN) and the forecasting are observed in good agreement with the observed concentrations through Central Pollution Control Board (CPCB) at different locations in Delhi. But such methods suffers from disadvantages like they provide limited accuracy as they are unable to predict the extreme points i.e. the pollution maximum and minimum cut-offs cannot be determined using such approach. Also, such methods are inefficient approach for better output forecasting. But with the advancement in technology and research, an alternative to the above traditional methods has been proposed i.e. the coupling of statistical techniques with artificial Intelligence (AI) can be used for forecasting purposes. The coupling of PCA, ANN and fuzzy logic is used for forecasting of air pollutant over Delhi urban area. The statistical measures e.g., correlation coefficient (R), normalized mean square error (NMSE), fractional bias (FB) and index of agreement (IOA) of the proposed model are observed in better agreement with the all other models. Hence, the coupling of statistical and artificial intelligence can be use for the forecasting of air pollutant over urban area.

  10. Anti-fatigue optimization design by artificial intelligence strategy

    NASA Astrophysics Data System (ADS)

    Xing, Bin; Yang, Qingxiong

    1991-11-01

    The artificial intelligence strategy is applied to the optimum antifatigue design of structural details. A set of approaches quite different from the traditional mathematical programming method is put forward. By setting up a state space of discrete design variables to which the intelligence search strategy is applied, the integralization of discrete variables can be avoided. An example of optimum antifatigue design for a typical structure detail of aircraft is worked out using the present method. Comparison between the results obtained by this method and those obtained by the mathematical programming method shows that the present method is better. Verification experiments conducted by the author show that the method proposed is feasible and reliable.

  11. An application of artificial intelligence theory to reconfigurable flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.

    1987-01-01

    Artificial intelligence techniques were used along with statistical hpyothesis testing and modern control theory, to help the pilot cope with the issues of information, knowledge, and capability in the event of a failure. An intelligent flight control system is being developed which utilizes knowledge of cause and effect relationships between all aircraft components. It will screen the information available to the pilots, supplement his knowledge, and most importantly, utilize the remaining flight capability of the aircraft following a failure. The list of failure types the control system will accommodate includes sensor failures, actuator failures, and structural failures.

  12. Artificial Intelligence Software for Assessing Postural Stability

    NASA Technical Reports Server (NTRS)

    Lieberman, Erez; Forth, Katharine; Paloski, William

    2013-01-01

    A software package reads and analyzes pressure distributions from sensors mounted under a person's feet. Pressure data from sensors mounted in shoes, or in a platform, can be used to provide a description of postural stability (assessing competence to deficiency) and enables the determination of the person's present activity (running, walking, squatting, falling). This package has three parts: a preprocessing algorithm for reading input from pressure sensors; a Hidden Markov Model (HMM), which is used to determine the person's present activity and level of sensing-motor competence; and a suite of graphical algorithms, which allows visual representation of the person's activity and vestibular function over time.

  13. Intelligent perturbation algorithms for space scheduling optimization

    NASA Technical Reports Server (NTRS)

    Kurtzman, Clifford R.

    1990-01-01

    The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.

  14. Applications of artificial intelligence to mission planning

    NASA Technical Reports Server (NTRS)

    Ford, Donnie R.; Floyd, Stephen A.; Rogers, John S.

    1990-01-01

    The following subject areas are covered: object-oriented programming task; rule-based programming task; algorithms for resource allocation; connecting a Symbolics to a VAX; FORTRAN from Lisp; trees and forest task; software data structure conversion; software functionality modifications and enhancements; portability of resource allocation to a TI MicroExplorer; frontier of feasibility software system; and conclusions.

  15. ABCluster: the artificial bee colony algorithm for cluster global optimization.

    PubMed

    Zhang, Jun; Dolg, Michael

    2015-10-01

    Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters. PMID:26327507

  16. Artificial Intelligence: An Analysis of the Technology for Training. Training and Development Research Center Project Number Fourteen.

    ERIC Educational Resources Information Center

    Sayre, Scott Alan

    The ultimate goal of the science of artificial intelligence (AI) is to establish programs that will use algorithmic computer techniques to imitate the heuristic thought processes of humans. Most AI programs, especially expert systems, organize their knowledge into three specific areas: data storage, a rule set, and a control structure. Limitations…

  17. [Algorithms, machine intelligence, big data : general considerations].

    PubMed

    Radermacher, F J

    2015-08-01

    We are experiencing astonishing developments in the areas of big data and artificial intelligence. They follow a pattern that we have now been observing for decades: according to Moore's Law,the performance and efficiency in the area of elementary arithmetic operations increases a thousand-fold every 20 years. Although we have not achieved the status where in the singular sense machines have become as "intelligent" as people, machines are becoming increasingly better. The Internet of Things has again helped to massively increase the efficiency of machines. Big data and suitable analytics do the same. If we let these processes simply continue, our civilization may be endangerd in many instances. If the "containment" of these processes succeeds in the context of a reasonable political global governance, a worldwide eco-social market economy, andan economy of green and inclusive markets, many desirable developments that are advantageous for our future may result. Then, at some point in time, the constant need for more and faster innovation may even stop. However, this is anything but certain. We are facing huge challenges.

  18. [Algorithms, machine intelligence, big data : general considerations].

    PubMed

    Radermacher, F J

    2015-08-01

    We are experiencing astonishing developments in the areas of big data and artificial intelligence. They follow a pattern that we have now been observing for decades: according to Moore's Law,the performance and efficiency in the area of elementary arithmetic operations increases a thousand-fold every 20 years. Although we have not achieved the status where in the singular sense machines have become as "intelligent" as people, machines are becoming increasingly better. The Internet of Things has again helped to massively increase the efficiency of machines. Big data and suitable analytics do the same. If we let these processes simply continue, our civilization may be endangerd in many instances. If the "containment" of these processes succeeds in the context of a reasonable political global governance, a worldwide eco-social market economy, andan economy of green and inclusive markets, many desirable developments that are advantageous for our future may result. Then, at some point in time, the constant need for more and faster innovation may even stop. However, this is anything but certain. We are facing huge challenges. PMID:26141245

  19. An artificial neural network controller for intelligent transportation systems applications

    SciTech Connect

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J.

    1996-04-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  20. The mind and the machine: Philosophical aspects of artificial intelligence

    SciTech Connect

    Torrance, S.

    1984-01-01

    What light do current developments in computing shed on philosophical and psychological understanding of the human mind. A group of authors from philosophy, artificial intelligence, linguistics, psychology, and computing provide answers in one of the first texts to bring together a wide range of disciplines into a single area of general international concern. Drawing heavily on current research, the topics discussed are relevant to the growth of public interest in artificial intelligence, expert systems, and fifth generation computers. Topics considered include thought experiments and conceptual investigations, remarks on the language of thought, machines and mind: the functional sphere and epistemological circles, machines, self-control and internationality, linguistic theory, computational models of reasoning, a computer scientist's view of meaning, and learning as a non-deterministic but exact logical process.

  1. The 1991 Goddard Conference on Space Applications of Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Rash, James L. (Editor)

    1991-01-01

    The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in this proceeding fall into the following areas: Planning and scheduling, fault monitoring/diagnosis/recovery, machine vision, robotics, system development, information management, knowledge acquisition and representation, distributed systems, tools, neural networks, and miscellaneous applications.

  2. Experiments with microcomputer-based artificial intelligence environments

    SciTech Connect

    Summers, E.G.; MacDonald, R.A.

    1988-11-01

    The US Geological Survey (USGS) has been experimenting with the use of relatively inexpensive microcomputers as artificial intelligence (AI) development environments. Several AI languages are available that perform fairly well on desk-top personal computers, as are low-to-medium cost expert system packages. Although performance of these systems is respectable, their speed and capacity limitations are questionable for serious earth science applications foreseen by the USGS. The most capable artificial intelligence applications currently are concentrated on what is known as the artificial intelligence computer, and include Xerox D-series, Tektronix 4400 series, Symbolics 3600, VAX, LMI, and Texas Instruments Explorer. The artificial intelligence computer runs expert system shells and Lisp, Prolog, and Smalltalk programming languages. However, these AI environments are expensive. Recently inexpensive 32-bit hardware has become available for the IBM/AT microcomputer. USGS has acquired and recently completed Beta-testing of the Golf Hill Systems 80386 Hummingboard, which runs Common Lisp on an IBM/AT microcomputer. Hummingboard appears to have the potential to overcome many of the speed/capacity limitations observed with AI-applications on standard personal computers. USGS is a Beta-test site for the Gold Hill Systems GoldWorks expert system. GoldWorks combines some high-end expert system shell capabilities in a medium-cost package. This shell is developed in Common Lisp, runs on the 80386 Hummingboard, and provides some expert system features formerly available only on AI-computers including frame and rule-based reasoning, on-line tutorial, multiple inheritance, and object-programming.

  3. Exodus - Distributed artificial intelligence for Shuttle firing rooms

    NASA Technical Reports Server (NTRS)

    Heard, Astrid E.

    1990-01-01

    This paper describes the Expert System for Operations Distributed Users (EXODUS), a knowledge-based artificial intelligence system developed for the four Firing Rooms at the Kennedy Space Center. EXODUS is used by the Shuttle engineers and test conductors to monitor and control the sequence of tasks required for processing and launching Shuttle vehicles. In this paper, attention is given to the goals and the design of EXODUS, the operational requirements, and the extensibility of the technology.

  4. Robotic air vehicle. Blending artificial intelligence with conventional software

    NASA Technical Reports Server (NTRS)

    Mcnulty, Christa; Graham, Joyce; Roewer, Paul

    1987-01-01

    The Robotic Air Vehicle (RAV) system is described. The program's objectives were to design, implement, and demonstrate cooperating expert systems for piloting robotic air vehicles. The development of this system merges conventional programming used in passive navigation with Artificial Intelligence techniques such as voice recognition, spatial reasoning, and expert systems. The individual components of the RAV system are discussed as well as their interactions with each other and how they operate as a system.

  5. Potential applications for artificial intelligence in the petroleum industry

    SciTech Connect

    Alegre, L. )

    1991-11-01

    This article clarifies some concepts of artificial intelligence (AI), discusses some of its applications, and demonstrates its potential application in the petroleum industry. AI is divided into two levels: the psychological, where it attempts to represent knowledge explicitly, and the intuitive, where explication of knowledge is not important and the emphasis is on brain architecture. Expert systems, which implement explicit knowledge, are discussed in more detail. A brief discussion of use of AI in Brazil, particularly at Petrobras, is presented.

  6. Artificial intelligence issues related to automated computing operations

    NASA Technical Reports Server (NTRS)

    Hornfeck, William A.

    1989-01-01

    Large data processing installations represent target systems for effective applications of artificial intelligence (AI) constructs. The system organization of a large data processing facility at the NASA Marshall Space Flight Center is presented. The methodology and the issues which are related to AI application to automated operations within a large-scale computing facility are described. Problems to be addressed and initial goals are outlined.

  7. Artificial intelligence programming languages for computer aided manufacturing

    NASA Technical Reports Server (NTRS)

    Rieger, C.; Samet, H.; Rosenberg, J.

    1979-01-01

    Eight Artificial Intelligence programming languages (SAIL, LISP, MICROPLANNER, CONNIVER, MLISP, POP-2, AL, and QLISP) are presented and surveyed, with examples of their use in an automated shop environment. Control structures are compared, and distinctive features of each language are highlighted. A simple programming task is used to illustrate programs in SAIL, LISP, MICROPLANNER, and CONNIVER. The report assumes reader knowledge of programming concepts, but not necessarily of the languages surveyed.

  8. [I, Robot: artificial intelligence, uniqueness and self-consciousness].

    PubMed

    Agrest, Martín

    2008-01-01

    The cinematographic version of the science fiction classical book by Isaac Asimov (I, Robot) is used as a starting point, from the Artificial Intelligence perspective, in order to analyze what it is to have a self. Uniqueness or the exchange impossibility and the continuity of being one self are put forward to understand the movie's characters as well as the possibilities of feeling self conscious.

  9. Issues in the Evaluation of Artificial Intelligence Systems in Medicine

    PubMed Central

    Miller, Perry L.

    1985-01-01

    The paper discusses the underlying issues in the evaluation of computer systems which apply artificial intelligence in medicine (AIM). Three different levels of evaluation are described: 1) the subjective evaluation of the research contribution of a developmental prototype, 2) the validation of a system's knowledge and performance, 3) the evaluation of the clinical efficacy of an operational system. The paper outlines a number of evaluation issues at each level, and discusses how previous AIM evaluations fit into this framework.

  10. Experiments with microcomputer-based artificial intelligence environments

    USGS Publications Warehouse

    Summers, E.G.; MacDonald, R.A.

    1988-01-01

    The U.S. Geological Survey (USGS) has been experimenting with the use of relatively inexpensive microcomputers as artificial intelligence (AI) development environments. Several AI languages are available that perform fairly well on desk-top personal computers, as are low-to-medium cost expert system packages. Although performance of these systems is respectable, their speed and capacity limitations are questionable for serious earth science applications foreseen by the USGS. The most capable artificial intelligence applications currently are concentrated on what is known as the "artificial intelligence computer," and include Xerox D-series, Tektronix 4400 series, Symbolics 3600, VAX, LMI, and Texas Instruments Explorer. The artificial intelligence computer runs expert system shells and Lisp, Prolog, and Smalltalk programming languages. However, these AI environments are expensive. Recently, inexpensive 32-bit hardware has become available for the IBM/AT microcomputer. USGS has acquired and recently completed Beta-testing of the Gold Hill Systems 80386 Hummingboard, which runs Common Lisp on an IBM/AT microcomputer. Hummingboard appears to have the potential to overcome many of the speed/capacity limitations observed with AI-applications on standard personal computers. USGS is a Beta-test site for the Gold Hill Systems GoldWorks expert system. GoldWorks combines some high-end expert system shell capabilities in a medium-cost package. This shell is developed in Common Lisp, runs on the 80386 Hummingboard, and provides some expert system features formerly available only on AI-computers including frame and rule-based reasoning, on-line tutorial, multiple inheritance, and object-programming. ?? 1988 International Association for Mathematical Geology.

  11. Issues in management of artificial intelligence based projects

    NASA Technical Reports Server (NTRS)

    Kiss, P. A.; Freeman, Michael S.

    1988-01-01

    Now that Artificial Intelligence (AI) is gaining acceptance, it is important to examine some of the obstacles that still stand in the way of its progress. Ironically, many of these obstacles are related to management and are aggravated by the very characteristcs that make AI useful. The purpose of this paper is to heighten awareness of management issues in AI development and to focus attention on their resolution.

  12. Artificial intelligence, expert systems, computer vision, and natural language processing

    NASA Technical Reports Server (NTRS)

    Gevarter, W. B.

    1984-01-01

    An overview of artificial intelligence (AI), its core ingredients, and its applications is presented. The knowledge representation, logic, problem solving approaches, languages, and computers pertaining to AI are examined, and the state of the art in AI is reviewed. The use of AI in expert systems, computer vision, natural language processing, speech recognition and understanding, speech synthesis, problem solving, and planning is examined. Basic AI topics, including automation, search-oriented problem solving, knowledge representation, and computational logic, are discussed.

  13. Distinct Neurocognitive Strategies for Comprehensions of Human and Artificial Intelligence

    PubMed Central

    Ge, Jianqiao; Han, Shihui

    2008-01-01

    Although humans have inevitably interacted with both human and artificial intelligence in real life situations, it is unknown whether the human brain engages homologous neurocognitive strategies to cope with both forms of intelligence. To investigate this, we scanned subjects, using functional MRI, while they inferred the reasoning processes conducted by human agents or by computers. We found that the inference of reasoning processes conducted by human agents but not by computers induced increased activity in the precuneus but decreased activity in the ventral medial prefrontal cortex and enhanced functional connectivity between the two brain areas. The findings provide evidence for distinct neurocognitive strategies of taking others' perspective and inhibiting the process referenced to the self that are specific to the comprehension of human intelligence. PMID:18665211

  14. The Outline of Personhood Law Regarding Artificial Intelligences and Emulated Human Entities

    NASA Astrophysics Data System (ADS)

    Muzyka, Kamil

    2013-12-01

    On the verge of technological breakthroughs, which define and revolutionize our understanding of intelligence, cognition, and personhood, especially when speaking of artificial intelligences and mind uploads, one must consider the legal implications of granting personhood rights to artificial intelligences or emulated human entities

  15. Northeast Artificial Intelligence Consortium (NAIC). Volume 8. Artificial intelligence applications to speech recognition. Final report, Sep 84-Dec 89

    SciTech Connect

    Rhody, H.; Biles, J.

    1990-12-01

    The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose was to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress during the existence of the NAIC of the technical research tasks undertaken at the member universities. The topics covered in general are: versatile expert system for equipment maintenance, distributed AI for communications system control, automatic photointerpretation, time-oriented problem solving, speech understanding systems, knowledge based maintenance, hardware architectures for very large systems, knowledge based reasoning and planning, and a knowledge acquisition, assistance, and explanation system. The specific topic for this volume is the design and implementation of a knowledge-based system to read speech spectrograms.

  16. Northeast Artificial Intelligence Consortium (NAIC). Volume 4. Distributed artificial intelligence for communications network management. Final report, Sep 84-Dec 89

    SciTech Connect

    Meyer, R.A.; Conry, S.E.

    1990-12-01

    The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the office of Scientific Research. Its purpose was to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress during the existence of the NAIC on the technical research tasks undertaken at the member universities. The topics covered in general are: versatile expert system for equipment maintenance, distributed AI for communications system control, automatic photointerpretation, time-oriented problem solving, speech understanding systems, knowledge base maintenance, hardware architectures for very large systems, knowledge based reasoning and planning, and a knowledge acquisition, assistance, and explanation system. The specific topic for this volume is the use of knowledge based systems for communications network management and control via an architecture for a diversely distributed multi-agent system.

  17. Space Communication Artificial Intelligence for Link Evaluation Terminal (SCAILET)

    NASA Technical Reports Server (NTRS)

    Shahidi, Anoosh K.; Schlegelmilch, Richard F.; Petrik, Edward J.; Walters, Jerry L.

    1992-01-01

    A software application to assist end-users of the high burst rate (HBR) link evaluation terminal (LET) for satellite communications is being developed. The HBR LET system developed at NASA Lewis Research Center is an element of the Advanced Communications Technology Satellite (ACTS) Project. The HBR LET is divided into seven major subsystems, each with its own expert. Programming scripts, test procedures defined by design engineers, set up the HBR LET system. These programming scripts are cryptic, hard to maintain and require a steep learning curve. These scripts were developed by the system engineers who will not be available for the end-users of the system. To increase end-user productivity a friendly interface needs to be added to the system. One possible solution is to provide the user with adequate documentation to perform the needed tasks. With the complexity of this system the vast amount of documentation needed would be overwhelming and the information would be hard to retrieve. With limited resources, maintenance is another reason for not using this form of documentation. An advanced form of interaction is being explored using current computer techniques. This application, which incorporates a combination of multimedia and artificial intelligence (AI) techniques to provided end-users with an intelligent interface to the HBR LET system, is comprised of an intelligent assistant, intelligent tutoring, and hypermedia documentation. The intelligent assistant and tutoring systems address the critical programming needs of the end-user.

  18. Hierarchical artificial bee colony algorithm for RFID network planning optimization.

    PubMed

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

  19. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    PubMed Central

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

  20. Hierarchical artificial bee colony algorithm for RFID network planning optimization.

    PubMed

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  1. Application of Artificial Intelligence Techniques in Unmanned Aerial Vehicle Flight

    NASA Technical Reports Server (NTRS)

    Bauer, Frank H. (Technical Monitor); Dufrene, Warren R., Jr.

    2003-01-01

    This paper describes the development of an application of Artificial Intelligence for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in Artificial Intelligence (AI) at Nova southeastern University and as an adjunct to a project at NASA Goddard Space Flight Center's Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an AI method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed. A low cost approach was taken using freeware, gnu, software, and demo programs. The focus of this research has been to outline some of the AI techniques used for UAV flight control and discuss some of the tools used to apply AI techniques. The intent is to succeed with the implementation of applying AI techniques to actually control different aspects of the flight of an UAV.

  2. Artificial intelligence support for scientific model-building

    NASA Technical Reports Server (NTRS)

    Keller, Richard M.

    1992-01-01

    Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

  3. Validating a UAV artificial intelligence control system using an autonomous test case generator

    NASA Astrophysics Data System (ADS)

    Straub, Jeremy; Huber, Justin

    2013-05-01

    The validation of safety-critical applications, such as autonomous UAV operations in an environment which may include human actors, is an ill posed problem. To confidence in the autonomous control technology, numerous scenarios must be considered. This paper expands upon previous work, related to autonomous testing of robotic control algorithms in a two dimensional plane, to evaluate the suitability of similar techniques for validating artificial intelligence control in three dimensions, where a minimum level of airspeed must be maintained. The results of human-conducted testing are compared to this automated testing, in terms of error detection, speed and testing cost.

  4. Artificial intelligence in sports on the example of weight training.

    PubMed

    Novatchkov, Hristo; Baca, Arnold

    2013-01-01

    The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data

  5. Artificial Intelligence in Sports on the Example of Weight Training

    PubMed Central

    Novatchkov, Hristo; Baca, Arnold

    2013-01-01

    The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key points Artificial intelligence is a promising field for sport-related analysis. Implementations integrating pattern recognition techniques enable the automatic evaluation of data

  6. Discrete event simulation in the artificial intelligence environment

    SciTech Connect

    Egdorf, H.W.; Roberts, D.J.

    1987-01-01

    Discrete Event Simulations performed in an Artificial Intelligence (AI) environment provide benefits in two major areas. The productivity provided by Object Oriented Programming, Rule Based Programming, and AI development environments allows simulations to be developed and maintained more efficiently than conventional environments allow. Secondly, the use of AI techniques allows direct simulation of human decision making processes and Command and Control aspects of a system under study. An introduction to AI techniques is presented. Two discrete event simulations produced in these environments are described. Finally, a software engineering methodology is discussed that allows simulations to be designed for use in these environments. 3 figs.

  7. The application of artificial intelligence techniques to large distributed networks

    NASA Technical Reports Server (NTRS)

    Dubyah, R.; Smith, T. R.; Star, J. L.

    1985-01-01

    Data accessibility and transfer of information, including the land resources information system pilot, are structured as large computer information networks. These pilot efforts include the reduction of the difficulty to find and use data, reducing processing costs, and minimize incompatibility between data sources. Artificial Intelligence (AI) techniques were suggested to achieve these goals. The applicability of certain AI techniques are explored in the context of distributed problem solving systems and the pilot land data system (PLDS). The topics discussed include: PLDS and its data processing requirements, expert systems and PLDS, distributed problem solving systems, AI problem solving paradigms, query processing, and distributed data bases.

  8. Artificial Intelligence For A Safer And More Efficient Car Driving

    NASA Astrophysics Data System (ADS)

    Adorni, Giovanni

    1989-03-01

    In this paper a project, PROMETHEUS, is described in which fourteen of Europe's leading car manufacturers are to join with approximately forty research institutes and governmental agencies to make the traffic of Europe safer, more efficient and more economical. PROMETHEUS project is divided into seven areas. In this paper one of the seven areas, PRO-ART, is described. PRO-ART is aimed at clarifying the need for and the principles of the artificial intelligence to be used in the next generation automobile. After a brief description of the overhall project, the description of the seven years PRO-ART Italian research programme will be given.

  9. The application of artificial intelligence technology to aeronautical system design

    NASA Technical Reports Server (NTRS)

    Bouchard, E. E.; Kidwell, G. H.; Rogan, J. E.

    1988-01-01

    This paper describes the automation of one class of aeronautical design activity using artificial intelligence and advanced software techniques. Its purpose is to suggest concepts, terminology, and approaches that may be useful in enhancing design automation. By understanding the basic concepts and tasks in design, and the technologies that are available, it will be possible to produce, in the future, systems whose capabilities far exceed those of today's methods. Some of the tasks that will be discussed have already been automated and are in production use, resulting in significant productivity benefits. The concepts and techniques discussed are applicable to all design activity, though aeronautical applications are specifically presented.

  10. The role of artificial intelligence techniques in scheduling systems

    NASA Technical Reports Server (NTRS)

    Geoffroy, Amy L.; Britt, Daniel L.; Gohring, John R.

    1990-01-01

    Artificial Intelligence (AI) techniques provide good solutions for many of the problems which are characteristic of scheduling applications. However, scheduling is a large, complex heterogeneous problem. Different applications will require different solutions. Any individual application will require the use of a variety of techniques, including both AI and conventional software methods. The operational context of the scheduling system will also play a large role in design considerations. The key is to identify those places where a specific AI technique is in fact the preferable solution, and to integrate that technique into the overall architecture.

  11. Artificial intelligence and expert systems in-flight software testing

    NASA Technical Reports Server (NTRS)

    Demasie, M. P.; Muratore, J. F.

    1991-01-01

    The authors discuss the introduction of advanced information systems technologies such as artificial intelligence, expert systems, and advanced human-computer interfaces directly into Space Shuttle software engineering. The reconfiguration automation project (RAP) was initiated to coordinate this move towards 1990s software technology. The idea behind RAP is to automate several phases of the flight software testing procedure and to introduce AI and ES into space shuttle flight software testing. In the first phase of RAP, conventional tools to automate regression testing have already been developed or acquired. There are currently three tools in use.

  12. Artificial intelligence in medicine: humans need not apply?

    PubMed

    Diprose, William; Buist, Nicholas

    2016-05-06

    Artificial intelligence (AI) is a rapidly growing field with a wide range of applications. Driven by economic constraints and the potential to reduce human error, we believe that over the coming years AI will perform a significant amount of the diagnostic and treatment decision-making traditionally performed by the doctor. Humans would continue to be an important part of healthcare delivery, but in many situations, less expensive fit-for-purpose healthcare workers could be trained to 'fill the gaps' where AI are less capable. As a result, the role of the doctor as an expensive problem-solver would become redundant.

  13. Artificial intelligence techniques for embryo and oocyte classification.

    PubMed

    Manna, Claudio; Nanni, Loris; Lumini, Alessandra; Pappalardo, Sebastiana

    2013-01-01

    One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in the capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. This work concentrates the efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the local binary patterns). The proposed system was tested on two data sets of 269 oocytes and 269 corresponding embryos from 104 women and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they show an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in our capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. In this work, we concentrate our efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology

  14. Using artificial intelligence to control fluid flow computations

    NASA Technical Reports Server (NTRS)

    Gelsey, Andrew

    1992-01-01

    Computational simulation is an essential tool for the prediction of fluid flow. Many powerful simulation programs exist today. However, using these programs to reliably analyze fluid flow and other physical situations requires considerable human effort and expertise to set up a simulation, determine whether the output makes sense, and repeatedly run the simulation with different inputs until a satisfactory result is achieved. Automating this process is not only of considerable practical importance but will also significantly advance basic artificial intelligence (AI) research in reasoning about the physical world.

  15. Application of artificial intelligence techniques to emergency response facilities architecture

    SciTech Connect

    Fouad, H.Y. )

    1991-01-01

    This paper reports on nuclear power plants designed, equipped, and operated within safety envelopes. Unanticipated failures, operator's malfunctions, and external events cause emergency conditions. Despite the fact that in the Three Mile Island and Chernobyl events the systems failed, there have been multitudes of other situations where systems succeeded. The use of artificial intelligence and expert systems techniques provide useful tools to meet those challenges. The application includes three phases: diagnostics, critical functions restoration, and optimum recovery. The use of expert systems techniques to enhance the operator functions in the emergency conditions are outlined.

  16. Use of artificial intelligence in well-test interpretation

    SciTech Connect

    Allain, O.F. ); Horne, R.N. )

    1990-03-01

    This paper describes techniques for the identification of well-test interpretation models from pressure-derivative data. Artificial intelligence (Al) techniques are used to separate the derivative response from signal and differentiation noise, and a rule-based recognition system characterizes the response using a symbolic representation. The computer's choice of a model is based on the pressure-derivative curve and simulates the visual diagnosis performed by a human expert. The reasoning involved in such a diagnosis uses a symbolic representation of the derivative curve.

  17. Fourth Conference on Artificial Intelligence for Space Applications

    NASA Technical Reports Server (NTRS)

    Odell, Stephen L. (Compiler); Denton, Judith S. (Compiler); Vereen, Mary (Compiler)

    1988-01-01

    Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming.

  18. Artificial intelligence techniques for scheduling Space Shuttle missions

    NASA Technical Reports Server (NTRS)

    Henke, Andrea L.; Stottler, Richard H.

    1994-01-01

    Planning and scheduling of NASA Space Shuttle missions is a complex, labor-intensive process requiring the expertise of experienced mission planners. We have developed a planning and scheduling system using combinations of artificial intelligence knowledge representations and planning techniques to capture mission planning knowledge and automate the multi-mission planning process. Our integrated object oriented and rule-based approach reduces planning time by orders of magnitude and provides planners with the flexibility to easily modify planning knowledge and constraints without requiring programming expertise.

  19. Artificial intelligence in medicine: humans need not apply?

    PubMed

    Diprose, William; Buist, Nicholas

    2016-01-01

    Artificial intelligence (AI) is a rapidly growing field with a wide range of applications. Driven by economic constraints and the potential to reduce human error, we believe that over the coming years AI will perform a significant amount of the diagnostic and treatment decision-making traditionally performed by the doctor. Humans would continue to be an important part of healthcare delivery, but in many situations, less expensive fit-for-purpose healthcare workers could be trained to 'fill the gaps' where AI are less capable. As a result, the role of the doctor as an expensive problem-solver would become redundant. PMID:27349266

  20. Artificial intelligence program in a computer application supporting reactor operations

    SciTech Connect

    Stratton, R.C.; Town, G.G.

    1985-01-01

    Improving nuclear reactor power plant operability is an ever-present concern for the nuclear industry. The definition of plant operability involves a complex interaction of the ideas of reliability, safety, and efficiency. This paper presents observations concerning the issues involved and the benefits derived from the implementation of a computer application which combines traditional computer applications with artificial intelligence (AI) methodologies. A system, the Component Configuration Control System (CCCS), is being installed to support nuclear reactor operations at the Experimental Breeder Reactor II.

  1. A new improved artificial bee colony algorithm for ship hull form optimization

    NASA Astrophysics Data System (ADS)

    Huang, Fuxin; Wang, Lijue; Yang, Chi

    2016-04-01

    The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

  2. A multiuser detector based on artificial bee colony algorithm for DS-UWB systems.

    PubMed

    Yin, Zhendong; Liu, Xiaohui; Wu, Zhilu

    2013-01-01

    Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity. PMID:23983638

  3. Artificial intelligence methods in deregulated power systems operations

    NASA Astrophysics Data System (ADS)

    Ilic, Jovan

    With the introduction of the power systems deregulation, many classical power transmission and distribution optimization tools became inadequate. Optimal Power Flow and Unit Commitment are common computer programs used in the regulated power industry. This work is addressing the Optimal Power Flow and Unit Commitment in the new deregulated environment. Optimal Power Flow is a high dimensional, non-linear, and non-convex optimization problem. As such, it is even now, after forty years since its introduction, a research topic without a widely accepted solution able to encompass all areas of interest. Unit Commitment is a high dimensional, combinatorial problem which should ideally include the Optimal Power Flow in its solution. The dimensionality of a typical Unit Commitment problem is so great that even the enumeration of all the combinations would take too much time for any practical purposes. This dissertation attacks the Optimal Power Flow problem using non-traditional tools from the Artificial Intelligence arena. Artificial Intelligence optimization methods are based on stochastic principles. Usually, stochastic optimization methods are successful where all other classical approaches fail. We will use Genetic Programming optimization for both Optimal Power Flow and Unit Commitment. Long processing times will also be addressed through supervised machine learning.

  4. Artificial Intelligence Techniques for Automatic Screening of Amblyogenic Factors

    PubMed Central

    Van Eenwyk, Jonathan; Agah, Arvin; Giangiacomo, Joseph; Cibis, Gerhard

    2008-01-01

    Purpose To develop a low-cost automated video system to effectively screen children aged 6 months to 6 years for amblyogenic factors. Methods In 1994 one of the authors (G.C.) described video vision development assessment, a digitizable analog video-based system combining Brückner pupil red reflex imaging and eccentric photorefraction to screen young children for amblyogenic factors. The images were analyzed manually with this system. We automated the capture of digital video frames and pupil images and applied computer vision and artificial intelligence to analyze and interpret results. The artificial intelligence systems were evaluated by a tenfold testing method. Results The best system was the decision tree learning approach, which had an accuracy of 77%, compared to the “gold standard” specialist examination with a “refer/do not refer” decision. Criteria for referral were strabismus, including microtropia, and refractive errors and anisometropia considered to be amblyogenic. Eighty-two percent of strabismic individuals were correctly identified. High refractive errors were also correctly identified and referred 90% of the time, as well as significant anisometropia. The program was less correct in identifying more moderate refractive errors, below +5 and less than −7. Conclusions Although we are pursuing a variety of avenues to improve the accuracy of the automated analysis, the program in its present form provides acceptable cost benefits for detecting ambylogenic factors in children aged 6 months to 6 years. PMID:19277222

  5. Natural and Artificial Intelligence, Language, Consciousness, Emotion, and Anticipation

    NASA Astrophysics Data System (ADS)

    Dubois, Daniel M.

    2010-11-01

    The classical paradigm of the neural brain as the seat of human natural intelligence is too restrictive. This paper defends the idea that the neural ectoderm is the actual brain, based on the development of the human embryo. Indeed, the neural ectoderm includes the neural crest, given by pigment cells in the skin and ganglia of the autonomic nervous system, and the neural tube, given by the brain, the spinal cord, and motor neurons. So the brain is completely integrated in the ectoderm, and cannot work alone. The paper presents fundamental properties of the brain as follows. Firstly, Paul D. MacLean proposed the triune human brain, which consists to three brains in one, following the species evolution, given by the reptilian complex, the limbic system, and the neo-cortex. Secondly, the consciousness and conscious awareness are analysed. Thirdly, the anticipatory unconscious free will and conscious free veto are described in agreement with the experiments of Benjamin Libet. Fourthly, the main section explains the development of the human embryo and shows that the neural ectoderm is the whole neural brain. Fifthly, a conjecture is proposed that the neural brain is completely programmed with scripts written in biological low-level and high-level languages, in a manner similar to the programmed cells by the genetic code. Finally, it is concluded that the proposition of the neural ectoderm as the whole neural brain is a breakthrough in the understanding of the natural intelligence, and also in the future design of robots with artificial intelligence.

  6. Games and Machine Learning: A Powerful Combination in an Artificial Intelligence Course

    ERIC Educational Resources Information Center

    Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

    2010-01-01

    Project MLeXAI [Machine Learning eXperiences in Artificial Intelligence (AI)] seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense--a simple real-time strategy game…

  7. Artificial Intelligence Measurement System, Overview and Lessons Learned. Final Project Report.

    ERIC Educational Resources Information Center

    Baker, Eva L.; Butler, Frances A.

    This report summarizes the work conducted for the Artificial Intelligence Measurement System (AIMS) Project which was undertaken as an exploration of methodology to consider how the effects of artificial intelligence systems could be compared to human performance. The research covered four areas of inquiry: (1) natural language processing and…

  8. Artificial Intelligence in Sports Biomechanics: New Dawn or False Hope?

    PubMed Central

    Bartlett, Roger

    2006-01-01

    This article reviews developments in the use of Artificial Intelligence (AI) in sports biomechanics over the last decade. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements (‘techniques’) and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then given to the use of Artificial Neural Networks (ANNs) in sports biomechanics, focusing on Kohonen self-organizing maps, which have been the most widely used in technique analysis, and multi-layer networks, which have been far more widely used in biomechanics in general. Examples of the use of ANNs in sports biomechanics are presented for javelin and discus throwing, shot putting and football kicking. I also present an example of the use of Evolutionary Computation in movement optimization in the soccer throw in, which predicted an optimal technique close to that in the coaching literature. After briefly overviewing the use of AI in both sports science and biomechanics in general, the article concludes with some speculations about future uses of AI in sports biomechanics. Key Points Expert Systems remain almost unused in sports biomechanics, unlike in the similar discipline of gait analysis. Artificial Neural Networks, particularly Kohonen Maps, have been used, although their full value remains unclear. Other AI applications, including Evolutionary Computation, have received little attention. PMID:24357939

  9. A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.

    PubMed

    Li, Shan; Kang, Liying; Zhao, Xing-Ming

    2014-01-01

    With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

  10. A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

    PubMed Central

    Li, Shan; Zhao, Xing-Ming

    2014-01-01

    With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969

  11. How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods

    PubMed Central

    Grossi, Enzo

    2006-01-01

    Background In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years Discussion The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. Summary The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level. PMID:16672045

  12. Space applications of artificial intelligence; 1990 Goddard Conference, Greenbelt, MD, May 1, 2, 1990, Selected Papers

    NASA Technical Reports Server (NTRS)

    Rash, James L. (Editor)

    1990-01-01

    The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.

  13. METEOR - an artificial intelligence system for convective storm forecasting

    SciTech Connect

    Elio, R.; De haan, J.; Strong, G.S.

    1987-03-01

    An AI system called METEOR, which uses the meteorologist's heuristics, strategies, and statistical tools to forecast severe hailstorms in Alberta, is described, emphasizing the information and knowledge that METEOR uses to mimic the forecasting procedure of an expert meteorologist. METEOR is then discussed as an AI system, emphasizing the ways in which it is qualitatively different from algorithmic or statistical approaches to prediction. Some features of METEOR's design and the AI techniques for representing meteorological knowledge and for reasoning and inference are presented. Finally, some observations on designing and implementing intelligent consultants for meteorological applications are made. 7 references.

  14. Applications of artificial intelligence X: Machine vision and robotics; Proceedings of the Meeting, Orlando, FL, Apr. 22-24, 1992

    SciTech Connect

    Bowyer, K.W.

    1992-01-01

    Various papers on artificial intelligence in machine vision and robotics are presented. The general topics addressed include: design of a robot head, machine vision inspection techniques, segmentation of fused range and intensity imagery, parallel and VLSI architectures for machine vision, comparison of range image segmentation algorithms, state of the art in postcanny edge detection, simulation and visualization environments for autonomous robots, exploration of recognition by components representation and matching, reactive robotic control strategies, image processing techniques.

  15. Evolvable mathematical models: A new artificial Intelligence paradigm

    NASA Astrophysics Data System (ADS)

    Grouchy, Paul

    We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which interagent communication emerges and evolves from initially noncommunicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analyzed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.

  16. Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

    NASA Astrophysics Data System (ADS)

    Wilson, Eric Lee

    Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

  17. A survey on the design of multiprocessing systems for artificial intelligence applications

    NASA Technical Reports Server (NTRS)

    Wah, Benjamin W.; Li, Guo Jie

    1989-01-01

    Some issues in designing computers for artificial intelligence (AI) processing are discussed. These issues are divided into three levels: the representation level, the control level, and the processor level. The representation level deals with the knowledge and methods used to solve the problem and the means to represent it. The control level is concerned with the detection of dependencies and parallelism in the algorithmic and program representations of the problem, and with the synchronization and sheduling of concurrent tasks. The processor level addresses the hardware and architectural components needed to evaluate the algorithmic and program representations. Solutions for the problems of each level are illustrated by a number of representative systems. Design decisions in existing projects on AI computers are classed into top-down, bottom-up, and middle-out approaches.

  18. A discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequences.

    PubMed

    Karaboga, D; Aslan, S

    2016-01-01

    The great majority of biological sequences share significant similarity with other sequences as a result of evolutionary processes, and identifying these sequence similarities is one of the most challenging problems in bioinformatics. In this paper, we present a discrete artificial bee colony (ABC) algorithm, which is inspired by the intelligent foraging behavior of real honey bees, for the detection of highly conserved residue patterns or motifs within sequences. Experimental studies on three different data sets showed that the proposed discrete model, by adhering to the fundamental scheme of the ABC algorithm, produced competitive or better results than other metaheuristic motif discovery techniques. PMID:27173272

  19. AIonAI: a humanitarian law of artificial intelligence and robotics.

    PubMed

    Ashrafian, Hutan

    2015-02-01

    The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and morals deriving from this technological prospect have been considered in the philosophy of artificial intelligence, the design of automatons with roboethics and the contemplation of machine ethics through the concept of artificial moral agents. Across these categories, the robotics laws first proposed by Isaac Asimov in the twentieth century remain well-recognised and esteemed due to their specification of preventing human harm, stipulating obedience to humans and incorporating robotic self-protection. However the overwhelming predominance in the study of this field has focussed on human-robot interactions without fully considering the ethical inevitability of future artificial intelligences communicating together and has not addressed the moral nature of robot-robot interactions. A new robotic law is proposed and termed AIonAI or artificial intelligence-on-artificial intelligence. This law tackles the overlooked area where future artificial intelligences will likely interact amongst themselves, potentially leading to exploitation. As such, they would benefit from adopting a universal law of rights to recognise inherent dignity and the inalienable rights of artificial intelligences. Such a consideration can help prevent exploitation and abuse of rational and sentient beings, but would also importantly reflect on our moral code of ethics and the humanity of our civilisation.

  20. Artificial intelligence for multi-mission planetary operations

    NASA Technical Reports Server (NTRS)

    Atkinson, David J.; Lawson, Denise L.; James, Mark L.

    1990-01-01

    A brief introduction is given to an automated system called the Spacecraft Health Automated Reasoning Prototype (SHARP). SHARP is designed to demonstrate automated health and status analysis for multi-mission spacecraft and ground data systems operations. The SHARP system combines conventional computer science methodologies with artificial intelligence techniques to produce an effective method for detecting and analyzing potential spacecraft and ground systems problems. The system performs real-time analysis of spacecraft and other related telemetry, and is also capable of examining data in historical context. Telecommunications link analysis of the Voyager II spacecraft is the initial focus for evaluation of the prototype in a real-time operations setting during the Voyager spacecraft encounter with Neptune in August, 1989. The preliminary results of the SHARP project and plans for future application of the technology are discussed.

  1. Artificial intelligence (AI) based tactical guidance for fighter aircraft

    NASA Technical Reports Server (NTRS)

    Mcmanus, John W.; Goodrich, Kenneth H.

    1990-01-01

    A research program investigating the use of artificial intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within Visual Range air combat engagements is discussed. The application of AI programming and problem solving methods in the development and implementation of the Computerized Logic For Air-to-Air Warfare Simulations (CLAWS), a second generation TDG, is presented. The knowledge-based systems used by CLAWS to aid in the tactical decision-making process are outlined in detail, and the results of tests to evaluate the performance of CLAWS versus a baseline TDG developed in FORTRAN to run in real time in the Langley Differential Maneuvering Simulator, are presented. To date, these test results have shown significant performance gains with respect to the TDG baseline in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify and maintain than the baseline FORTRAN TDG programs.

  2. Applications of Some Artificial Intelligence Methods to Satellite Soundings

    NASA Technical Reports Server (NTRS)

    Munteanu, M. J.; Jakubowicz, O.

    1985-01-01

    Hard clustering of temperature profiles and regression temperature retrievals were used to refine the method using the probabilities of membership of each pattern vector in each of the clusters derived with discriminant analysis. In hard clustering the maximum probability is taken and the corresponding cluster as the correct cluster are considered discarding the rest of the probabilities. In fuzzy partitioned clustering these probabilities are kept and the final regression retrieval is a weighted regression retrieval of several clusters. This method was used in the clustering of brightness temperatures where the purpose was to predict tropopause height. A further refinement is the division of temperature profiles into three major regions for classification purposes. The results are summarized in the tables total r.m.s. errors are displayed. An approach based on fuzzy logic which is intimately related to artificial intelligence methods is recommended.

  3. Application of Artificial Intelligence for Bridge Deterioration Model.

    PubMed

    Chen, Zhang; Wu, Yangyang; Li, Li; Sun, Lijun

    2015-01-01

    The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  4. Application of Artificial Intelligence to Reservoir Characterization - An Interdisciplinary Approach

    SciTech Connect

    Kelkar, B.G.; Gamble, R.F.; Kerr, D.R.; Thompson, L.G.; Shenoi, S.

    2000-01-12

    The primary goal of this project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlation's between wells. Using the correlation's and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical techniques. The generated petrophysical properties are used as input in the next step where the production data are honored. By adjusting the petrophysical properties, the match between the simulated and the observed production rates is obtained.

  5. Artificial intelligence and the space station software support environment

    NASA Technical Reports Server (NTRS)

    Marlowe, Gilbert

    1986-01-01

    In a software system the size of the Space Station Software Support Environment (SSE), no one software development or implementation methodology is presently powerful enough to provide safe, reliable, maintainable, cost effective real time or near real time software. In an environment that must survive one of the most harsh and long life times, software must be produced that will perform as predicted, from the first time it is executed to the last. Many of the software challenges that will be faced will require strategies borrowed from Artificial Intelligence (AI). AI is the only development area mentioned as an example of a legitimate reason for a waiver from the overall requirement to use the Ada programming language for software development. The limits are defined of the applicability of the Ada language Ada Programming Support Environment (of which the SSE is a special case), and software engineering to AI solutions by describing a scenario that involves many facets of AI methodologies.

  6. Estimation of mechanical properties of nanomaterials using artificial intelligence methods

    NASA Astrophysics Data System (ADS)

    Vijayaraghavan, V.; Garg, A.; Wong, C. H.; Tai, K.

    2014-09-01

    Computational modeling tools such as molecular dynamics (MD), ab initio, finite element modeling or continuum mechanics models have been extensively applied to study the properties of carbon nanotubes (CNTs) based on given input variables such as temperature, geometry and defects. Artificial intelligence techniques can be used to further complement the application of numerical methods in characterizing the properties of CNTs. In this paper, we have introduced the application of multi-gene genetic programming (MGGP) and support vector regression to formulate the mathematical relationship between the compressive strength of CNTs and input variables such as temperature and diameter. The predictions of compressive strength of CNTs made by these models are compared to those generated using MD simulations. The results indicate that MGGP method can be deployed as a powerful method for predicting the compressive strength of the carbon nanotubes.

  7. Application of Artificial Intelligence for Bridge Deterioration Model

    PubMed Central

    Chen, Zhang; Wu, Yangyang; Li, Li; Sun, Lijun

    2015-01-01

    The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention. PMID:26601121

  8. Implementing Artificial Intelligence Behaviors in a Virtual World

    NASA Technical Reports Server (NTRS)

    Krisler, Brian; Thome, Michael

    2012-01-01

    In this paper, we will present a look at the current state of the art in human-computer interface technologies, including intelligent interactive agents, natural speech interaction and gestural based interfaces. We describe our use of these technologies to implement a cost effective, immersive experience on a public region in Second Life. We provision our Artificial Agents as a German Shepherd Dog avatar with an external rules engine controlling the behavior and movement. To interact with the avatar, we implemented a natural language and gesture system allowing the human avatars to use speech and physical gestures rather than interacting via a keyboard and mouse. The result is a system that allows multiple humans to interact naturally with AI avatars by playing games such as fetch with a flying disk and even practicing obedience exercises using voice and gesture, a natural seeming day in the park.

  9. An intercomparison of artificial intelligence approaches for polar scene identification

    NASA Technical Reports Server (NTRS)

    Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

    1993-01-01

    The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

  10. A Distributed Artificial Intelligence Approach To Object Identification And Classification

    NASA Astrophysics Data System (ADS)

    Sikka, Digvijay I.; Varshney, Pramod K.; Vannicola, Vincent C.

    1989-09-01

    This paper presents an application of Distributed Artificial Intelligence (DAI) tools to the data fusion and classification problem. Our approach is to use a blackboard for information management and hypothe-ses formulation. The blackboard is used by the knowledge sources (KSs) for sharing information and posting their hypotheses on, just as experts sitting around a round table would do. The present simulation performs classification of an Aircraft(AC), after identifying it by its features, into disjoint sets (object classes) comprising of the five commercial ACs; Boeing 747, Boeing 707, DC10, Concord and Boeing 727. A situation data base is characterized by experimental data available from the three levels of expert reasoning. Ohio State University ElectroScience Laboratory provided this experimental data. To validate the architecture presented, we employ two KSs for modeling the sensors, aspect angle polarization feature and the ellipticity data. The system has been implemented on Symbolics 3645, under Genera 7.1, in Common LISP.

  11. Artificial intelligence applications in space and SDI: A survey

    NASA Technical Reports Server (NTRS)

    Fiala, Harvey E.

    1988-01-01

    The purpose of this paper is to survey existing and planned Artificial Intelligence (AI) applications to show that they are sufficiently advanced for 32 percent of all space applications and SDI (Space Defense Initiative) software to be AI-based software. To best define the needs that AI can fill in space and SDI programs, this paper enumerates primary areas of research and lists generic application areas. Current and planned NASA and military space projects in AI will be reviewed. This review will be largely in the selected area of expert systems. Finally, direct applications of AI to SDI will be treated. The conclusion covers the importance of AI to space and SDI applications, and conversely, their importance to AI.

  12. [Artificial intelligence to assist clinical diagnosis in medicine].

    PubMed

    Lugo-Reyes, Saúl Oswaldo; Maldonado-Colín, Guadalupe; Murata, Chiharu

    2014-01-01

    Medicine is one of the fields of knowledge that would most benefit from a closer interaction with Computer studies and Mathematics by optimizing complex, imperfect processes such as differential diagnosis; this is the domain of Machine Learning, a branch of Artificial Intelligence that builds and studies systems capable of learning from a set of training data, in order to optimize classification and prediction processes. In Mexico during the last few years, progress has been made on the implementation of electronic clinical records, so that the National Institutes of Health already have accumulated a wealth of stored data. For those data to become knowledge, they need to be processed and analyzed through complex statistical methods, as it is already being done in other countries, employing: case-based reasoning, artificial neural networks, Bayesian classifiers, multivariate logistic regression, or support vector machines, among other methodologies; to assist the clinical diagnosis of acute appendicitis, breast cancer and chronic liver disease, among a wide array of maladies. In this review we shift through concepts, antecedents, current examples and methodologies of machine learning-assisted clinical diagnosis.

  13. Programming environment for distributed applications design in artificial intelligence

    NASA Astrophysics Data System (ADS)

    Baujard, Olivier; Pesty, Sylvie; Garbay, Catherine

    1992-03-01

    Complex applications in artificial intelligence need a multiple representation of knowledge and tasks in terms of abstraction levels and points of view. The integration of numerous resources (knowledge-based systems, real-time systems, data bases, etc.), often geographically distributed on different machines connected into a network, is moreover a necessity for the development of real scale systems. The distributed artificial intelligence (DAI) approach is thus becoming important to solve problems in complex situations. There are several currents in DAI research and we are involved in the design of DAI programming platforms for large and complex real-world problem solving systems. Blackboard systems constitute the earlier architecture. It is based on a shared memory which permits the communication among a collection of specialists and an external and unique control structure. Blackboard architectures have been extended, especially to introduce parallelism. Multi-agent architectures are based on coordinated agents (problem-solvers) communicating most of the time via message passing. A solution is found through the cooperation between several agents, each of them being in charge of a specific task, but no one having sufficient resources to obtain a solution. Coordination, cooperation, knowledge, goal, plan, exchanges are then necessary to reach a global solution. Our own research is along this last line. The current presentation describes Multi-Agent Problem Solver (MAPS) which is an agent-oriented language for a DAI system design embedded in a full programming environment. An agent is conceived as an autonomous entity with specific goals, roles, skills, and resources. Knowledge (descriptive and operative) is distributed among agents organized into networks (agents communicate through message sending). Agents are moreover geographically distributed and run in a parallel mode. Our purpose is to build a powerful environment for DAI applications design that not only

  14. Virtual Reality for Artificial Intelligence: human-centered simulation for social science.

    PubMed

    Cipresso, Pietro; Riva, Giuseppe

    2015-01-01

    There is a long last tradition in Artificial Intelligence as use of Robots endowing human peculiarities, from a cognitive and emotional point of view, and not only in shape. Today Artificial Intelligence is more oriented to several form of collective intelligence, also building robot simulators (hardware or software) to deeply understand collective behaviors in human beings and society as a whole. Modeling has also been crucial in the social sciences, to understand how complex systems can arise from simple rules. However, while engineers' simulations can be performed in the physical world using robots, for social scientist this is impossible. For decades, researchers tried to improve simulations by endowing artificial agents with simple and complex rules that emulated human behavior also by using artificial intelligence (AI). To include human beings and their real intelligence within artificial societies is now the big challenge. We present an hybrid (human-artificial) platform where experiments can be performed by simulated artificial worlds in the following manner: 1) agents' behaviors are regulated by the behaviors shown in Virtual Reality involving real human beings exposed to specific situations to simulate, and 2) technology transfers these rules into the artificial world. These form a closed-loop of real behaviors inserted into artificial agents, which can be used to study real society. PMID:26799903

  15. Virtual Reality for Artificial Intelligence: human-centered simulation for social science.

    PubMed

    Cipresso, Pietro; Riva, Giuseppe

    2015-01-01

    There is a long last tradition in Artificial Intelligence as use of Robots endowing human peculiarities, from a cognitive and emotional point of view, and not only in shape. Today Artificial Intelligence is more oriented to several form of collective intelligence, also building robot simulators (hardware or software) to deeply understand collective behaviors in human beings and society as a whole. Modeling has also been crucial in the social sciences, to understand how complex systems can arise from simple rules. However, while engineers' simulations can be performed in the physical world using robots, for social scientist this is impossible. For decades, researchers tried to improve simulations by endowing artificial agents with simple and complex rules that emulated human behavior also by using artificial intelligence (AI). To include human beings and their real intelligence within artificial societies is now the big challenge. We present an hybrid (human-artificial) platform where experiments can be performed by simulated artificial worlds in the following manner: 1) agents' behaviors are regulated by the behaviors shown in Virtual Reality involving real human beings exposed to specific situations to simulate, and 2) technology transfers these rules into the artificial world. These form a closed-loop of real behaviors inserted into artificial agents, which can be used to study real society.

  16. Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

    NASA Astrophysics Data System (ADS)

    Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

    2016-06-01

    With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

  17. Artificial intelligence techniques to optimize the EDC/NHS-mediated immobilization of cellulase on Eudragit L-100.

    PubMed

    Zhang, Yu; Xu, Jing-Liang; Yuan, Zhen-Hong; Qi, Wei; Liu, Yun-Yun; He, Min-Chao

    2012-01-01

    Two artificial intelligence techniques, namely artificial neural network (ANN) and genetic algorithm (GA) were combined to be used as a tool for optimizing the covalent immobilization of cellulase on a smart polymer, Eudragit L-100. 1-Ethyl-3-(3-dimethyllaminopropyl) carbodiimide (EDC) concentration, N-hydroxysuccinimide (NHS) concentration and coupling time were taken as independent variables, and immobilization efficiency was taken as the response. The data of the central composite design were used to train ANN by back-propagation algorithm, and the result showed that the trained ANN fitted the data accurately (correlation coefficient R(2) = 0.99). Then a maximum immobilization efficiency of 88.76% was searched by genetic algorithm at a EDC concentration of 0.44%, NHS concentration of 0.37% and a coupling time of 2.22 h, where the experimental value was 87.97 ± 6.45%. The application of ANN based optimization by GA is quite successful.

  18. Artificial Intelligence Techniques to Optimize the EDC/NHS-Mediated Immobilization of Cellulase on Eudragit L-100

    PubMed Central

    Zhang, Yu; Xu, Jing-Liang; Yuan, Zhen-Hong; Qi, Wei; Liu, Yun-Yun; He, Min-Chao

    2012-01-01

    Two artificial intelligence techniques, namely artificial neural network (ANN) and genetic algorithm (GA) were combined to be used as a tool for optimizing the covalent immobilization of cellulase on a smart polymer, Eudragit L-100. 1-Ethyl-3-(3-dimethyllaminopropyl) carbodiimide (EDC) concentration, N-hydroxysuccinimide (NHS) concentration and coupling time were taken as independent variables, and immobilization efficiency was taken as the response. The data of the central composite design were used to train ANN by back-propagation algorithm, and the result showed that the trained ANN fitted the data accurately (correlation coefficient R2 = 0.99). Then a maximum immobilization efficiency of 88.76% was searched by genetic algorithm at a EDC concentration of 0.44%, NHS concentration of 0.37% and a coupling time of 2.22 h, where the experimental value was 87.97 ± 6.45%. The application of ANN based optimization by GA is quite successful. PMID:22942683

  19. Computerized detection of breast cancer with artificial intelligence and thermograms.

    PubMed

    Ng, E Y-K; Fok, S C; Peh, Y C; Ng, F C; Sim, L S J

    2002-01-01

    This paper shows the concurrent use of thermography and artificial neural networks (ANN) for the diagnosis of breast cancer, a disease that is growing in prominence in women all over the world. It has been reported that breast thermography itself could detect breast cancer up to 10 years earlier than the conventional golden methods such as mammography, in particular in the younger patient. However, the accuracy of thermography is dependent on many factors such as the symmetry of the breasts' temperature and temperature stability. A woman's body temperature is known to be stable in certain periods after menstruation and it was found that the accuracy of thermography in women whose thermal images are taken in a suitable period (5th - 12th and 21st day of menstruation) is higher (80%) than the total population of patients (73%). The stability of the body temperature will depend on physiological state. This paper examines the use of ANN to complement the infrared heat radiating from the surface of the body with other physiological data. Four backpropagation neural networks were developed and trained using the results from the Singapore General Hospital patients' physiological data and thermographs. Owing to the inaccuracies found in thermography and the low population size gathered for this project, the networks developed could only accurately diagnose about 61.54% of the breast cancer cases. Nevertheless, the basic neural network framework has been established and it has great potential for future development of an intelligent breast cancer diagnosis system. This would be especially useful to the teenagers and young adults who are unsuitable for mammography at a young age. An intelligent breast thermography-neural network will be able to give an accurate diagnosis of breast cancer and can make a positive impact on breast disease detection.

  20. Northeast Artificial Intelligence Consortium (NAIC). volume 18. A distributed artificial intelligence approach to information fusion and object classification. Final report, Sep 84-Dec 89

    SciTech Connect

    Varshney, P.K.; Sikka, D.I.

    1990-12-01

    The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose was to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress during the existence of the NAIC on the technical research tasks undertaken at the member universities. The topics covered in general are: versatile expert system for equipment maintenance, distributed AI for communication system control, automatic photointerpretation, time-oriented problem solving, speech understanding systems, knowledge base maintenance, hardware architectures for very large systems, knowledge based reasoning and planning, and a knowledge acquisition, assistance, and explanation system. The specific topic for this volume is the application of distributed Artificial Intelligence tools to the data fusion and classification problem of modern C3I systems.

  1. Making Computers Smarter: A Look At the Controversial Field of Artificial Intelligence.

    ERIC Educational Resources Information Center

    Green, John O.

    1984-01-01

    Defines artificial intelligence (AI) and discusses its history; the current state of the art, research, experimentation, and practical applications; and probable future developments. Key dates in the history of AI and eight references are provided. (MBR)

  2. A Modular Artificial Intelligence Inference Engine System (MAIS) for support of on orbit experiments

    NASA Technical Reports Server (NTRS)

    Hancock, Thomas M., III

    1994-01-01

    This paper describes a Modular Artificial Intelligence Inference Engine System (MAIS) support tool that would provide health and status monitoring, cognitive replanning, analysis and support of on-orbit Space Station, Spacelab experiments and systems.

  3. Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

    NASA Astrophysics Data System (ADS)

    Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

    2016-06-01

    Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

  4. A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Genrang; Lin, ZhengChun

    The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

  5. An application of artificial intelligence to the interpretation of mass spectra.

    NASA Technical Reports Server (NTRS)

    Buchanan, B. G.; Duffield, A. M.; Robertson, A. V.

    1971-01-01

    Description of the DENDRAL (Dendritic Algorithm) project, the objectives of which were to base the computer program on an alogorithm that generates an exhaustive, nonredundant list of all the structural isomers of a given chemical composition, and to devise a computer program that would perform an organic structure determination, given a molecular formula and a mass spectrum. This program is called 'Heuristic DENDRAL' and it operates by using the known structure/spectrum correlations to constrain the DENDRAL isomer generator to produce a single isomer for that composition. The collaboration of chemists and computer scientists has produced a tool of some practical utility from the chemical viewpoint, and an interesting program from the viewpoint of artificial intelligence.

  6. Artificial Intelligence Techniques for the Berth Allocation and Container Stacking Problems in Container Terminals

    NASA Astrophysics Data System (ADS)

    Salido, Miguel A.; Rodriguez-Molins, Mario; Barber, Federico

    The Container Stacking Problem and the Berth Allocation Problem are two important problems in maritime container terminal's management which are clearly related. Terminal operators normally demand all containers to be loaded into an incoming vessel should be ready and easily accessible in the terminal before vessel's arrival. Similarly, customers (i.e., vessel owners) expect prompt berthing of their vessels upon arrival. In this paper, we present an artificial intelligence based-integrated system to relate these problems. Firstly, we develop a metaheuristic algorithm for berth allocation which generates an optimized order of vessel to be served according to existing berth constraints. Secondly, we develop a domain-oriented heuristic planner for calculating the number of reshuffles needed to allocate containers in the appropriate place for a given berth ordering of vessels. By combining these optimized solutions, terminal operators can be assisted to decide the most appropriated solution in each particular case.

  7. An overview of Space Communication Artificial Intelligence for Link Evaluation Terminal (SCAILET) Project

    NASA Technical Reports Server (NTRS)

    Shahidi, Anoosh K.; Schlegelmilch, Richard F.; Petrik, Edward J.; Walters, Jerry L.

    1991-01-01

    A software application to assist end-users of the link evaluation terminal (LET) for satellite communications is being developed. This software application incorporates artificial intelligence (AI) techniques and will be deployed as an interface to LET. The high burst rate (HBR) LET provides 30 GHz transmitting/20 GHz receiving (220/110 Mbps) capability for wideband communications technology experiments with the Advanced Communications Technology Satellite (ACTS). The HBR LET can monitor and evaluate the integrity of the HBR communications uplink and downlink to the ACTS satellite. The uplink HBR transmission is performed by bursting the bit-pattern as a modulated signal to the satellite. The HBR LET can determine the bit error rate (BER) under various atmospheric conditions by comparing the transmitted bit pattern with the received bit pattern. An algorithm for power augmentation will be applied to enhance the system's BER performance at reduced signal strength caused by adverse conditions.

  8. An artificial intelligence method for the synthesis of heat-integrated distillation flowsheets

    SciTech Connect

    Fang-Yu Han; Ben-Guang Rong

    1996-12-31

    In this paper, a concept of Heat-Integrated Distillation Flowsheet (HIDF) is proposed. It refers to the multicomponent separation flowsheet which is composed of simple and complex columns and in which heat integration is considered so as to achieve minimum energy consumption. The HIDF is very different from the Heat-Integrated Distillation Sequences (HIDS) in the literature which only consists of simple columns. In this paper, an artificial intelligence method is proposed for the synthesis of HIDF. It is based on the Database, Simulator, Heat Exchanger Network, and four Knowledge Bases which are all developed by the authors and their coworkers. A strategy of eight steps which combines the knowledge-based and rigorous algorithms is adopted for the synthesis of HIDF. The illustrated examples for the conventional distillation and extractive distillation show that the practical minimum energy consumption distillation flowsheets; can be generated with this method.

  9. Integration of artificial intelligence and numerical optimization techniques for the design of complex aerospace systems

    SciTech Connect

    Tong, S.S.; Powell, D.; Goel, S. GE Consulting Services, Albany, NY )

    1992-02-01

    A new software system called Engineous combines artificial intelligence and numerical methods for the design and optimization of complex aerospace systems. Engineous combines the advanced computational techniques of genetic algorithms, expert systems, and object-oriented programming with the conventional methods of numerical optimization and simulated annealing to create a design optimization environment that can be applied to computational models in various disciplines. Engineous has produced designs with higher predicted performance gains that current manual design processes - on average a 10-to-1 reduction of turnaround time - and has yielded new insights into product design. It has been applied to the aerodynamic preliminary design of an aircraft engine turbine, concurrent aerodynamic and mechanical preliminary design of an aircraft engine turbine blade and disk, a space superconductor generator, a satellite power converter, and a nuclear-powered satellite reactor and shield. 23 refs.

  10. An Artificial Intelligence Approach to Automatic Left Ventricular Border Detection in 2-D Echocardiography

    PubMed Central

    Buchan, Iris; Covvey, H. Dominic; Rakowski, Harry

    1985-01-01

    A program has been developed for left ventricular (LV) border tracking on ultrasound images. For each frame, forty border points at equally-spaced angles around the LV center are found gradually during three passes. Pass 1 uses adaptive thresholding to find the most obvious border points. Pass 2 then uses an artificial intelligence technique of finding possible border path segments, associating a score with each, and, from paths with superior scores, obtaining more of the border points. Pass 3 closes any remaining gaps by interpolation. The program tracks the LV border quite well in spite of dropout and interference from intracardiac structures, except during end-systole. Multi-level passes provide a very useful structure for border tracking, with increasingly slow but more sophisticated algorithms possible at higher levels for use when earlier passes recognise failure.

  11. Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach.

    PubMed

    Nettleton, D; Muñiz, J

    2001-09-01

    In this article, we revise and try to resolve some of the problems inherent in questionnaire screening of sleep apnea cases and apnea diagnosis based on attributes which are relevant and reliable. We present a way of learning information about the relevance of the data, comparing this with the definition of the information by the medical expert. We generate a predictive data model using a data aggregation operator which takes relevance and reliability information about the data into account to produce a diagnosis for each case. We also introduce a grade of membership for each question response which allows the patient to indicate a level of confidence or doubt in their own judgement. The method is tested with data collected from patients in a Sleep Clinic using questionnaires specially designed for the study. Other artificial intelligence predictive modeling algorithms are also tested on the same data and their predictive accuracy compared to that of the aggregation operator.

  12. Classifying epilepsy diseases using artificial neural networks and genetic algorithm.

    PubMed

    Koçer, Sabri; Canal, M Rahmi

    2011-08-01

    In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.

  13. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    PubMed

    Pathak, Lakshmi; Singh, Vineeta; Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C K M; Mishra, B N

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  14. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    PubMed Central

    Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C. K. M.; Mishra, B. N.

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500. PMID:26368924

  15. The role of artificial intelligence and expert systems in increasing STS operations productivity

    NASA Technical Reports Server (NTRS)

    Culbert, C.

    1985-01-01

    Artificial Intelligence (AI) is discussed. A number of the computer technologies pioneered in the AI world can make significant contributions to increasing STS operations productivity. Application of expert systems, natural language, speech recognition, and other key technologies can reduce manpower while raising productivity. Many aspects of STS support lend themselves to this type of automation. The artificial intelligence section of the mission planning and analysis division has developed a number of functioning prototype systems which demonstrate the potential gains of applying AI technology.

  16. The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

    NASA Technical Reports Server (NTRS)

    Hostetter, Carl F. (Editor)

    1995-01-01

    This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed.

  17. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    SciTech Connect

    Saini, K. K.; Saini, Sanju

    2008-10-07

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  18. Applications of artificial intelligence V; Proceedings of the Meeting, Orlando, FL, May 18-20, 1987

    NASA Technical Reports Server (NTRS)

    Gilmore, John F. (Editor)

    1987-01-01

    The papers contained in this volume focus on current trends in applications of artificial intelligence. Topics discussed include expert systems, image understanding, artificial intelligence tools, knowledge-based systems, heuristic systems, manufacturing applications, and image analysis. Papers are presented on expert system issues in automated, autonomous space vehicle rendezvous; traditional versus rule-based programming techniques; applications to the control of optional flight information; methodology for evaluating knowledge-based systems; and real-time advisory system for airborne early warning.

  19. Enhancing nuclear power plant performance through the use of artificial intelligence

    SciTech Connect

    Maren, A.J.; Miller, L.F.; Tsoukalas, L.H.; Uhrig, R.E.; Upadhyaya, B.R.

    1992-03-31

    The objective of this research was to advance the state-of-the-art of applying artificial intelligence technology (both expert systems and neural networks) to enhancing the performance (safety, efficiency, control and management) of nuclear power plants. A second, but equally important objective, was to build a broadly based critical mass of expertise in the artificial intelligence field that can be brought to bear on the technology of nuclear power plants.

  20. Building an artificial intelligence capability at Los Alamos

    SciTech Connect

    Marinuzzi, J.G.

    1988-01-01

    In 1985, after three years of preliminary work, Management of the Los Alamos National Laboratory started an ambitious program to develop a strong technical capability in the rapidly emerging field of Artificial Intelligence/Knowledge Based Systems (AI/KBS). When this AI development program began, except for a few staff members doing basic AI research, AI was essentially nonexistent at the laboratory. The basics, including such things as AI computer hardware and software, literature, books, knowledgeable personnel, or even a general knowledge of what AI was, were most difficult if not impossible to find. For this reason, we had to approach the problem with a very broad perspective, which strongly addressed the basics while aiming toward more advanced AI program elements. Broad, intensive education was the ''bootstrapping'' tool used in this five year, multi-million dollar AI capability development program. Halfway through the program, our accomplishments indicate that the program is extremely successful. In terms of trained staff, active programs and ''state-of-the-art equipment,'' we have developed one of the strongest AI technical capabilities within the Department of Energy (DOE) and the Department of Defense(DOD). However, a great deal more must be done before the full potential of the program can be realized. 1 fig.

  1. (Artificial intelligence, human factors, robotics, and computer simulation)

    SciTech Connect

    Spelt, P.F.

    1990-09-06

    Traveler was invited to participate in information exchange between Oak Ridge National Laboratory (ORNL) and CISC/JAERI on four topics: Artificial Intelligence, Human Factors, robotics, and computer simulation. This exchange took the form of 9 (2-hour) lectures presented by traveler on work done in CS HF Group, and four presentations by Japanese for traveler's edification. Seven of traveler's lectures were to CISC/JAERI, one to Toshiba Corporation, and one to the AI Steering Committee of JAERI. There was also a presentation by Toshiba Corporation on HF work connected with their Boiling Water Reactor (BWR) control room. Final discussion between traveler and JAERI personnel concerned an umbrella agreement with the US Department of Energy (DOE) permitting researcher exchange similar to nuclear researchers. Conclusions are: the US has definite advantages in most areas of AI progress; the Japanese are creating a Monte Carlo radiation dose calculation simulation which will operate at the level of radiating particles (neutrons) with doses calculated for all major organ systems of humans, and major circuits for robots; they are gaining experience in creating major integrated simulations of human/robot activity in a nuclear reactor; and that it would be advantageous for us to have a formal agreement permitting scientists to visit there for more than 15 days at a time.

  2. Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

    NASA Astrophysics Data System (ADS)

    Wali, W. A.; Hassan, K. H.; Cullen, J. D.; Al-Shamma'a, A. I.; Shaw, A.; Wylie, S. R.

    2011-08-01

    Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

  3. Classification of stilbenoid compounds by entropy of artificial intelligence.

    PubMed

    Castellano, Gloria; Lara, Ana; Torrens, Francisco

    2014-01-01

    A set of 66 stilbenoid compounds is classified into a system of periodic properties by using a procedure based on artificial intelligence, information entropy theory. Eight characteristics in hierarchical order are used to classify structurally the stilbenoids. The former five features mark the group or column while the latter three are used to indicate the row or period in the table of periodic classification. Those stilbenoids in the same group are suggested to present similar properties. Furthermore, compounds also in the same period will show maximum resemblance. In this report, the stilbenoids in the table are related to experimental data of bioactivity and antioxidant properties available in the technical literature. It should be noted that stilbenoids with glycoxyl groups esterified with benzoic acid derivatives, in the group g11000 in the extreme right of the periodic table, show the greatest antioxidant activity as confirmed by experiments in the bibliography. Moreover, the second group from the right (g10111) contains E-piceatannol, which antioxidant activity is recognized in the literature. The experiments confirm our results of the periodic classification.

  4. Applying artificial intelligence to clinical guidelines: the GLARE approach.

    PubMed

    Terenziani, Paolo; Montani, Stefania; Bottrighi, Alessio; Molino, Gianpaolo; Torchio, Mauro

    2008-01-01

    We present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines (GL). GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed. Second, a user-friendly acquisition tool, which provides expert physicians with various forms of help, has been implemented. Third, a tool for executing GL on a specific patient has been made available. At all the levels above, advanced AI techniques have been exploited, in order to enhance flexibility and user-friendliness and to provide decision support. Specifically, this chapter focuses on the methods we have developed in order to cope with (i) automatic resource-based adaptation of GL, (ii) representation and reasoning about temporal constraints in GL, (iii) decision making support, and (iv) model-based verification. We stress that, although we have devised such techniques within the GLARE project, they are mostly system-independent, so that they might be applied to other guideline management systems.

  5. Spike: Artificial intelligence scheduling for Hubble space telescope

    NASA Technical Reports Server (NTRS)

    Johnston, Mark; Miller, Glenn; Sponsler, Jeff; Vick, Shon; Jackson, Robert

    1990-01-01

    Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous interacting constraints. Artificial intelligence techniques were applied to the scheduling of the NASA/ESA Hubble Space Telescope (HST). This presents a particularly challenging problem since a yearlong observing program can contain some tens of thousands of exposures which are subject to a large number of scientific, operational, spacecraft, and environmental constraints. New techniques were developed for machine reasoning about scheduling constraints and goals, especially in cases where uncertainty is an important scheduling consideration and where resolving conflicts among conflicting preferences is essential. These technique were utilized in a set of workstation based scheduling tools (Spike) for HST. Graphical displays of activities, constraints, and schedules are an important feature of the system. High level scheduling strategies using both rule based and neural network approaches were developed. While the specific constraints implemented are those most relevant to HST, the framework developed is far more general and could easily handle other kinds of scheduling problems. The concept and implementation of the Spike system are described along with some experiments in adapting Spike to other spacecraft scheduling domains.

  6. An intercomparison of artificial intelligence approaches for polar scene identification

    SciTech Connect

    Tovinkere, V.R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R.C.; Berendes, T.A.; Welch, R.M. )

    1993-03-20

    Six advanced very high resolution radiometer local area coverage arctic scenes are classified into 10 classes. These include water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over ice, cumulus over water, and multilayer cloudiness. Eight spectral and textural features are computed. The textural features are based upon the gray level difference vector method. Six different artificial intelligence classifiers are examined: (1) the feed forward back propagation neural network; (2) the probabilistic neural network; (3) the hybrid back propagation neural network; (4) the [open quotes]don't care[close quotes] perceptron network; (5) the [open quotes]don't care[close quotes] back propagation neural network; and (6) a fuzzy logic-based expert system. Accuracies in excess of 95% are obtained for all but the hybrid neural network. The [open quotes]don't care[close quotes] back propagation neural network produces the highest accuracies and also has low CPU requirements. Thin fog/stratus over ice is the class consistently with the lowest accuracy, often misclassified as broken sea ice. Water, land, cirrus over ice, and snow-covered mountains are all classified with high accuracy ([ge]98%). The high accuracy achieved in the present study can be traced to (1) accurate classifiers; (2) an excellent choice for the feature vector, and (3) accurate labeling. A sophisticated new interactive visual image classification system is used for the labeling. 33 refs., 8 figs., 7 tabs.

  7. Advanced aerospace composite material structural design using artificial intelligent technology

    SciTech Connect

    Sun, S.H.; Chen, J.L.; Hwang, W.C.

    1993-12-31

    Due to the complexity in the prediction of property and behavior, composite material has not substituted for metal widely yet, though it has high specific-strength and high specific-modulus that are more important in the aerospace industry. In this paper two artificial intelligent techniques, the expert systems and neural network technology, were introduced to the structural design of composite material. Expert System which has good ability in symbolic processing can helps us to solve problem by saving experience and knowledge. It is, therefore, a reasonable way to combine expert system technology to tile composite structural design. The development of a prototype expert system to help designer during the process of composite structural design is presented. Neural network is a network similar to people`s brain that can simulate the thinking way of people and has the ability of learning from the training data by adapting the weights of network. Because of the bottleneck in knowledge acquisition processes, the application of neural network and its learning ability to strength design of composite structures are presented. Some examples are in this paper to demonstrate the idea.

  8. Man-made minds: The promise of artificial intelligence

    SciTech Connect

    Waldrop, M.M.

    1987-01-01

    Artificial Intelligence (AI), the art of designing machines that are both omniscient and sensible, has become the crowning challenge of today's information explosion. Physicist/science writer Waldrop explains the prodigious difficulties of endowing computers with the ability to learn, to reason, to understand. He reports on progress (and setbacks) at major AI research centers. Here scientists of many disciplines are beginning to simulate human vision, and they are designing AI programs to supervise ever more sophisticated industrial and communications systems. Underlying all AI research, the author points out, is the quest to understand the processes of human thought - which poses AI's most baffling question: Can a machine ever be made to actually think. At the practical level, AI is likely to give us within the next twenty years computer/robots that will converse in everyday language; provide expert advice on a wide spectrum of personal, professional, and business concerns; and serve as a responsive home tutor or an instant, world-wide librarian. Globally, AI is the ultimate prize in international technological competition. And in its expanding military role, AI, is being called on to supply the infallible mastermind for Star Wars ''defenses'' and other strategic systems.

  9. Theory of problem solving: a branch of artificial intelligence

    SciTech Connect

    Banerji, R.B.

    1982-12-01

    A mathematical model is given for the concepts of problem and solution, and related to activities in other branches of artificial intelligence (AI). Three techniques are described for guiding the search for the solution of a given problem: to wit, the branch and bound or a* technique of Hart, Nilsson, and Raphael (1968); the general problem solver (GPS) of Simon, Newell, and Shaw (1960); and the constraint satisfaction techniques developed by various authors. Of these, the first technique has been investigated with respect to the efficiency of search as a function of the accuracy of the bound. The results of these investigations are discussed. The first two techniques are dependent for their success in search reduction on the identification of certain functions (the bound in a*) and sets (differences in the GPS). The logical and algebraic techniques for their identification are indicated. The third technique so far has been applied to special classes of problems and includes some method of search reduction. The concepts are illustrated by a set of mathematical puzzles. Two-person perfect information games have been discussed in extended form. Search strategies for winning moves have been discussed from a perspective similar to that discussed earlier. 38 references.

  10. A grounded theory of abstraction in artificial intelligence.

    PubMed Central

    Zucker, Jean-Daniel

    2003-01-01

    In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed. PMID:12903672

  11. Artificial intelligence in the service of system administrators

    NASA Astrophysics Data System (ADS)

    Haen, C.; Barra, V.; Bonaccorsi, E.; Neufeld, N.

    2012-12-01

    The LHCb online system relies on a large and heterogeneous IT infrastructure made from thousands of servers on which many different applications are running. They run a great variety of tasks: critical ones such as data taking and secondary ones like web servers. The administration of such a system and making sure it is working properly represents a very important workload for the small expert-operator team. Research has been performed to try to automatize (some) system administration tasks, starting in 2001 when IBM defined the so-called “self objectives” supposed to lead to “autonomic computing”. In this context, we present a framework that makes use of artificial intelligence and machine learning to monitor and diagnose at a low level and in a non intrusive way Linux-based systems and their interaction with software. Moreover, the multi agent approach we use, coupled with an “object oriented paradigm” architecture should increase our learning speed a lot and highlight relations between problems.

  12. Macrocell path loss prediction using artificial intelligence techniques

    NASA Astrophysics Data System (ADS)

    Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.

    2014-04-01

    The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.

  13. Predicting production using a neural network (artificial intelligence beats human intelligence)

    SciTech Connect

    Boomer, R.J.

    1995-12-31

    Petroleum professionals, that is engineers and geoscientist, routinely make recommendations to drill wells. This process requires the generation of a production profile, oil producing rate versus time, which is a key component in the final economic decision of whether or not to drill the well. The success of the drilling program, and possibly the professional`s career, rests, to a high degree, upon the accuracy of this predicted production profile. The more accurate this prediction the more certain the economic value of the recommended drilling location. This paper looks at this predictive process as used in the Vacuum Field of New Mexico. It explores the predictive methods used in the past and reports on their accuracy. A new method, using artificial intelligence, is presented which improves upon the accuracy of this process.

  14. Aircraft sensor validation monitor and state estimator using artificial intelligence

    NASA Astrophysics Data System (ADS)

    Kwak, Seung-Keon; Yoon, Hwan-Sik

    2006-03-01

    A new Sensor Validity Monitoring, Verification, and Accommodation (SVMVA) technique based on an artificial neural network is developed for a self-repairing Flight Control System (FCS). For the proposed system, the Learning Vector Quantization (LVQ) method is employed as the on-line, real time learning, monitoring, and estimation tool. In order to conduct a feasibility study, we applied the developed algorithm to a flight vehicle simulator. The simulation results show that the proposed SVMVA with LVQ can instantly detect the failure of physical sensors and accommodate them for more than 30 minutes. By employing this type of analytical sensor redundancy, a flight vehicle can save power, weight, and space, which are required for installing redundant physical sensors.

  15. Using Artificial Intelligence in Education: Computer-Based Tools for Instructional Development.

    ERIC Educational Resources Information Center

    Perez, Ray S.; Seidel, Robert J.

    1990-01-01

    Discussion of the use of artificial intelligence in computer-based instruction focuses on training development for the U.S. Army. Topics discussed include the Systems Approach to Training (SAT); knowledge acquisition; domain expertise; intelligent computer-assisted instruction; software tools and decision aids; and expert systems. (10 references)…

  16. Monitoring of space station life support systems with miniature mass spectrometry and artificial intelligence

    NASA Technical Reports Server (NTRS)

    Yost, Richard A.; Johnson, Jodie V.; Wong, Carla M.

    1987-01-01

    The combination of quadrupole ion trap tandem mass spectroscopy with artificial intelligence is a promising approach for monitoring the performance of the life support systems in the space station. Such an analytical system can provide the selectivity, sensitivity, speed, small size, and decision making intelligence to detect, identify, and quantify trace toxic compounds which may accumulate in the space station habitat.

  17. A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

    PubMed Central

    Liu, Wen

    2014-01-01

    Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster. PMID:24982924

  18. Artificial Intelligence and the Brave New World of Eclipsing Binaries

    NASA Astrophysics Data System (ADS)

    Devinney, E.; Guinan, E.; Bradstreet, D.; DeGeorge, M.; Giammarco, J.; Alcock, C.; Engle, S.

    2005-12-01

    The explosive growth of observational capabilities and information technology over the past decade has brought astronomy to a tipping point - we are going to be deluged by a virtual fire hose (more like Niagara Falls!) of data. An important component of this deluge will be newly discovered eclipsing binary stars (EBs) and other valuable variable stars. As exploration of the Local Group Galaxies grows via current and new ground-based and satellite programs, the number of EBs is expected to grow explosively from some 10,000 today to 8 million as GAIA comes online. These observational advances will present a unique opportunity to study the properties of EBs formed in galaxies with vastly different dynamical, star formation, and chemical histories than our home Galaxy. Thus the study of these binaries (e.g., from light curve analyses) is expected to provide clues about the star formation rates and dynamics of their host galaxies as well as the possible effects of varying chemical abundance on stellar evolution and structure. Additionally, minimal-assumption-based distances to Local Group objects (and possibly 3-D mapping within these objects) shall be returned. These huge datasets of binary stars will provide tests of current theories (or suggest new theories) regarding binary star formation and evolution. However, these enormous data will far exceed the capabilities of analysis via human examination. To meet the daunting challenge of successfully mining this vast potential of EBs and variable stars for astrophysical results with minimum human intervention, we are developing new data processing techniques and methodologies. Faced with an overwhelming volume of data, our goal is to integrate technologies of Machine Learning and Pattern Processing (Artificial Intelligence [AI]) into the data processing pipelines of the major current and future ground- and space-based observational programs. Data pipelines of the future will have to carry us from observations to

  19. Modeling Common-Sense Decisions in Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Zak, Michail

    2010-01-01

    A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation

  20. Efficient implementation and application of the artificial bee colony algorithm to low-dimensional optimization problems

    NASA Astrophysics Data System (ADS)

    von Rudorff, Guido Falk; Wehmeyer, Christoph; Sebastiani, Daniel

    2014-06-01

    We adapt a swarm-intelligence-based optimization method (the artificial bee colony algorithm, ABC) to enhance its parallel scaling properties and to improve the escaping behavior from deep local minima. Specifically, we apply the approach to the geometry optimization of Lennard-Jones clusters. We illustrate the performance and the scaling properties of the parallelization scheme for several system sizes (5-20 particles). Our main findings are specific recommendations for ranges of the parameters of the ABC algorithm which yield maximal performance for Lennard-Jones clusters and Morse clusters. The suggested parameter ranges for these different interaction potentials turn out to be very similar; thus, we believe that our reported values are fairly general for the ABC algorithm applied to chemical optimization problems.

  1. Improving Search Algorithms by Using Intelligent Coordinates

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.; Tumer, Kagan; Bandari, Esfandiar

    2004-01-01

    We consider algorithms that maximize a global function G in a distributed manner, using a different adaptive computational agent to set each variable of the underlying space. Each agent eta is self-interested; it sets its variable to maximize its own function g (sub eta). Three factors govern such a distributed algorithm's performance, related to exploration/exploitation, game theory, and machine learning. We demonstrate how to exploit alI three factors by modifying a search algorithm's exploration stage: rather than random exploration, each coordinate of the search space is now controlled by a separate machine-learning-based player engaged in a noncooperative game. Experiments demonstrate that this modification improves simulated annealing (SA) by up to an order of magnitude for bin packing and for a model of an economic process run over an underlying network. These experiments also reveal interesting small-world phenomena.

  2. Optimization of well placement geothermal reservoirs using artificial intelligence

    NASA Astrophysics Data System (ADS)

    Akın, Serhat; Kok, Mustafa V.; Uraz, Irtek

    2010-06-01

    This research proposes a framework for determining the optimum location of an injection well using an inference method, artificial neural networks and a search algorithm to create a search space and locate the global maxima. A complex carbonate geothermal reservoir (Kizildere Geothermal field, Turkey) production history is used to evaluate the proposed framework. Neural networks are used as a tool to replicate the behavior of commercial simulators, by capturing the response of the field given a limited number of parameters such as temperature, pressure, injection location, and injection flow rate. A study on different network designs indicates that a combination of neural network and an optimization algorithm (explicit search with variable stepping) to capture local maxima can be used to locate a region or a location for optimum well placement. Results also indicate shortcomings and possible pitfalls associated with the approach. With the provided flexibility of the proposed workflow, it is possible to incorporate various parameters including injection flow rate, temperature, and location. For the field of study, optimum injection well location is found to be in the southeastern part of the field. Specific locations resulting from the workflow indicated a consistent search space, having higher values in that particular region. When studied with fixed flow rates (2500 and 4911 m 3/day), a search run through the whole field located two locations which are in the very same region resulting in consistent predictions. Further study carried out by incorporating effect of different flow rates indicates that the algorithm can be run in a particular region of interest and different flow rates may yield different locations. This analysis resulted with a new location in the same region and an optimum injection rate of 4000 m 3/day). It is observed that use of neural network, as a proxy to numerical simulator is viable for narrowing down or locating the area of interest for

  3. The use of artificially intelligent agents with bounded rationality in the study of economic markets

    SciTech Connect

    Rajan, V.; Slagle, J.R.

    1996-12-31

    The concepts of {open_quote}knowledge{close_quote} and {open_quote}rationality{close_quote} are of central importance to fields of science that are interested in human behavior and learning, such as artificial intelligence, economics, and psychology. The similarity between artificial intelligence and economics - both are concerned with intelligent thought, rational behavior, and the use and acquisition of knowledge - has led to the use of economic models as a paradigm for solving problems in distributed artificial intelligence (DAI) and multi agent systems (MAS). What we propose is the opposite; the use of artificial intelligence in the study of economic markets. Over the centuries various theories of market behavior have been advanced. The prevailing theory holds that an asset`s current price converges to the risk adjusted value of the rationally expected dividend stream. While this rational expectations model holds in equilibrium or near-equilibrium conditions, it does not sufficiently explain conditions of market disequilibrium. An example of market disequilibrium is the phenomenon of a speculative bubble. We present an example of using artificially intelligent agents with bounded rationality in the study of speculative bubbles.

  4. Evaluation of an artificial intelligence program for estimating occupational exposures.

    PubMed

    Johnston, Karen L; Phillips, Margaret L; Esmen, Nurtan A; Hall, Thomas A

    2005-03-01

    Estimation and Assessment of Substance Exposure (EASE) is an artificial intelligence program developed by UK's Health and Safety Executive to assess exposure. EASE computes estimated airborne concentrations based on a substance's vapor pressure and the types of controls in the work area. Though EASE is intended only to make broad predictions of exposure from occupational environments, some occupational hygienists might attempt to use EASE for individual exposure characterizations. This study investigated whether EASE would accurately predict actual sampling results from a chemical manufacturing process. Personal breathing zone time-weighted average (TWA) monitoring data for two volatile organic chemicals--a common solvent (toluene) and a specialty monomer (chloroprene)--present in this manufacturing process were compared to EASE-generated estimates. EASE-estimated concentrations for specific tasks were weighted by task durations reported in the monitoring record to yield TWA estimates from EASE that could be directly compared to the measured TWA data. Two hundred and six chloroprene and toluene full-shift personal samples were selected from eight areas of this manufacturing process. The Spearman correlation between EASE TWA estimates and measured TWA values was 0.55 for chloroprene and 0.44 for toluene, indicating moderate predictive values for both compounds. For toluene, the interquartile range of EASE estimates at least partially overlapped the interquartile range of the measured data distributions in all process areas. The interquartile range of EASE estimates for chloroprene fell above the interquartile range of the measured data distributions in one process area, partially overlapped the third quartile of the measured data in five process areas and fell within the interquartile range in two process areas. EASE is not a substitute for actual exposure monitoring. However, EASE can be used in conditions that cannot otherwise be sampled and in preliminary

  5. The application of artificial intelligence to astronomical scheduling problems

    NASA Technical Reports Server (NTRS)

    Johnston, Mark D.

    1992-01-01

    Efficient utilization of expensive space- and ground-based observatories is an important goal for the astronomical community; the cost of modern observing facilities is enormous, and the available observing time is much less than the demand from astronomers around the world. The complexity and variety of scheduling constraints and goals has led several groups to investigate how artificial intelligence (AI) techniques might help solve these kinds of problems. The earliest and most successful of these projects was started at Space Telescope Science Institute in 1987 and has led to the development of the Spike scheduling system to support the scheduling of Hubble Space Telescope (HST). The aim of Spike at STScI is to allocate observations to timescales of days to a week observing all scheduling constraints and maximizing preferences that help ensure that observations are made at optimal times. Spike has been in use operationally for HST since shortly after the observatory was launched in Apr. 1990. Although developed specifically for HST scheduling, Spike was carefully designed to provide a general framework for similar (activity-based) scheduling problems. In particular, the tasks to be scheduled are defined in the system in general terms, and no assumptions about the scheduling timescale are built in. The mechanisms for describing, combining, and propagating temporal and other constraints and preferences are quite general. The success of this approach has been demonstrated by the application of Spike to the scheduling of other satellite observatories: changes to the system are required only in the specific constraints that apply, and not in the framework itself. In particular, the Spike framework is sufficiently flexible to handle both long-term and short-term scheduling, on timescales of years down to minutes or less. This talk will discuss recent progress made in scheduling search techniques, the lessons learned from early HST operations, the application of Spike

  6. Application of Artificial Intelligence For Euler Solutions Clustering

    NASA Astrophysics Data System (ADS)

    Mikhailov, V.; Galdeano, A.; Diament, M.; Gvishiani, A.; Agayan, S.; Bogoutdinov, Sh.; Graeva, E.; Sailhac, P.

    Results of Euler deconvolution strongly depend on the selection of viable solutions. Synthetic calculations using multiple causative sources show that Euler solutions clus- ter in the vicinity of causative bodies even when they do not group densely about perimeter of the bodies. We have developed a clustering technique to serve as a tool for selecting appropriate solutions. The method RODIN, employed in this study, is based on artificial intelligence and was originally designed for problems of classification of large data sets. It is based on a geometrical approach to study object concentration in a finite metric space of any dimension. The method uses a formal definition of cluster and includes free parameters that facilitate the search for clusters of given proper- ties. Test on synthetic and real data showed that the clustering technique successfully outlines causative bodies more accurate than other methods of discriminating Euler solutions. In complicated field cases such as the magnetic field in the Gulf of Saint Malo region (Brittany, France), the method provides geologically insightful solutions. Other advantages of the clustering method application are: - Clusters provide solutions associated with particular bodies or parts of bodies permitting the analysis of different clusters of Euler solutions separately. This may allow computation of average param- eters for individual causative bodies. - Those measurements of the anomalous field that yield clusters also form dense clusters themselves. The application of cluster- ing technique thus outlines areas where the influence of different causative sources is more prominent. This allows one to focus on areas for reinterpretation, using different window sizes, structural indices and so on.

  7. An intelligent allocation algorithm for parallel processing

    NASA Technical Reports Server (NTRS)

    Carroll, Chester C.; Homaifar, Abdollah; Ananthram, Kishan G.

    1988-01-01

    The problem of allocating nodes of a program graph to processors in a parallel processing architecture is considered. The algorithm is based on critical path analysis, some allocation heuristics, and the execution granularity of nodes in a program graph. These factors, and the structure of interprocessor communication network, influence the allocation. To achieve realistic estimations of the executive durations of allocations, the algorithm considers the fact that nodes in a program graph have to communicate through varying numbers of tokens. Coarse and fine granularities have been implemented, with interprocessor token-communication duration, varying from zero up to values comparable to the execution durations of individual nodes. The effect on allocation of communication network structures is demonstrated by performing allocations for crossbar (non-blocking) and star (blocking) networks. The algorithm assumes the availability of as many processors as it needs for the optimal allocation of any program graph. Hence, the focus of allocation has been on varying token-communication durations rather than varying the number of processors. The algorithm always utilizes as many processors as necessary for the optimal allocation of any program graph, depending upon granularity and characteristics of the interprocessor communication network.

  8. Improved artificial bee colony algorithm for wavefront sensor-less system in free space optical communication

    NASA Astrophysics Data System (ADS)

    Niu, Chaojun; Han, Xiang'e.

    2015-10-01

    Adaptive optics (AO) technology is an effective way to alleviate the effect of turbulence on free space optical communication (FSO). A new adaptive compensation method can be used without a wave-front sensor. Artificial bee colony algorithm (ABC) is a population-based heuristic evolutionary algorithm inspired by the intelligent foraging behaviour of the honeybee swarm with the advantage of simple, good convergence rate, robust and less parameter setting. In this paper, we simulate the application of the improved ABC to correct the distorted wavefront and proved its effectiveness. Then we simulate the application of ABC algorithm, differential evolution (DE) algorithm and stochastic parallel gradient descent (SPGD) algorithm to the FSO system and analyze the wavefront correction capabilities by comparison of the coupling efficiency, the error rate and the intensity fluctuation in different turbulence before and after the correction. The results show that the ABC algorithm has much faster correction speed than DE algorithm and better correct ability for strong turbulence than SPGD algorithm. Intensity fluctuation can be effectively reduced in strong turbulence, but not so effective in week turbulence.

  9. A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems.

    PubMed

    Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro

    2015-06-01

    The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively.

  10. A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems.

    PubMed

    Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro

    2015-06-01

    The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively. PMID:25982071

  11. A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps

    PubMed Central

    Mao, Wei; Li, Hao-ru

    2016-01-01

    As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions. PMID:27293426

  12. A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps.

    PubMed

    Mao, Wei; Lan, Heng-You; Li, Hao-Ru

    2016-01-01

    As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions. PMID:27293426

  13. Accelerating progress in Artificial General Intelligence: Choosing a benchmark for natural world interaction

    NASA Astrophysics Data System (ADS)

    Rohrer, Brandon

    2010-12-01

    Measuring progress in the field of Artificial General Intelligence (AGI) can be difficult without commonly accepted methods of evaluation. An AGI benchmark would allow evaluation and comparison of the many computational intelligence algorithms that have been developed. In this paper I propose that a benchmark for natural world interaction would possess seven key characteristics: fitness, breadth, specificity, low cost, simplicity, range, and task focus. I also outline two benchmark examples that meet most of these criteria. In the first, the direction task, a human coach directs a machine to perform a novel task in an unfamiliar environment. The direction task is extremely broad, but may be idealistic. In the second, the AGI battery, AGI candidates are evaluated based on their performance on a collection of more specific tasks. The AGI battery is designed to be appropriate to the capabilities of currently existing systems. Both the direction task and the AGI battery would require further definition before implementing. The paper concludes with a description of a task that might be included in the AGI battery: the search and retrieve task.

  14. Parallel artificial-intelligence search techniques for real-time applications. Master's thesis

    SciTech Connect

    Shakley, D.J.

    1987-12-01

    State-space search is an important component of many problem-solving methodologies. The computational models within Artificial Intelligence depend heavily upon state-space searches. Production systems are one such computational model. Production systems are being explored for real-time environments where timing is of a critical nature. Parallel processing of these systems and, in particular, concurrent state-space searching seems to provide a promising method to increase the performance (effective and efficient) of production systems in the real-time environment. Production systems in the form of expert systems, for example, are being used to govern the intelligent control of the Robotic Air Vehicle (RAV) which is currently a research project at the Air Force Wright Aeronautical Laboratories. Due to the nature of the RAV system, the associated expert system needs to perform in a demanding real-time environment. The use of a parallel-processing capability to support the associated computational requirement may be critical in this application. Thus, parallel search algorithms for real-time expert systems are designed, analyzed, and synthesized on the Texas Instruments (TI) Explorer and Intel Hypercube.

  15. Worldwide Intelligent Systems: Approaches to Telecommunications and Network Management. Frontiers in Artificial Intelligence and Applications, Volume 24.

    ERIC Educational Resources Information Center

    Liebowitz, Jay, Ed.; Prerau, David S., Ed.

    This is an international collection of 12 papers addressing artificial intelligence (AI) and knowledge technology applications in telecommunications and network management. It covers the latest and emerging AI technologies as applied to the telecommunications field. The papers are: "The Potential for Knowledge Technology in Telecommunications: A…

  16. Decision-Making and the Interface between Human Intelligence and Artificial Intelligence. AIR 1987 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Henard, Ralph E.

    Possible future developments in artificial intelligence (AI) as well as its limitations are considered that have implications for institutional research in higher education, and especially decision making and decision support systems. It is noted that computer software programs have been developed that store knowledge and mimic the decision-making…

  17. A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.

    PubMed

    Gao, Yanbin; Guan, Lianwu; Wang, Tingjun; Sun, Yunlong

    2015-05-05

    The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes' pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

  18. A Novel Artificial Fish Swarm Algorithm for Recalibration of Fiber Optic Gyroscope Error Parameters

    PubMed Central

    Gao, Yanbin; Guan, Lianwu; Wang, Tingjun; Sun, Yunlong

    2015-01-01

    The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes’ pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration. PMID:25951339

  19. A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.

    PubMed

    Gao, Yanbin; Guan, Lianwu; Wang, Tingjun; Sun, Yunlong

    2015-01-01

    The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes' pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration. PMID:25951339

  20. Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

    NASA Astrophysics Data System (ADS)

    Kong, Changduk; Lim, Semyeong

    2011-12-01

    Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

  1. Hybrid Intelligent Perception System: Intelligent perception through combining Artificial Neural Networks and an Expert System

    SciTech Connect

    Glover, C.W.; Spelt, P.F.

    1990-01-01

    This paper presents a report of work-in-progress on a project to combine Artificial Neural Networks (ANNs) and Expert Systems (ESs) into a hybrid, self-improving pattern recognition system. The purpose of this project is to explore methods of combining multiple classifiers into a Hybrid Intelligent Perception (HIP) System. The central research issue to be addressed for a multiclassifier hybrid system is whether such a system can perform better than the two classifiers taken by themselves. ANNs and ESs have different strengths and weaknesses, which are being exploited in this project in such a way that they are complementary to each other: Strengths in one system make up for weaknesses in the other, and vice versa. There is presently considerable interest in the AI community in ways to exploit the strengths of these methodologies to produce an intelligent system which is more robust and flexible than one using either technology alone. Perception, which involves both data-driven (bottom-up) and concept-driven (top-down) processing, is a process which seems especially well-suited to displaying the capabilities of such a hybrid system. This work has been funded for the past six months by an Oak Ridge National Laboratory seed grant, and most of the system components are operating in both the PC and the hypercube computer environments. Here we report on the efforts to develop the low-level ANNs and a graphic representation of their knowledge, and discuss ways of using an ES to integrate and supervise the entire system. 11 refs., 3 figs.

  2. Artificial bee colony algorithm for solving optimal power flow problem.

    PubMed

    Le Dinh, Luong; Vo Ngoc, Dieu; Vasant, Pandian

    2013-01-01

    This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem.

  3. Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem

    PubMed Central

    Le Dinh, Luong; Vo Ngoc, Dieu

    2013-01-01

    This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem. PMID:24470790

  4. Importance of nonverbal expression to the emergence of emotive artificial intelligence systems

    NASA Astrophysics Data System (ADS)

    Pioggia, Giovanni; Hanson, David; Dinelli, Serena; Di Francesco, Fabio; Francesconi, R.; De Rossi, Danilo

    2002-07-01

    The nonverbal expression of the emotions, especially in the human face, has rapidly become an area of intense interest in computer science and robotics. Exploring the emotions as a link between external events and behavioural responses, artificial intelligence designers and psychologists are approaching a theoretical understanding of foundational principles which will be key to the physical embodiment of artificial intelligence. In fact, it has been well demonstrated that many important aspects of intelligence are grounded in intimate communication with the physical world- so-called embodied intelligence . It follows naturally, then, that recent advances in emotive artificial intelligence show clear and undeniable broadening in the capacities of biologically-inspired robots to survive and thrive in a social environment. The means by which AI may express its foundling emotions are clearly integral to such capacities. In effect: powerful facial expressions are critical to the development of intelligent, sociable robots. Following discussion the importance of the nonverbal expression of emotions in humans and robots, this paper describes methods used in robotically emulating nonverbal expressions using human-like robotic faces. Furthermore, it describes the potentially revolutionary impact of electroactive polymer (EAP) actuators as artificial muscles for such robotic devices.

  5. Artificial immune algorithm for multi-depot vehicle scheduling problems

    NASA Astrophysics Data System (ADS)

    Wu, Zhongyi; Wang, Donggen; Xia, Linyuan; Chen, Xiaoling

    2008-10-01

    In the fast-developing logistics and supply chain management fields, one of the key problems in the decision support system is that how to arrange, for a lot of customers and suppliers, the supplier-to-customer assignment and produce a detailed supply schedule under a set of constraints. Solutions to the multi-depot vehicle scheduling problems (MDVRP) help in solving this problem in case of transportation applications. The objective of the MDVSP is to minimize the total distance covered by all vehicles, which can be considered as delivery costs or time consumption. The MDVSP is one of nondeterministic polynomial-time hard (NP-hard) problem which cannot be solved to optimality within polynomial bounded computational time. Many different approaches have been developed to tackle MDVSP, such as exact algorithm (EA), one-stage approach (OSA), two-phase heuristic method (TPHM), tabu search algorithm (TSA), genetic algorithm (GA) and hierarchical multiplex structure (HIMS). Most of the methods mentioned above are time consuming and have high risk to result in local optimum. In this paper, a new search algorithm is proposed to solve MDVSP based on Artificial Immune Systems (AIS), which are inspirited by vertebrate immune systems. The proposed AIS algorithm is tested with 30 customers and 6 vehicles located in 3 depots. Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving MDVSP problems.

  6. Improving search algorithms by using intelligent coordinates

    NASA Astrophysics Data System (ADS)

    Wolpert, David; Tumer, Kagan; Bandari, Esfandiar

    2004-01-01

    We consider algorithms that maximize a global function G in a distributed manner, using a different adaptive computational agent to set each variable of the underlying space. Each agent η is self-interested; it sets its variable to maximize its own function gη. Three factors govern such a distributed algorithm’s performance, related to exploration/exploitation, game theory, and machine learning. We demonstrate how to exploit all three factors by modifying a search algorithm’s exploration stage: rather than random exploration, each coordinate of the search space is now controlled by a separate machine-learning-based “player” engaged in a noncooperative game. Experiments demonstrate that this modification improves simulated annealing (SA) by up to an order of magnitude for bin packing and for a model of an economic process run over an underlying network. These experiments also reveal interesting small-world phenomena.

  7. Artificial intelligence and design: Opportunities, research problems and directions

    NASA Technical Reports Server (NTRS)

    Amarel, Saul

    1990-01-01

    The issues of industrial productivity and economic competitiveness are of major significance in the U.S. at present. By advancing the science of design, and by creating a broad computer-based methodology for automating the design of artifacts and of industrial processes, we can attain dramatic improvements in productivity. It is our thesis that developments in computer science, especially in Artificial Intelligence (AI) and in related areas of advanced computing, provide us with a unique opportunity to push beyond the present level of computer aided automation technology and to attain substantial advances in the understanding and mechanization of design processes. To attain these goals, we need to build on top of the present state of AI, and to accelerate research and development in areas that are especially relevant to design problems of realistic complexity. We propose an approach to the special challenges in this area, which combines 'core work' in AI with the development of systems for handling significant design tasks. We discuss the general nature of design problems, the scientific issues involved in studying them with the help of AI approaches, and the methodological/technical issues that one must face in developing AI systems for handling advanced design tasks. Looking at basic work in AI from the perspective of design automation, we identify a number of research problems that need special attention. These include finding solution methods for handling multiple interacting goals, formation problems, problem decompositions, and redesign problems; choosing representations for design problems with emphasis on the concept of a design record; and developing approaches for the acquisition and structuring of domain knowledge with emphasis on finding useful approximations to domain theories. Progress in handling these research problems will have major impact both on our understanding of design processes and their automation, and also on several fundamental questions

  8. Artificial intelligent techniques for optimizing water allocation in a reservoir watershed

    NASA Astrophysics Data System (ADS)

    Chang, Fi-John; Chang, Li-Chiu; Wang, Yu-Chung

    2014-05-01

    This study proposes a systematical water allocation scheme that integrates system analysis with artificial intelligence techniques for reservoir operation in consideration of the great uncertainty upon hydrometeorology for mitigating droughts impacts on public and irrigation sectors. The AI techniques mainly include a genetic algorithm and adaptive-network based fuzzy inference system (ANFIS). We first derive evaluation diagrams through systematic interactive evaluations on long-term hydrological data to provide a clear simulation perspective of all possible drought conditions tagged with their corresponding water shortages; then search the optimal reservoir operating histogram using genetic algorithm (GA) based on given demands and hydrological conditions that can be recognized as the optimal base of input-output training patterns for modelling; and finally build a suitable water allocation scheme through constructing an adaptive neuro-fuzzy inference system (ANFIS) model with a learning of the mechanism between designed inputs (water discount rates and hydrological conditions) and outputs (two scenarios: simulated and optimized water deficiency levels). The effectiveness of the proposed approach is tested on the operation of the Shihmen Reservoir in northern Taiwan for the first paddy crop in the study area to assess the water allocation mechanism during drought periods. We demonstrate that the proposed water allocation scheme significantly and substantially avails water managers of reliably determining a suitable discount rate on water supply for both irrigation and public sectors, and thus can reduce the drought risk and the compensation amount induced by making restrictions on agricultural use water.

  9. Testing the applicability of artificial intelligence techniques to the subject of erythemal ultraviolet solar radiation. Part two: an intelligent system based on multi-classifier technique.

    PubMed

    Elminir, Hamdy K; Own, Hala S; Azzam, Yosry A; Riad, A M

    2008-03-28

    The problem we address here describes the on-going research effort that takes place to shed light on the applicability of using artificial intelligence techniques to predict the local noon erythemal UV irradiance in the plain areas of Egypt. In light of this fact, we use the bootstrap aggregating (bagging) algorithm to improve the prediction accuracy reported by a multi-layer perceptron (MLP) network. The results showed that, the overall prediction accuracy for the MLP network was only 80.9%. When bagging algorithm is used, the accuracy reached 94.8%; an improvement of about 13.9% was achieved. These improvements demonstrate the efficiency of the bagging procedure, and may be used as a promising tool at least for the plain areas of Egypt.

  10. Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

    PubMed Central

    Ni, Jianjun; Wu, Liuying; Fan, Xinnan; Yang, Simon X.

    2016-01-01

    Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research. PMID:26819582

  11. Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey.

    PubMed

    Ni, Jianjun; Wu, Liuying; Fan, Xinnan; Yang, Simon X

    2016-01-01

    Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research. PMID:26819582

  12. Optimization of the operational parameters in a fast axial flow CW CO 2 laser using artificial neural networks and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Adineh, V. R.; Aghanajafi, C.; Dehghan, G. H.; Jelvani, S.

    2008-11-01

    This paper presents an artificial intelligence approach for optimization of the operational parameters such as gas pressure ratio and discharge current in a fast-axial-flow CW CO 2 laser by coupling artificial neural networks and genetic algorithm. First, a series of experiments were used as the learning data for artificial neural networks. The best-trained network was connected to genetic algorithm as a fitness function to find the optimum parameters. After the optimization, the calculated laser power increases by 33% and the measured value increases by 21% in an experiment as compared to a non-optimized case.

  13. RAVEN: a GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework

    SciTech Connect

    C. Rabiti; D. Mandelli; A. Alfonsi; J. Cogliati; R. Kinoshita; D. Gaston; R. Martineau; C. Curtis

    2013-06-01

    Increases in computational power and pressure for more accurate simulations and estimations of accident scenario consequences are driving the need for Dynamic Probabilistic Risk Assessment (PRA) [1] of very complex models. While more sophisticated algorithms and computational power address the back end of this challenge, the front end is still handled by engineers that need to extract meaningful information from the large amount of data and build these complex models. Compounding this problem is the difficulty in knowledge transfer and retention, and the increasing speed of software development. The above-described issues would have negatively impacted deployment of the new high fidelity plant simulator RELAP-7 (Reactor Excursion and Leak Analysis Program) at Idaho National Laboratory. Therefore, RAVEN that was initially focused to be the plant controller for RELAP-7 will help mitigate future RELAP-7 software engineering risks. In order to accomplish this task, Reactor Analysis and Virtual Control Environment (RAVEN) has been designed to provide an easy to use Graphical User Interface (GUI) for building plant models and to leverage artificial intelligence algorithms in order to reduce computational time, improve results, and help the user to identify the behavioral pattern of the Nuclear Power Plants (NPPs). In this paper we will present the GUI implementation and its current capability status. We will also introduce the support vector machine algorithms and show our evaluation of their potentiality in increasing the accuracy and reducing the computational costs of PRA analysis. In this evaluation we will refer to preliminary studies performed under the Risk Informed Safety Margins Characterization (RISMC) project of the Light Water Reactors Sustainability (LWRS) campaign [3]. RISMC simulation needs and algorithm testing are currently used as a guidance to prioritize RAVEN developments relevant to PRA.

  14. Fourth-generation inverters add artificial intelligence to the control of GMA welding

    SciTech Connect

    Nacey, T.J. . Panasonic Factory Automation Co.)

    1993-01-01

    A new level of control has been achieved over the welding arc by using some of the latest techniques in artificial machine-intelligence computer control and by direct control of the short-circuit waveform. By controlling the short-circuit waveform in real time, these artificially intelligent power supplies control the instantaneous welding conditions and improve the GMAW process in terms of welding performance. These artificial-intelligent power supplies can yield many benefits. Among them are lower spatter levels, both in argon-based shielding gases and 100% CO[sub 2] shielding gases. Another benefit is faster arc speeds. In many cases the arc speeds can be 25% faster. Better productivity, through the better arc-striking capability and reduction of downtime associated with spatter on peripheral equipment, is another important factor. These power supplies are synergic and therefore provide for easier operator control. In addition, they have higher electrical efficiency; thus they yield lower operating cost.

  15. New Trends in Computing Anticipatory Systems : Emergence of Artificial Conscious Intelligence with Machine Learning Natural Language

    NASA Astrophysics Data System (ADS)

    Dubois, Daniel M.

    2008-10-01

    This paper deals with the challenge to create an Artificial Intelligence System with an Artificial Consciousness. For that, an introduction to computing anticipatory systems is presented, with the definitions of strong and weak anticipation. The quasi-anticipatory systems of Robert Rosen are linked to open-loop controllers. Then, some properties of the natural brain are presented in relation to the triune brain theory of Paul D. MacLean, and the mind time of Benjamin Libet, with his veto of the free will. The theory of the hyperincursive discrete anticipatory systems is recalled in view to introduce the concept of hyperincursive free will, which gives a similar veto mechanism: free will as unpredictable hyperincursive anticipation The concepts of endo-anticipation and exo-anticipation are then defined. Finally, some ideas about artificial conscious intelligence with natural language are presented, in relation to the Turing Machine, Formal Language, Intelligent Agents and Mutli-Agent System.

  16. Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

    NASA Astrophysics Data System (ADS)

    Chen, Wei

    2015-07-01

    In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

  17. An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch.

    PubMed

    Yurtkuran, Alkın; Emel, Erdal

    2016-01-01

    The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature. PMID:26819591

  18. An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch

    PubMed Central

    Yurtkuran, Alkın

    2016-01-01

    The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature. PMID:26819591

  19. An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch.

    PubMed

    Yurtkuran, Alkın; Emel, Erdal

    2016-01-01

    The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.

  20. A Paradigmatic Example of an Artificially Intelligent Instructional System.

    ERIC Educational Resources Information Center

    Brown, John Seely; Burton, Richard R.

    1978-01-01

    Describes the philosophy of intelligent instructional systems and presents an example of such a system, BLOCKS. The notion of BLOCKS as a paradigmatic system is explicated from both the system development and educational points of view. (Author/VT)

  1. Proceedings of the thirteenth national conference on artificial intelligence and the eighth innovative applications of artificial intelligence conference. Volume 1 and 2

    SciTech Connect

    1996-12-31

    This report contain papers from the Thirteenth National Conference on Artificial Intelligence and the Eighth Conference on Innovative Applications of Artificial Intelligence collected in two volumes. General areas of research for these papers are: interaction; internet agents; multiagent learning; multiagent problem solving; negotiation and coalition; AI in art and entertainment; constraint satisfaction; data consistency; game-tree search; phase transition; search control; search and learning; stochastic search; temporal reasoning; education; information retrieval and natural language processing; knowledge-based systems; knowledge compilation; knowledge representation; belief and belief revision; description logics and probabilities reasoning; knowledge-base and context; nonmonotonic reasoning; reasoning about action; learning; mobile robots; model-based reasoning; natural language; preception; planning; rule-based reasoning and connectionism; uncertainty; robot competition; student abstracts; and case studies.

  2. Integration of artificial intelligence applications in the EMS: Issues and solutions

    SciTech Connect

    Bann, J.; Irisarri, G.; Kirschen, D.; Miller, B.; Mokhtari, S.

    1996-02-01

    This paper discusses the issues which must be addressed when integrating Artificial Intelligence (AI) and, in particular, expert system applications in an Energy Management System (EMS) environment. It is argued that these issues can be resolved by creating an environment which supports all the interfaces between the Artificial Intelligence (AI) applications and the EMS. This environment should also be responsible for maintaining a model of the power system common to all the AI applications. Once this environment has been created, AI applications can be easily plugged into the EMS. The design of such an environment is described and case studies of its implementation are provided to illustrate its flexibility.

  3. Survey of artificial intelligence methods for detection and identification of component faults in nuclear power plants

    SciTech Connect

    Reifman, J.

    1997-07-01

    A comprehensive survey of computer-based systems that apply artificial intelligence methods to detect and identify component faults in nuclear power plants is presented. Classification criteria are established that categorize artificial intelligence diagnostic systems according to the types of computing approaches used (e.g., computing tools, computer languages, and shell and simulation programs), the types of methodologies employed (e.g., types of knowledge, reasoning and inference mechanisms, and diagnostic approach), and the scope of the system. The major issues of process diagnostics and computer-based diagnostic systems are identified and cross-correlated with the various categories used for classification. Ninety-five publications are reviewed.

  4. Northeast Artificial Intelligence Consortium (NAIC). Volume 1. Executive Summary. Final report, Sep 84-Dec 89

    SciTech Connect

    Weiss, V.; Brule, J.F.

    1990-12-01

    The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose was to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress during the existence of the NAIC on the technical research tasks undertaken at the member universities. The topics covered in general are: versatile expert system for equipment maintenance, distributed AI for communications systems control, automatic photointerpretation, time-oriented problem solving, speech understanding systems, knowledge-based reasoning and planning, and a knowledge acquisition, assistance, and explanation system. This volume provides the Executive Summary of the NAIC.

  5. Integration of artificial intelligence applications in the EMS: Issues and solutions

    SciTech Connect

    Bann, J.; Irisarri, G.; Kirschen, D.; Miller, B.; Mokhtari, S.

    1995-12-31

    This paper discusses the issues which must be addressed when integrating Artificial Intelligence (AI) and, in particular, expert system applications in an Energy Management System (EMS) environment. It is argued that these issues can be resolved by creating an environment which supports all the interfaces between the Artificial Intelligence (AI) applications and the EMS. This environment should also be responsible for maintaining a model of the power system common to all the AI applications. Once this environment has been created, AI applications can be easily plugged into the EMS. The design of such an environment is described and case studies of its implementation are provided to illustrate its flexibility.

  6. 1988 Goddard Conference on Space Applications of Artificial Intelligence, Greenbelt, MD, May 24, 1988, Proceedings

    NASA Technical Reports Server (NTRS)

    Rash, James L. (Editor)

    1988-01-01

    This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools methodologies.

  7. Practical systems use natural languages and store human expertise (artificial intelligence)

    SciTech Connect

    Evanczuk, S.; Manuel, T.

    1983-12-01

    For earlier articles see T. Manuel et al., ibid., vol.56, no.22, p.127-37. This second part of a special report on commercial applications of artificial intelligence examines the milestones which mark this major new path for the software industry. It covers state-space search, the problem of ambiguity, augmented transition networks, early commercial products, current and expected personal computer software, natural-language interfaces, research projects, knowledge engineering, the workings of artificial-intelligence-based applications programs, LISP, attributes and object orientation.

  8. Smart Collections: Can Artificial Intelligence Tools and Techniques Assist with Discovering, Evaluating and Tagging Digital Learning Resources?

    ERIC Educational Resources Information Center

    Leibbrandt, Richard; Yang, Dongqiang; Pfitzner, Darius; Powers, David; Mitchell, Pru; Hayman, Sarah; Eddy, Helen

    2010-01-01

    This paper reports on a joint proof of concept project undertaken by researchers from the Flinders University Artificial Intelligence Laboratory in partnership with information managers from the Education Network Australia (edna) team at Education Services Australia to address the question of whether artificial intelligence techniques could be…

  9. United States Army Training and Doctrine Command (TRADOC) - artificial intelligence and robotics symposium

    SciTech Connect

    Not Available

    1985-01-01

    Various papers on artificial intelligence and robotics and their applications for the US Army are presented. Topics include US Army robotics development directions; mobile robots for surveillance, reconnaissance, and manipulative missions in hazardous environments; technology development in intelligent machine systems; control of a multi-robot process line using AI; land vehicles; remote control weapons platforms; expert systems for logistic analysis. Also addressed are software architecture for real-time, embedded expert systems; knowledge integrity maintenance; embedding AI systems into command and control; a natural language understanding system for maneuver control; and a design of a generic intelligent trainer.

  10. Northeast Artificial Intelligence Consortium (NAIC). Volume 13. Image understanding and intelligent parallel systems. Final report, Sep 84-Dec 89

    SciTech Connect

    Brown, C.M.

    1990-12-01

    The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose was to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress during the existence of the NAIC on the technical research tasks undertaken at the member universities. The topics covered in general are: versatile expert system for equipment maintenance, distributed AI for communications system control, automatic photointerpretation, time-oriented problem solving, speech understanding systems, knowledge base maintenance, hardware architectures for very large systems, knowledge-based reasoning and planning, and a knowledge acquisition, assistance, and explanation system. The specific topics for this volume are various aspects of parallel, structural and optimal techniques in computer vision.

  11. An Intelligent Model for Pairs Trading Using Genetic Algorithms

    PubMed Central

    Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

    2015-01-01

    Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236

  12. An Intelligent Model for Pairs Trading Using Genetic Algorithms.

    PubMed

    Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

    2015-01-01

    Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236

  13. Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation.

    PubMed

    Du, Tingsong; Hu, Yang; Ke, Xianting

    2015-01-01

    An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.

  14. Guidance for human interface with artificial intelligence systems

    NASA Technical Reports Server (NTRS)

    Potter, Scott S.; Woods, David D.

    1991-01-01

    The beginning of a research effort to collect and integrate existing research findings about how to combine computer power and people is discussed, including problems and pitfalls as well as desirable features. The goal of the research is to develop guidance for the design of human interfaces with intelligent systems. Fault management tasks in NASA domains are the focus of the investigation. Research is being conducted to support the development of guidance for designers that will enable them to make human interface considerations into account during the creation of intelligent systems.

  15. Launch vehicle operations cost reduction through artificial intelligence techniques

    NASA Technical Reports Server (NTRS)

    Davis, Tom C., Jr.

    1988-01-01

    NASA's Kennedy Space Center has attempted to develop AI methods in order to reduce the cost of launch vehicle ground operations as well as to improve the reliability and safety of such operations. Attention is presently given to cost savings estimates for systems involving launch vehicle firing-room software and hardware real-time diagnostics, as well as the nature of configuration control and the real-time autonomous diagnostics of launch-processing systems by these means. Intelligent launch decisions and intelligent weather forecasting are additional applications of AI being considered.

  16. Artificial Intelligence Tools for Grammar and Spelling Instruction.

    ERIC Educational Resources Information Center

    Pijls, Fieny; And Others

    1987-01-01

    Discusses grammar and spelling instruction in The Netherlands for students aged 10-15 and describes an intelligent computer-assisted instructional environment consisting of a linguistic expert system, a didactic module, and a student interface. Three prototypes are described: BOUWSTEEN and COGO for analyzing sentences, and TDTDT for conjugating…

  17. Turtle Escapes the Plane: Some Advanced Turtle Geometry. Artificial Intelligence Memo Number 348.

    ERIC Educational Resources Information Center

    diSessa, Andy

    The LOGO Turtles, originally developed at the Massachusetts Institute of Technology Artificial Intelligence Laboratory for teaching concepts in elementary geometry to primary-age children, can also be used in teaching higher-level mathematics. In the exercises described here, the turtle was programed to traverse curved surfaces. Both geometric and…

  18. Telerobot task planning and reasoning: Introduction to JPL artificial intelligence research

    NASA Technical Reports Server (NTRS)

    Atkinson, D. J.

    1987-01-01

    A view of the capabilities and areas of artificial intelligence research which are required for autonomous space telerobotics extending through the year 2000 is given. In the coming years, JPL will be conducting directed research to achieve these capabilities, as well as drawing heavily on collaborative efforts conducted with other research laboratories.

  19. A Computational View of the Skill of Juggling. Artificial Intelligence Memo Number 330.

    ERIC Educational Resources Information Center

    Austin, Howard

    This research has as its basic premise the belief that physical and mental skills are highly similar, enough so in fact that computation paradigms such as the ones used in Artificial Intelligence research about predominantly mental skills can be usefully extended to include physical skills. This thesis is pursued experimentally by the…

  20. The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

    ERIC Educational Resources Information Center

    Metz, Dale Evan; And Others

    1992-01-01

    A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…

  1. ICCE/ICCAI 2000 Full & Short Papers (Artificial Intelligence in Education).

    ERIC Educational Resources Information Center

    2000

    This document contains the full and short papers on artificial intelligence in education from ICCE/ICCAI 2000 (International Conference on Computers in Education/International Conference on Computer-Assisted Instruction) covering the following topics: a computational model for learners' motivation states in individualized tutoring system; a…

  2. Using Computer-Based Artificial Intelligence Technology to Help ESL Students.

    ERIC Educational Resources Information Center

    Adams, Dennis M.

    This paper discusses ways in which artificial intelligence (AI) technologies may be used to aid students for whom English is a second language in the development of language and reading skills, and asserts that the coupling of technology with close adult-teacher contacts within a context of cultural precedents and social rewards is an important…

  3. An Artificial Intelligence Tutor: A Supplementary Tool for Teaching and Practicing Braille

    ERIC Educational Resources Information Center

    McCarthy, Tessa; Rosenblum, L. Penny; Johnson, Benny G.; Dittel, Jeffrey; Kearns, Devin M.

    2016-01-01

    Introduction: This study evaluated the usability and effectiveness of an artificial intelligence Braille Tutor designed to supplement the instruction of students with visual impairments as they learned to write braille contractions. Methods: A mixed-methods design was used, which incorporated a single-subject, adapted alternating treatments design…

  4. Installation and evaluation of a nuclear power plant operator advisor based on artificial intelligence technology

    SciTech Connect

    Hajek, B.K.; Miller, D.W.

    1989-06-20

    This report discusses the following topics on a Nuclear Power Plant operator advisor based on artificial Intelligence Technology; Workstation conversion; Software Conversion; V V Program Development Development; Simulator Interface Development; Knowledge Base Expansion; Dynamic Testing; Database Conversion; Installation at the Perry Simulator; Evaluation of Operator Interaction; Design of Man-Machine Interface; and Design of Maintenance Facility.

  5. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1995-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

  6. Artificial intelligence in process control: Knowledge base for the shuttle ECS model

    NASA Technical Reports Server (NTRS)

    Stiffler, A. Kent

    1989-01-01

    The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

  7. The Roles of Artificial Intelligence in Education: Current Progress and Future Prospects

    ERIC Educational Resources Information Center

    McArthur, David; Lewis, Matthew; Bishary, Miriam

    2005-01-01

    This report begins by summarizing current applications of ideas from artificial intelligence (Al) to education. It then uses that summary to project various future applications of Al--and advanced technology in general--to education, as well as highlighting problems that will confront the wide­ scale implementation of these technologies in the…

  8. Teaching Children to be Mathematicians vs. Teaching About Mathematics. Artificial Intelligence Memo Number 249.

    ERIC Educational Resources Information Center

    Papert, Seymour

    Children will become more proficient in mathematics if they do mathematics rather than merely learn about mathematics. Once educators overcome the misconception that the average student can't do math, they can develop new mathematical activities for children, such as those invented by the staff of the Artificial Intelligence Laboratory at…

  9. Keeping Pace with New Technology: An Introduction to Robotics, FORTH, and Artificial Intelligence.

    ERIC Educational Resources Information Center

    Reck, Gene

    A course was developed to introduce students at a community college to four major areas of emphasis in emerging technologies: FORTH programming language, elementary electronic theory, robotics, and artificial intelligence. After a needs assessment indicated the importance of such a course, a pretest focusing on the four areas was given to students…

  10. LOGO Progress Report 1973-1975. Artificial Intelligence Memo Number 356. Revised.

    ERIC Educational Resources Information Center

    Abelson, H.; And Others

    This report outlines the accomplishments of the LOGO project of the Massachusetts Institute of Technology's Artificial Intelligence Laboratory during the period 1973-1975. Three major areas of work are listed: (1) building learning environments, (2) the theory behind the environments, and (3) experimenting with learning environments. Advances in…

  11. A Retrospective and Prospective View of Information Retrieval and Artificial Intelligence in the 21st Century.

    ERIC Educational Resources Information Center

    Garfield, Eugene

    2001-01-01

    Traces the development of information retrieval/services and suggests that the creation of large digital libraries seems inevitable. Examines possibilities for increasing electronic access and the role of artificial intelligence. Highlights include: searching full text; sending full texts; selective dissemination of information (SDI) profiling and…

  12. Artificial Intelligence and Expert Systems Research and Their Possible Impact on Information Science.

    ERIC Educational Resources Information Center

    Borko, Harold

    1985-01-01

    Defines artificial intelligence (AI) and expert systems; describes library applications utilizing AI to automate creation of document representations, request formulations, and design and modify search strategies for information retrieval systems; discusses expert system development for information services; and reviews impact of these…

  13. Prediction of shipboard electromagnetic interference (EMI) problems using artificial intelligence (AI) technology

    NASA Technical Reports Server (NTRS)

    Swanson, David J.

    1990-01-01

    The electromagnetic interference prediction problem is characteristically ill-defined and complicated. Severe EMI problems are prevalent throughout the U.S. Navy, causing both expected and unexpected impacts on the operational performance of electronic combat systems onboard ships. This paper focuses on applying artificial intelligence (AI) technology to the prediction of ship related electromagnetic interference (EMI) problems.

  14. Artificial Intelligence and Linguistics: A Brief History of a One-Way Relationship.

    ERIC Educational Resources Information Center

    Rosenberg, Richard S.

    For the past 15 years there has been a serious interest in the processing of natural language (English) by researchers in Artificial Intelligence (A.I.). This processing has included machine translation, question-answering systems, man-machine dialogue, and speech understanding. This interest has engendered an awareness of and a concern with the…

  15. Exploiting Artificial Intelligence To Enhance Training: A Short- and Medium-Term Perspective.

    ERIC Educational Resources Information Center

    Cumming, Geoff

    This paper is an introductory discussion of industrial training, artificial intelligence (AI), and AI applications in training, prepared in the context of the United Kingdom Training Commission (TC) program. Following an outline of the activities and aims of the program, individual sections describe perspectives on: (1) training needs, including…

  16. Survey of Artificial Intelligence and Expert Systems in Library and Information Science Literature.

    ERIC Educational Resources Information Center

    Hsieh, Cynthia C.; Hall, Wendy

    1989-01-01

    Examines the definition and history of artificial intelligence (AI) and investigates the body of literature on AI found in "Library Literature" and "Library and Information Science Abstracts." The results reported include the number of articles by year and per journal, and the percentage of articles dealing with library applications. (nine…

  17. Artificial Intelligence in Education--State of the Art and Perspectives. ZIFF Papiere 111.

    ERIC Educational Resources Information Center

    Buiu, Catalin

    This review contains an overview of past and present trends in the application of what is called "artificial intelligence" in traditional face-to-face education and in distance education. The reviewed trends are illustrated with examples of research projects and results throughout the world. The first section of the review discusses intelligence…

  18. Artificial Intelligence, Expert Systems, Natural Language Interfaces, Knowledge Engineering and the Librarian.

    ERIC Educational Resources Information Center

    Davies, Jim

    This paper begins by examining concepts of artificial intelligence (AI) and discusses various definitions of the concept that have been suggested in the literature. The nesting relationship of expert systems within the broader framework of AI is described, and expert systems are characterized as knowledge-based systems (KBS) which attempt to solve…

  19. Artificial Intelligence and Language Development and Language Usage with the Deaf.

    ERIC Educational Resources Information Center

    Leach, John Mark

    The paper reviews research on the application of artificial intelligence (AI) in language development and/or instruction with the deaf. Contributions of computer assisted instruction are noted, as are the problems resulting from over-dependence on a drill and practice format and from deaf students' difficulties in receiving and understanding new…

  20. Application of artificial intelligence to reservoir characterization: An interdisciplinary approach. Final report, August 31, 1997

    SciTech Connect

    Kerr, D.R.; Thompson, L.G.; Shenoi, S.

    1998-03-01

    The primary goal of the project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlations between wells. Using the correlations and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical techniques. The generated petrophysical properties are used as input in the next step where the production data are honored. By adjusting the petrophysical properties, the match between the simulated and the observed production rates is obtained. Although all the components within the overall system are functioning, the integration of dynamic data may not be practical due to the single-phase flow limitations and the computationally intensive algorithms. The future work needs to concentrate on making the dynamic data integration computationally efficient.

  1. Designing novel copolymers of donor-acceptor polymers using an artificial intelligence method

    NASA Astrophysics Data System (ADS)

    Kapoor, Vinita; Bakhshi, A. K.

    2013-08-01

    Using the ab initio Hartree-Fock crystal orbital results of three donor-acceptor polymers, PFUCO ([A]x), PSIFCO ([B]x) and PSIFCH ([C]x), the electronic properties of their novel quasi-one-dimensional copolymers (AmBn)x and (AmCn)x were investigated using an artificial intelligence technique, the genetic algorithm, in combination with negative factor counting and inverse iteration method. The repeat units in PFUCO consist of bifuran bridged by electron accepting groups Y (>Cdbnd O); while in PSIFCO and PSIFCH, the repeat units consist of bicyclopentadifluorosilole bridged by electron accepting groups Y (Y is >Cdbnd O in PSIFCO, and >Cdbnd CH2 in PSIFCH). The trends in the electronic properties of the copolymers (AmBn)x and (AmCn)x as a function of block sizes m and n, and arrangement of units (periodic and random) in the copolymer chain are also discussed. The results obtained are important guidelines for molecular designing of copolymers with tailor-made conduction properties.

  2. Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

    NASA Astrophysics Data System (ADS)

    Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

    Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

  3. Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.

    PubMed

    Jahidin, A H; Megat Ali, M S A; Taib, M N; Tahir, N Md; Yassin, I M; Lias, S

    2014-04-01

    This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.

  4. Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

    NASA Technical Reports Server (NTRS)

    1994-01-01

    The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments.

  5. Collective intelligence of the artificial life community on its own successes, failures, and future.

    PubMed

    Rasmussen, Steen; Raven, Michael J; Keating, Gordon N; Bedau, Mark A

    2003-01-01

    We describe a novel Internet-based method for building consensus and clarifying conflicts in large stakeholder groups facing complex issues, and we use the method to survey and map the scientific and organizational perspectives of the artificial life community during the Seventh International Conference on Artificial Life (summer 2000). The issues addressed in this survey included artificial life's main successes, main failures, main open scientific questions, and main strategies for the future, as well as the benefits and pitfalls of creating a professional society for artificial life. By illuminating the artificial life community's collective perspective on these issues, this survey illustrates the value of such methods of harnessing the collective intelligence of large stakeholder groups. PMID:12906730

  6. A TLD dose algorithm using artificial neural networks

    SciTech Connect

    Moscovitch, M.; Rotunda, J.E.; Tawil, R.A.; Rathbone, B.A.

    1995-12-31

    An artificial neural network was designed and used to develop a dose algorithm for a multi-element thermoluminescence dosimeter (TLD). The neural network architecture is based on the concept of functional links network (FLN). Neural network is an information processing method inspired by the biological nervous system. A dose algorithm based on neural networks is fundamentally different as compared to conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with given responses of a multi-element dosimeter (input) many times. The algorithm, being trained that way, eventually is capable to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personal dosimetry, the output consists of the desired dose components: deep dose, shallow dose and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. The neural network approach was applied to the Harshaw Type 8825 TLD, and was shown to significantly improve the performance of this dosimeter, well within the U.S. accreditation requirements for personnel dosimeters.

  7. Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm

    NASA Astrophysics Data System (ADS)

    Şencan Şahin, Arzu

    2012-11-01

    Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector.

  8. A comparative study of artificial intelligent-based maximum power point tracking for photovoltaic systems

    NASA Astrophysics Data System (ADS)

    Hussain Mutlag, Ammar; Mohamed, Azah; Shareef, Hussain

    2016-03-01

    Maximum power point tracking (MPPT) is normally required to improve the performance of photovoltaic (PV) systems. This paper presents artificial intelligent-based maximum power point tracking (AI-MPPT) by considering three artificial intelligent techniques, namely, artificial neural network (ANN), adaptive neuro fuzzy inference system with seven triangular fuzzy sets (7-tri), and adaptive neuro fuzzy inference system with seven gbell fuzzy sets. The AI-MPPT is designed for the 25 SolarTIFSTF-120P6 PV panels, with the capacity of 3 kW peak. A complete PV system is modelled using 300,000 data samples and simulated in the MATLAB/SIMULINK. The AI-MPPT has been tested under real environmental conditions for two days from 8 am to 18 pm. The results showed that the ANN based MPPT gives the most accurate performance and then followed by the 7-tri-based MPPT.

  9. Intelligent decision support algorithm for distribution system restoration.

    PubMed

    Singh, Reetu; Mehfuz, Shabana; Kumar, Parmod

    2016-01-01

    Distribution system is the means of revenue for electric utility. It needs to be restored at the earliest if any feeder or complete system is tripped out due to fault or any other cause. Further, uncertainty of the loads, result in variations in the distribution network's parameters. Thus, an intelligent algorithm incorporating hybrid fuzzy-grey relation, which can take into account the uncertainties and compare the sequences is discussed to analyse and restore the distribution system. The simulation studies are carried out to show the utility of the method by ranking the restoration plans for a typical distribution system. This algorithm also meets the smart grid requirements in terms of an automated restoration plan for the partial/full blackout of network.

  10. Intelligent decision support algorithm for distribution system restoration.

    PubMed

    Singh, Reetu; Mehfuz, Shabana; Kumar, Parmod

    2016-01-01

    Distribution system is the means of revenue for electric utility. It needs to be restored at the earliest if any feeder or complete system is tripped out due to fault or any other cause. Further, uncertainty of the loads, result in variations in the distribution network's parameters. Thus, an intelligent algorithm incorporating hybrid fuzzy-grey relation, which can take into account the uncertainties and compare the sequences is discussed to analyse and restore the distribution system. The simulation studies are carried out to show the utility of the method by ranking the restoration plans for a typical distribution system. This algorithm also meets the smart grid requirements in terms of an automated restoration plan for the partial/full blackout of network. PMID:27512634

  11. On introduction of artificial intelligence elements to heat power engineering

    NASA Astrophysics Data System (ADS)

    Dregalin, A. F.; Nazyrova, R. R.

    1993-10-01

    The basic problems of 'the thermodynamic intelligence' of personal computers have been outlined. The thermodynamic intellect of personal computers as a concept has been introduced to heat processes occurring in engines of flying vehicles. In particular, the thermodynamic intellect of computers is determined by the possibility of deriving formal relationships between thermodynamic functions. In chemical thermodynamics, a concept of a characteristic function has been introduced.

  12. The role of artificial intelligence in design possibilities and pitfalls

    SciTech Connect

    Middleton, R.H.; Rees, D.; Esat, I.; Phillips, G.

    1996-12-31

    Design is often considered a human-only activity requiring a creativity and intelligence not available to computers. In recent years CAD and related technologies have become widespread, improvements in AI technology are enabling computers to assist in areas of the design process that were previously inaccessible. This paper looks at the role which AI is playing in design. The potential future uses of this technology, as well as possible problems which may arise are also considered.

  13. An artificial bee colony algorithm for uncertain portfolio selection.

    PubMed

    Chen, Wei

    2014-01-01

    Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm. PMID:25089292

  14. An artificial bee colony algorithm for uncertain portfolio selection.

    PubMed

    Chen, Wei

    2014-01-01

    Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

  15. An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

    PubMed Central

    Chen, Wei

    2014-01-01

    Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm. PMID:25089292

  16. Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches.

    PubMed

    Çelik, Ufuk; Yurtay, Nilüfer; Koç, Emine Rabia; Tepe, Nermin; Güllüoğlu, Halil; Ertaş, Mustafa

    2015-01-01

    The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.

  17. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

    PubMed

    Garro, Beatriz A; Vázquez, Roberto A

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

  18. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

    PubMed Central

    Garro, Beatriz A.; Vázquez, Roberto A.

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

  19. Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

    PubMed Central

    Çelik, Ufuk; Yurtay, Nilüfer; Koç, Emine Rabia; Tepe, Nermin; Güllüoğlu, Halil; Ertaş, Mustafa

    2015-01-01

    The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy. PMID:26075014

  20. An investigation of artificial-intelligence methods for use in programming environments: The inclusion of an expert programmer in the SLAW

    SciTech Connect

    Doran, M.V.

    1989-01-01

    The Structured Language Algorithm Writer (SLAW) has been developed at Tulane University for use on IBM-PC's over the past several years. The environment, currently in its third generation, allows for the creation and editing of a structure chart with automatic translation of the algorithm into Pascal, FORTRAN and BASIC code. The scope of this research has centered around the artificial intelligence methods and techniques necessary to include an expert tutor into the SLAW environment. The main aid provided by this expert tutor is assisting the user in the creation of valid algorithms and acquiring certain algorithm design concepts. The main tasks which were necessary to accomplish these goals have been the ability of the tutor to understand the goal of the algorithms and also the proper creation and use of a knowledge base of algorithms. The overall focus of the environment, which differs from other programming environments, is that it centers upon the design phase, rather than programming coding and syntax. By focusing on design it leads to developing problem solving skills rather than programming skills, which are the underlying basis of computer science and all sciences. Thus the objective of the research has been to investigate the marriage of software engineering and artificial intelligence into a useful teaching tool focusing on design rather than coding skills.

  1. A new distributed systems scheduling algorithm: a swarm intelligence approach

    NASA Astrophysics Data System (ADS)

    Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

    2011-12-01

    The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

  2. Artificial intelligence, neural network, and Internet tool integration in a pathology workstation to improve information access

    NASA Astrophysics Data System (ADS)

    Sargis, J. C.; Gray, W. A.

    1999-03-01

    The APWS allows user friendly access to several legacy systems which would normally each demand domain expertise for proper utilization. The generalized model, including objects, classes, strategies and patterns is presented. The core components of the APWS are the Microsoft Windows 95 Operating System, Oracle, Oracle Power Objects, Artificial Intelligence tools, a medical hyperlibrary and a web site. The paper includes a discussion of how could be automated by taking advantage of the expert system, object oriented programming and intelligent relational database tools within the APWS.

  3. Artificial intelligence and signal processing for infrastructure assessment

    NASA Astrophysics Data System (ADS)

    Assaleh, Khaled; Shanableh, Tamer; Yehia, Sherif

    2015-04-01

    The Ground Penetrating Radar (GPR) is being recognized as an effective nondestructive evaluation technique to improve the inspection process. However, data interpretation and complexity of the results impose some limitations on the practicality of using this technique. This is mainly due to the need of a trained experienced person to interpret images obtained by the GPR system. In this paper, an algorithm to classify and assess the condition of infrastructures utilizing image processing and pattern recognition techniques is discussed. Features extracted form a dataset of images of defected and healthy slabs are used to train a computer vision based system while another dataset is used to evaluate the proposed algorithm. Initial results show that the proposed algorithm is able to detect the existence of defects with about 77% success rate.

  4. Applicability of artificial intelligence to reservoir induced earthquakes

    NASA Astrophysics Data System (ADS)

    Samui, Pijush; Kim, Dookie

    2014-06-01

    This paper proposes to use least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of the magnitude (M) of induced earthquakes based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are used as input variables of the LSSVM and RVM. The output of the LSSVM and RVM is M. Equations have been presented based on the developed LSSVM and RVM. The developed RVM also gives variance of the predicted M. A comparative study has been carried out between the developed LSSVM, RVM, artificial neural network (ANN), and linear regression models. Finally, the results demonstrate the effectiveness and efficiency of the LSSVM and RVM models.

  5. Swarm Intelligence Algorithm for Induction Motor Field Efficiency Evaluation

    NASA Astrophysics Data System (ADS)

    Sakthivel, V. P.; Subramanian, S.

    Determining induction motor field efficiency is imperative in industries for energy conservation and cost savings. The induction motor efficiency is generally tested in a laboratories by certain methods defined in IEEE Standard - 112. But these methods cannot be used for motor efficiency evaluations in the field because it disrupts the production process of the industry. This paper proposes a swarm intelligence algorithm, Particle Swarm Optimization (PSO) for efficiency evaluation of in-service induction motor based on a modified induction motor equivalent circuit model. In this model, stray load losses are considered. The proposed efficiency evaluation method combines the PSO and the equivalent circuit method. First, the equivalent circuit parameters are estimated by minimizing the difference between measured and calculated values of stator current and input power of the motor using the PSO algorithm. Based on these parameters, the efficiency of the motor at various load points are evaluated by using the equivalent circuit method. To exemplify the performance of the PSO based efficiency estimation method, a 5 HP motor has been tested, compared with genetic algorithm (GA), torque gauge method, equivalent circuit method, slip method, current method and segregated loss method and found to be superior. Accordingly, the method will be useful for engineers who implement the energy efficiency programs to the electric motor systems in industries.

  6. Intelligence algorithms for autonomous navigation in a ground vehicle

    NASA Astrophysics Data System (ADS)

    Petkovsek, Steve; Shakya, Rahul; Shin, Young Ho; Gautam, Prasanna; Norton, Adam; Ahlgren, David J.

    2012-01-01

    This paper will discuss the approach to autonomous navigation used by "Q," an unmanned ground vehicle designed by the Trinity College Robot Study Team to participate in the Intelligent Ground Vehicle Competition (IGVC). For the 2011 competition, Q's intelligence was upgraded in several different areas, resulting in a more robust decision-making process and a more reliable system. In 2010-2011, the software of Q was modified to operate in a modular parallel manner, with all subtasks (including motor control, data acquisition from sensors, image processing, and intelligence) running simultaneously in separate software processes using the National Instruments (NI) LabVIEW programming language. This eliminated processor bottlenecks and increased flexibility in the software architecture. Though overall throughput was increased, the long runtime of the image processing process (150 ms) reduced the precision of Q's realtime decisions. Q had slow reaction times to obstacles detected only by its cameras, such as white lines, and was limited to slow speeds on the course. To address this issue, the image processing software was simplified and also pipelined to increase the image processing throughput and minimize the robot's reaction times. The vision software was also modified to detect differences in the texture of the ground, so that specific surfaces (such as ramps and sand pits) could be identified. While previous iterations of Q failed to detect white lines that were not on a grassy surface, this new software allowed Q to dynamically alter its image processing state so that appropriate thresholds could be applied to detect white lines in changing conditions. In order to maintain an acceptable target heading, a path history algorithm was used to deal with local obstacle fields and GPS waypoints were added to provide a global target heading. These modifications resulted in Q placing 5th in the autonomous challenge and 4th in the navigation challenge at IGVC.

  7. An Application of Artificial Intelligence to the Choreography of Dance.

    ERIC Educational Resources Information Center

    Bradford, James H.; Cote-Laurence, Paulette

    1995-01-01

    Describes an experimental computer program that attempts to simulate a choreographers' knowledge and expertise. The user expresses a set of rules that describe some of the dynamic aspects of a dance. These rules are applied nondeterministically by a "rule driver" program. The rule driver embodies a heuristic algorithm. (MJP)

  8. Selective Snapshot of State-of-the-Art Artificial Intelligence & Robotics with Reference to the Icarus Starship

    NASA Astrophysics Data System (ADS)

    Ellery, A.

    Since the remarkable British Interplanetary Society starship study of the late 1970s - Daedalus - there have been significant developments in the areas of artificial intelligence and robotics. These will be critical technologies for any starship as indeed they are for the current generation of exploratory spacecraft and in-situ planetary robotic explorers. Although early visions of truly intelligent robots have yet to materialize (reasons for which will be outlined), there are nonetheless revolutionary developments which have attempted to address at least some of these earlier unperceived deficiencies. The current state of the art comprises a number of separate strands of research which provide components of robotic intelligence though no over- arching approach has been forthcoming. The first question to be considered is the level of intelligent functionality required to support a long-duration starship mission. This will, at a minimum, need to be extensive imposed by the requirement for complex reconfigurability and repair. The second question concerns the tools that we have at our disposal to implement the required intelligent functions of the starship. These are based on two very different approaches - good old-fashioned artificial intelligence (GOFAI) based on logical theorem-proving and knowledge-encoding recently augmented by modal, temporal, circumscriptive and fuzzy logics to address the well-known “frame problem”; and the more recent soft computing approaches based on artificial neural networks, evolutionary algorithms and immunity models and their variants to implement learning. The former has some flight heritage through the Remote Agent architecture whilst the latter has yet to be deployed on any space mission. However, the notion of reconfigurable hardware of recent interest in the space community warrants the use of evolutionary algorithms and neural networks implemented on field programmable gate array technology, blurring the distinction between

  9. Utilization of artificial intelligence techniques for the Space Station power system

    NASA Technical Reports Server (NTRS)

    Evatt, Thomas C.; Gholdston, Edward W.

    1988-01-01

    Due to the complexity of the Space Station Electrical Power System (EPS) as currently envisioned, artificial intelligence/expert system techniques are being investigated to automate operations, maintenance, and diagnostic functions. A study was conducted to investigate this technology as it applies to failure detection, isolation, and reconfiguration (FDIR) and health monitoring of power system components and of the total system. Control system utilization of expert systems for load scheduling and shedding operations was also researched. A discussion of the utilization of artificial intelligence/expert systems for Initial Operating Capability (IOC) for the Space Station effort is presented along with future plans at Rocketdyne for the utilization of this technology for enhanced Space Station power capability.

  10. Games and machine learning: a powerful combination in an artificial intelligence course

    NASA Astrophysics Data System (ADS)

    Wallace, Scott A.; McCartney, Robert; Russell, Ingrid

    2010-03-01

    Project MLeXAI (Machine Learning eXperiences in Artificial Intelligence (AI)) seeks to build a set of reusable course curriculum and hands on laboratory projects for the artificial intelligence classroom. In this article, we describe two game-based projects from the second phase of project MLeXAI: Robot Defense - a simple real-time strategy game and Checkers - a classic turn-based board game. From the instructors' prospective, we examine aspects of design and implementation as well as the challenges and rewards of using the curricula. We explore students' responses to the projects via the results of a common survey. Finally, we compare the student perceptions from the game-based projects to non-game based projects from the first phase of Project MLeXAI.

  11. Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan.

    PubMed

    Liao, Pei-Hung; Hsu, Pei-Ti; Chu, William; Chu, Woei-Chyn

    2015-06-01

    This study applied artificial intelligence to help nurses address problems and receive instructions through information technology. Nurses make diagnoses according to professional knowledge, clinical experience, and even instinct. Without comprehensive knowledge and thinking, diagnostic accuracy can be compromised and decisions may be delayed. We used a back-propagation neural network and other tools for data mining and statistical analysis. We further compared the prediction accuracy of the previous methods with an adaptive-network-based fuzzy inference system and the back-propagation neural network, identifying differences in the questions and in nurse satisfaction levels before and after using the nursing information system. This study investigated the use of artificial intelligence to generate nursing diagnoses. The percentage of agreement between diagnoses suggested by the information system and those made by nurses was as much as 87 percent. When patients are hospitalized, we can calculate the probability of various nursing diagnoses based on certain characteristics.

  12. Enhancing nuclear power plant performance through the use of artificial intelligence

    SciTech Connect

    Maren, A.J.; Miller, L.F.; Tsoukalas, L.H.; Uhrig, R.E.; Upadhyaya, B.R.

    1990-06-15

    The objective of this research is to advance the state-of-the-art of applying artificial intelligence technology (both expert systems and neural networks) to enhancing the performance (safety, efficiency, control and management) of nuclear power plants. A second, but equally important, objective is to build a broadly based critical mass of expertise in the artificial intelligence field that can be brought to bear on the technology of nuclear power plants. This means the production of graduates at the B.S., M.S., and Ph.D. levels in Nuclear Engineering and related fields. The research undertaken for this program is particularly appropriate for the M.S. theses and Ph.D. dissertations. A third objective is to transfer the technology developed to the nuclear power community,'' as well as the scientific and technological community,'' through publications in appropriate journals and proceedings and through presentations at national and international meetings.

  13. Northeast Artificial Intelligence Consortium (NAIC). Volume 7. Automatic photointerpretation. Final report, Sep 84-Dec 89

    SciTech Connect

    Modestino, J.; Sanderson, A.

    1990-12-01

    The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose was to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress during the existence of the NAIC on the technical research tasks undertaken at the member universities. The topics covered in general are: versatile expert system for equipment maintenance, distributed AI for communications system control, automatic photointerpretation, time-oriented problem solving, speech understanding systems, knowledge base maintenance, hardware architectures for very large systems, knowledge-based reasoning and planning, and a knowledge acquisition, assistance, and explanation system. The specific topics for this volume are the use of expert systems for automated photo interpretation and other AI techniques to image segmentation and region identification.

  14. Reducing Energy Waste in Post-secondary Educational Institutions using Artificial Intelligence

    NASA Astrophysics Data System (ADS)

    Motta Cabrera, David Francisco

    This thesis focuses on computer-related and lighting energy consumption in post-secondary educational institutions. In this respect, artificial intelligence and data association mining are proposed as tools to identify and reduce energy waste. First, an artificial intelligence-based method for forecasting computer usage is proposed. Based on the models' forecast, workstations can be turned on and off, in order to strike a balance between energy savings and user comfort. The models are evaluated on different datasets and their results compared to commercially available alternatives. Second, a data association mining-based approach is proposed to uncover possible relationships between occupancy patterns and lighting-related energy waste in classrooms. A wireless data collection system is used to log data from both lighting consumption and occupancy states during a year. Next, energy savings results of using the proposed approach are compared to those of an occupancy-activated lighting control system for classrooms.

  15. Artificial intelligence techniques applied to the development of a decision–support system for diagnosing celiac disease

    PubMed Central

    Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

    2013-01-01

    Background Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. Objective To develop a clinical decision–support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. Methods A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. Results The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision–support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k = 0.68 (p < 0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician’s diagnostic impression and the gold standard k = 0. 64 (p < 0.0001). There was moderate agreement between the physician’s diagnostic impression and CDSS k = 0.46 (p = 0.0008). Conclusions The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent

  16. Exploiting Artificial Intelligence for Analysis and Data Selection on-board the Puerto Rico CubeSat

    NASA Astrophysics Data System (ADS)

    Bergman, J. E. S.; Bruhn, F.; Funk, P.; Isham, B.; Rincón-Charris, A. A.; Capo-Lugo, P.; Åhlén, L.

    2015-10-01

    CubeSat missions are constrained by the limited resources provided by the platform. Many payload providers have learned to cope with the low mass and power but the poor telemetry allocation remains a bottleneck. In the end, it is the data delivered to ground which determines the value of the mission. However, transmitting more data does not necessarily guarantee high value, since the value also depends on the data quality. By exploiting fast on-board computing and efficient artificial intelligence (AI) algorithms for analysis and data selection one could optimize the usage of the telemetry link and so increase the value of the mission. In a pilot project, we attempt to do this on the Puerto Rico CubeSat, where science objectives include the acquisition of space weather data to aid better understanding of the Sun to Earth connection.

  17. Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations.

    PubMed

    Horta, Antônio Carlos Luperni; da Silva, Adilson José; Sargo, Cíntia Regina; Gonçalves, Viviane Maimoni; Zangirolami, Teresa Cristina; Giordano, Roberto de Campos

    2011-09-01

    One of the most important events in fed-batch fermentations is the definition of the moment to start the feeding. This paper presents a methodology for a rational selection of the architecture of an artificial intelligence (AI) system, based on a neural network committee (NNC), which identifies the end of the batch phase. The AI system was successfully used during high cell density cultivations of recombinant Escherichia coli. The AI algorithm was validated for different systems, expressing three antigens to be used in human and animal vaccines: fragments of surface proteins of Streptococcus pneumoniae (PspA), clades 1 and 3, and of Erysipelothrix rhusiopathiae (SpaA). Standard feed-forward neural networks (NNs), with a single hidden layer, were the basis for the NNC. The NN architecture with best performance had the following inputs: stirrer speed, inlet air, and oxygen flow rates, carbon dioxide evolution rate, and CO2 molar fraction in the exhaust gas.

  18. Potential applications of artificial intelligence in computer-based management systems for mixed waste incinerator facility operation

    SciTech Connect

    Rivera, A.L.; Singh, S.P.N.; Ferrada, J.J.

    1991-01-01

    The Department of Energy/Oak Ridge Field Office (DOE/OR) operates a mixed waste incinerator facility at the Oak Ridge K-25 Site, designed for the thermal treatment of incinerable liquid, sludge, and solid waste regulated under the Toxic Substances Control Act (TSCA) and the Resource Conversion and Recovery Act (RCRA). Operation of the TSCA Incinerator is highly constrained as a result of the regulatory, institutional, technical, and resource availability requirements. This presents an opportunity for applying computer technology as a technical resource for mixed waste incinerator operation to facilitate promoting and sustaining a continuous performance improvement process while demonstrating compliance. This paper describes mixed waste incinerator facility performance-oriented tasks that could be assisted by Artificial Intelligence (AI) and the requirements for AI tools that would implement these algorithms in a computer-based system. 4 figs., 1 tab.

  19. Non-linear associations between laryngo-pharyngeal symptoms of gastro-oesophageal reflux disease: clues from artificial intelligence analysis

    PubMed Central

    Grossi, E

    2006-01-01

    Summary The relationship between the different symptoms of gastro-oesophageal reflux disease remain markedly obscure due to the high underlying non-linearity and the lack of studies focusing on the problem. Aim of this study was to evaluate the hidden relationships between the triad of symptoms related to gastro-oesophageal reflux disease using advanced mathematical techniques, borrowed from the artificial intelligence field, in a cohort of patients with oesophagitis. A total of 388 patients (from 60 centres) with endoscopic evidence of oesophagitis were recruited. The severity of oesophagitis was scored by means of the Savary-Miller classification. PST algorithm was employed. This study shows that laryngo-pharyngeal symptoms related to gastro-oesophageal reflux disease are correlated even if in a non-linear way. PMID:17345935

  20. Space Shuttle processing - A case study in artificial intelligence

    NASA Technical Reports Server (NTRS)

    Mollikarimi, Cindy; Gargan, Robert; Zweben, Monte

    1991-01-01

    A scheduling system incorporating AI is described and applied to the automated processing of the Space Shuttle. The unique problem of addressing the temporal, resource, and orbiter-configuration requirements of shuttle processing is described with comparisons to traditional project management for manufacturing processes. The present scheduling system is developed to handle the late inputs and complex programs that characterize shuttle processing by incorporating fixed preemptive scheduling, constraint-based simulated annealing, and the characteristics of an 'anytime' algorithm. The Space-Shuttle processing environment is modeled with 500 activities broken down into 4000 subtasks and with 1600 temporal constraints, 8000 resource constraints, and 3900 state requirements. The algorithm is shown to scale to very large problems and maintain anytime characteristics suggesting that an automated scheduling process is achievable and potentially cost-effective.

  1. Artificial intelligence techniques for ground test monitoring of rocket engines

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Gupta, U. K.

    1990-01-01

    An expert system is being developed which can detect anomalies in Space Shuttle Main Engine (SSME) sensor data significantly earlier than the redline algorithm currently in use. The training of such an expert system focuses on two approaches which are based on low frequency and high frequency analyses of sensor data. Both approaches are being tested on data from SSME tests and their results compared with the findings of NASA and Rocketdyne experts. Prototype implementations have detected the presence of anomalies earlier than the redline algorithms that are in use currently. It therefore appears that these approaches have the potential of detecting anomalies early eneough to shut down the engine or take other corrective action before severe damage to the engine occurs.

  2. Artificial intelligence for Space Station automation: Crew safety, productivity, autonomy, augmented capability

    NASA Technical Reports Server (NTRS)

    Firschein, O.; Georgeff, M. P.; Park, W.; Cheeseman, P. C.; Geldberg, J.

    1986-01-01

    Artificial intelligence (AI) R&D projects for the successful and efficient operation of the Space Station are described. The book explores the most advanced AI-based technologies, reviews the results of concept design studies to determine required AI capabilities, details demonstrations that would indicate the existence of these capabilities, and develops an R&D plan leading to such demonstrations. Particular attention is given to teleoperation and robotics, sensors, expert systems, computers, planning, and man-machine interface.

  3. Method and apparatus for optimizing operation of a power generating plant using artificial intelligence techniques

    DOEpatents

    Wroblewski, David; Katrompas, Alexander M.; Parikh, Neel J.

    2009-09-01

    A method and apparatus for optimizing the operation of a power generating plant using artificial intelligence techniques. One or more decisions D are determined for at least one consecutive time increment, where at least one of the decisions D is associated with a discrete variable for the operation of a power plant device in the power generating plant. In an illustrated embodiment, the power plant device is a soot cleaning device associated with a boiler.

  4. The use of artificial intelligence techniques to improve the multiple payload integration process

    NASA Technical Reports Server (NTRS)

    Cutts, Dannie E.; Widgren, Brian K.

    1992-01-01

    A maximum return of science and products with a minimum expenditure of time and resources is a major goal of mission payload integration. A critical component then, in successful mission payload integration is the acquisition and analysis of experiment requirements from the principal investigator and payload element developer teams. One effort to use artificial intelligence techniques to improve the acquisition and analysis of experiment requirements within the payload integration process is described.

  5. Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

    NASA Technical Reports Server (NTRS)

    Krishnan, G. S.

    1997-01-01

    A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

  6. Comparison of artificial intelligence classifiers for SIP attack data

    NASA Astrophysics Data System (ADS)

    Safarik, Jakub; Slachta, Jiri

    2016-05-01

    Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work; the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

  7. Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

    PubMed Central

    Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

    2016-01-01

    Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision. PMID:26885520

  8. Combination of artificial intelligence and procedural language programs in a computer application system supporting nuclear reactor operations

    SciTech Connect

    Town, G.G.; Stratton, R.C.

    1985-01-01

    A computer application system is described which provides nuclear reactor power plant operators with an improved decision support system. This system combines traditional computer applications such as graphics display with artificial intelligence methodologies such as reasoning and diagnosis so as to improve plant operability. This paper discusses the issues, and a solution, involved with the system integration of applications developed using traditional and artificial intelligence languages.

  9. Artificial General Intelligence: Concept, State of the Art, and Future Prospects

    NASA Astrophysics Data System (ADS)

    Goertzel, Ben

    2014-12-01

    In recent years broad community of researchers has emerged, focusing on the original ambitious goals of the AI field - the creation and study of software or hardware systems with general intelligence comparable to, and ultimately perhaps greater than, that of human beings. This paper surveys this diverse community and its progress. Approaches to defining the concept of Artificial General Intelligence (AGI) are reviewed including mathematical formalisms, engineering, and biology inspired perspectives. The spectrum of designs for AGI systems includes systems with symbolic, emergentist, hybrid and universalist characteristics. Metrics for general intelligence are evaluated, with a conclusion that, although metrics for assessing the achievement of human-level AGI may be relatively straightforward (e.g. the Turing Test, or a robot that can graduate from elementary school or university), metrics for assessing partial progress remain more controversial and problematic.

  10. Computer aided diagnosis based on medical image processing and artificial intelligence methods

    NASA Astrophysics Data System (ADS)

    Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

    2006-12-01

    Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

  11. Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

    PubMed

    Gupta, Shikha; Basant, Nikita; Rai, Premanjali; Singh, Kunwar P

    2015-11-01

    Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The

  12. Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS/GPS Integration Algorithms

    PubMed Central

    Chiang, Kai-Wei; Chang, Hsiu-Wen

    2010-01-01

    Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks—the Cascade Correlation Neural Network (CCNNs)—to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN

  13. Intelligent sensor positioning and orientation through constructive neural network-embedded INS/GPS integration algorithms.

    PubMed

    Chiang, Kai-Wei; Chang, Hsiu-Wen

    2010-01-01

    Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks--the Cascade Correlation Neural Network (CCNNs)--to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN

  14. Proceedings of the conference on artificial intelligence in the Department of Energy Complex

    SciTech Connect

    Marinuzzi, J.G.

    1990-05-01

    This report contains the following discussions: the defense program simulation of rocky flats plant; spatial representation and reasoning for automated mesh generation; INEL support to modernization efforts at the aberdeen proving ground; artificial intelligence applications at the ICPP; an expert system for tuning particle beam accelerators; quality control expert system; an easily maintained knowledge-based system for interactive delivery of detailed technical information; workload scheduling in DOE production complex; turning operations planning system; a nuclear power plant operator advisor based on artificial intelligence technology; a impact of artificial intelligence on the new production reactor; using expert systems in treaty verification; knowledge-based systems technology transfer in Oak Ridge; applications of AI to nuclear power plants; knowledge-based computer security systems; robotic grasping of unknown objects: a knowledge-based approach; applying expertise to data in the geologist's assistant expert system; feature recognition based automatic part classification and coding; object-oriented inventories for simulation of manufacturing process; expert system at AWE; plating expert system; inspection process planning expert; troubleshooting local area networks at Savannah River Site; maintenance importance generator; joint theater level simulator; a system for authoring of tutorials including video capture and annotation, links to manuals, and links to executable code; a personal computer based expert system for documenting compliance with the National Environmental Protection Act; spatial representation and reasoning for automated mesh generation; robotic grasping of unknown objects: a knowledge-based approach; and synthesis of engineering anticipatory systems.

  15. Neurolinguistically constrained simulation of sentence comprehension: integrating artificial intelligence and brain theory

    SciTech Connect

    Gigley, H.M.

    1982-01-01

    An artificial intelligence approach to the simulation of neurolinguistically constrained processes in sentence comprehension is developed using control strategies for simulation of cooperative computation in associative networks. The desirability of this control strategy in contrast to ATN and production system strategies is explained. A first pass implementation of HOPE, an artificial intelligence simulation model of sentence comprehension, constrained by studies of aphasic performance, psycholinguistics, neurolinguistics, and linguistic theory is described. Claims that the model could serve as a basis for sentence production simulation and for a model of language acquisition as associative learning are discussed. HOPE is a model that performs in a normal state and includes a lesion simulation facility. HOPE is also a research tool. Its modifiability and use as a tool to investigate hypothesized causes of degradation in comprehension performance by aphasic patients are described. Issues of using behavioral constraints in modelling and obtaining appropriate data for simulated process modelling are discussed. Finally, problems of validation of the simulation results are raised; and issues of how to interpret clinical results to define the evolution of the model are discussed. Conclusions with respect to the feasibility of artificial intelligence simulation process modelling are discussed based on the current state of research.

  16. Fractional snow cover mapping from MODIS data using wavelet-artificial intelligence hybrid models

    NASA Astrophysics Data System (ADS)

    Moosavi, Vahid; Malekinezhad, Hossein; Shirmohammadi, Bagher

    2014-04-01

    This study was carried out to evaluate the wavelet-artificial intelligence hybrid models to produce fractional snow cover maps. At first, cloud cover was removed from MODIS data and cloud free images were produced. SVM-based binary classified ETM+ imagery were then used as reference maps in order to obtain train and test data for sub-pixel classification models. ANN and ANFIS-based modeling were performed using raw data (without wavelet-based preprocessing). In the next step, several mother wavelets and levels were used in order to decompose the original data to obtain wavelet coefficients. Then, the decomposed data were used for further modeling processes. ANN, ANFIS, wavelet-ANN and wavelet-ANFIS models were compared to evaluate the effect of wavelet transformation on the ability of artificial intelligence models. It was demonstrated that wavelet transformation as a preprocessing approach can significantly enhance the performance of ANN and ANFIS models. This study indicated an overall accuracy of 92.45% for wavelet-ANFIS model, 86.13% for wavelet-ANN, 72.23% for ANFIS model and 66.78% for ANN model. In fact, hybrid wavelet-artificial intelligence models can extract the characteristics of the original signals (i.e. model inputs) accurately through decomposing the non-stationary and complex signals into several stationary and simpler signals. The positive effect of fuzzification as well as wavelet transformation in the wavelet-ANFIS model was also confirmed.

  17. Artificial intelligence approach in analysis of DNA sequences.

    PubMed

    Brézillon, P J; Zaraté, P; Saci, F

    1993-01-01

    We present an approach for designing a knowledge-based system, called Sequence Acquisition In Context (SAIC), that will be able to cooperate with a biologist in the analysis of DNA sequences. The main task of the system is the acquisition of the expert knowledge that the biologist uses for solving ambiguities from gel autoradiograms, with the aim of re-using it later for solving similar ambiguities. The various types of expert knowledge constitute what we call the contextual knowledge of the sequence analysis. Contextual knowledge deals with the unavoidable problems that are common in the study of the living material (eg noise on data, difficulties of observations). Indeed, the analysis of DNA sequences from autoradiograms belongs to an emerging and promising area of investigation, namely reasoning with images. The SAIC project is developed in a theoretical framework that is shared with other applications. Not all tasks have the same importance in each application. We use this observation for designing an intelligent assistant system with three applications. In the SAIC project, we focus on knowledge acquisition, human-computer interaction and explanation. The project will benefit research in the two other applications. We also discuss our SAIC project in the context of large international projects that aim to re-use and share knowledge in a repository.

  18. Surface roughness optimization of polyamide-6/nanoclay nanocomposites using artificial neural network: genetic algorithm approach.

    PubMed

    Moghri, Mehdi; Madic, Milos; Omidi, Mostafa; Farahnakian, Masoud

    2014-01-01

    During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite. PMID:24578636

  19. Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach

    PubMed Central

    Moghri, Mehdi; Omidi, Mostafa; Farahnakian, Masoud

    2014-01-01

    During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite. PMID:24578636

  20. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    NASA Astrophysics Data System (ADS)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.