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Sample records for northeast artificial intelligence

  1. Northeast Artificial Intelligence Consortium (NAIC). Volume 1. Executive Summary

    DTIC Science & Technology

    1990-12-01

    Consortium ( NAIC ) I Volker Weiss and James F. Brule" APPROVED FORPUL/RELEAS" DI/UTION UN.IMI/ED This effort was funded partially by the Laboratory...PERFORMING ORGANIZATION Northeast Artificial Intelligence Consortium ( NAIC ) REPORT NUMBER Science & Technology Center, Rm 2-296 N/A 111 College...Approved for public release; distribution unlimited. 13. ABSTRACT(MPau* = w" The Northeast Artificial Intelligence Consortium ( NAIC ) was created by the Air

  2. Northeast Artificial Intelligence Consortium (NAIC). Volume 4. Distributed Artificial Intelligence for Communications Network Management

    DTIC Science & Technology

    1990-12-01

    MANAGEMENT Northeast Artificial Intelligence Consortium (NAIC) Robert A. Meyer and Susan E. Conry APPROVED FOR PUBLIC RELE45E; DIS7T.SUTION UNLM 1IT7ED This...FUNDING NUMBERS DISTRIBUTED ARTIFICIAL INTELLIGENCE FOR COMMUNICATIONS C - F30602-85-C-0008 NETWORK MANAGEMENT PE - 62702F PR - 5581 & (S)TA - 2 7 Robert ...Proceedings of the International Conference on Distributed Computing Systems, May 1986. [47] L. Wos, R. Overbeek , E. Lusk, and J. Boyle. Automated Reasoning

  3. Northeast Artificial Intelligence Consortium Annual Report. Volume 2. 1988 Discussing, Using, and Recognizing Plans (NLP)

    DTIC Science & Technology

    1989-10-01

    Encontro Portugues de Inteligencia Artificial (EPIA), Oporto, Portugal, September 1985. [15] N. J. Nilsson. Principles Of Artificial Intelligence. Tioga...FI1 F COPY () RADC-TR-89-259, Vol II (of twelve) Interim Report October 1969 AD-A218 154 NORTHEAST ARTIFICIAL INTELLIGENCE CONSORTIUM ANNUAL...7a. NAME OF MONITORING ORGANIZATION Northeast Artificial Of p0ilcabe) Intelligence Consortium (NAIC) Rome_____ Air___ Development____Center

  4. Northeast Artificial Intelligence Consortium Annual Report for 1987. Volume 7. Parallel, Structural, and Optimal Techniques in Vision

    DTIC Science & Technology

    1989-03-01

    RADC-TR-88-324, Vol VII (of nine), Part B Interim ReportMarch 1969 AD-A208 611 NORTHEAST ARTIFICIAL INTELLIGENCE CONSORTIUM ANNUAL REPORT 1987...Northeast Artificial (If applicable) Rome Air Development Center (IRRE) Intelligence Consortium (NAIC) I 6c. ADDRESS (City, State, and ZIP Code) 7b...62702F 4594 18 E2 11. TITLE (Include Security Classification) NORTHEAST ARTIFICIAL INTELLIGENCE CONSORTIUM ANNUAL REPORT,1987 Parallel, Structural, and

  5. 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.

  6. 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.

  7. 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…

  8. 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…

  9. 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…

  10. 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…

  11. 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…

  12. Artificial Intelligence,

    DTIC Science & Technology

    PATTERN RECOGNITION, * ARTIFICIAL INTELLIGENCE , *TEXTBOOKS, COMPUTER PROGRAMMING, MATHEMATICAL LOGIC, ROBOTS, PROBLEM SOLVING, STATISTICAL ANALYSIS, GAME THEORY, NATURAL LANGUAGE, SELF ORGANIZING SYSTEMS.

  13. Artificial Intelligence.

    ERIC Educational Resources Information Center

    Smith, Linda C.; And Others

    1988-01-01

    A series of articles focuses on artificial intelligence research and development to enhance information systems and services. Topics discussed include knowledge base designs, expert system development tools, natural language processing, expert systems for reference services, and the role that artificial intelligence concepts should have in…

  14. 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)

  15. Northeast Artificial Intelligence Consortium annual report 1986. Volume 3. Distributed artificial intelligence for communication network management. Interim report, January-December 1986

    SciTech Connect

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

    1988-06-01

    The Northeast Consortium conducts pertinent research in artificial intelligence and performs activities ancillary to this research. This report describes progress made in the second year of the existence of the consortium 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 photo interpretation, 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.

  16. 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.

  17. Northeast Artificial Intelligence Consortium Annual Report 1986. Volume 4. Part A. Hierarchical Region-Based Approach to Automatic Photointerpretation. Part B. Application of AI Techniques to Image Segmentation and Region Identification

    DTIC Science & Technology

    1988-01-01

    MONITORING ORGANIZATION Northeast Artificial (If applicaole)nelincCostum(AcRome Air Development Center (COCU) Inteligence Consortium (NAIC)I 6c. ADDRESS...f, Offell RADC-TR-88-1 1, Vol IV (of eight) Interim Technical ReportS June 1988 NORTHEAST ARTIFICIAL INTELLIGENCE CONSORTIUM ANNUAL REPORT 1986...13441-5700 EMENT NO NO NO ACCESSION NO62702F 5 8 71 " " over) I 58 27 13 " ൓ TITLE (Include Security Classification) NORTHEAST ARTIFICIAL INTELLIGENCE

  18. Artificial Intelligence and Robotics.

    DTIC Science & Technology

    1984-02-01

    D-Ai42 488 ARTIFICIAL INEELLIGENCE AND ROBOTICS (U) MASSACHUSETTS i/1 INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB M BRADY FEB 84 AI-M-756...Subtile) S. TYPE OF REPORT A PERIOD COVERED Artificial Intelligence and Robotics 6. PERFORMING ORG. REPORT NUMBER 7. AUTHOR(*) S. CONTRACT OR GRANT NUMBER...Identify by block niiniber) -. Since Robotics is the field concerned with the connection of perception to action, Artificial Intelligence must have a

  19. Northeast Artificial Intelligence Consortium annual report - 1988. Volume 4. Distributed AI for communications network management. Interim report, January-December 1988

    SciTech Connect

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

    1989-10-01

    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 is to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress that has been made in the fourth year of 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-based reasoning and planning, knowledge base maintenance, hardware architectures for very large systems, 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.

  20. Intelligent Assistance without Artificial Intelligence

    DTIC Science & Technology

    1986-09-01

    Software Objects 4 Ponur 2: Expeftmntal and Pub~c Databases 9 If -𔄁-7~~,- 5 - bnitefignit Assisftanc without Artficial Intel~lgencO ’WIL Kaiseirl...production-qlualty software eng**"eeringevimnmen that provide seemin~gly Intelligent assistance without equirling new breakthoughs In Al research. Themre...8217.cance of SMILF as an example of Intelligent assistance without artificial intelligence . * 2 . ~2 Aichlbtscmr S 1BEi Utended for use by small torns

  1. 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

  2. STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

    DTIC Science & Technology

    ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

  3. 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.

  4. 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.

  5. 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

  6. Artificial intelligence in medicine.

    PubMed

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

    2004-09-01

    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. 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. 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. 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.

  7. Artificial Intelligence and Robotics.

    DTIC Science & Technology

    1982-09-20

    8217’AD-A122 414 ARTIFICIAL INTELLIGENCE AND ROBOTICS (.) ARMY SCIENCE 1/j 13OARD WA SH INGTON Od I C PEDEN ET AL. 20 SEP 82 UNCLASSIFIED F/G 15/3 NL LEE...AND ACQUISITION WASHINGTON, D. C. 20310 A RMY CIENCE BOARD AD HOC SUBGROUP REPORT ON ARTIFICIAL INTELLIGENCE AND ROBOTICS SEPTEMBER 1982 DTIC DEC 1 5...TITLE (aid Subtitle) S TYPE OF REPORT & PERIOD COVERED Army Science Board AHSG Report Final Artificial Intelligence and Robotics S. PERFORMING ORG

  8. Artificial intelligence and statistics

    SciTech Connect

    Gale, W.A.

    1987-01-01

    This book explores the possible applications of artificial intelligence in statistics and conversely, statistics in artificial intelligence. It is a collection of seventeen papers written by leaders in the field. Most of the papers were prepared for the Workshop on Artificial Intelligence and Statistics held in April 1985 and sponsored by ATandT Bell Laboratories. The book is divided into six parts: uncertainly propagation, clustering and learning, expert systems, environments for supporting statistical strategy, knowledge acquisition, and strategy. The editor ties the collection together in the first chapter by providing an overview of AI and statistics, discussing the Workshop, and exploring future research in the field.

  9. Invitation to artificial intelligence

    SciTech Connect

    D'heedene, R.N.

    1983-02-01

    Artificial intelligence (AI) is intelligence displayed by non-living objects, that is, machines. The possibility of creating intelligent machines has been a motivating force behind a great deal of computing machine development. The methods of AI are not only of historical interest, but are powerful in themselves. Artificial intelligence therefore deserves a prominent place in the undergraduate computer science curriculum. This paper discusses the pedagogical advantages of emphasizing AI in upper level courses, reasons for its present neglect, and the importance of introducing ai study. 5 references.

  10. Bibliography: Artificial Intelligence.

    ERIC Educational Resources Information Center

    Smith, Richard L.

    1986-01-01

    Annotates reference material on artificial intelligence, mostly at an introductory level, with applications to education and learning. Topics include: (1) programing languages; (2) expert systems; (3) language instruction; (4) tutoring systems; and (5) problem solving and reasoning. (JM)

  11. Introduction to artificial intelligence

    NASA Technical Reports Server (NTRS)

    Cheeseman, P.; Gevarter, W.

    1986-01-01

    This paper presents an introductory view of Artificial Intelligence (AI). In addition to defining AI, it discusses the foundations on which it rests, research in the field, and current and potential applications.

  12. Artificial Intelligence Study (AIS).

    DTIC Science & Technology

    1987-02-01

    ARTIFICIAL INTELLIGNECE HARDWARE ....... 2-50 AI Architecture ................................... 2-49 AI Hardware ....................................... 2...system: The synergy between discrete-event simulation and the approaches that programmers take to develop artifical - intelligence software took some...dated 28 October 1986 Subject: Army Artifical Intelligence Training Available at Fort Gordon) has indicated the availability of the following training

  13. Artificial intelligence: Human effects

    SciTech Connect

    Yazdani, M.; Narayanan, A.

    1984-01-01

    This book presents an up-to-date study of the interaction between the fast-growing discipline of artificial intelligence and other human endeavors. The volume explores the scope and limitations of computing, and presents a history of the debate on the possibility of machines achieving intelligence. The authors offer a state-of-the-art survey of Al, concentrating on the ''mind'' (language understanding) and the ''body'' (robotics) of intelligent computing systems.

  14. 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.

  15. 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.

  16. 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.

  17. Applications Of Artificial Intelligence

    NASA Astrophysics Data System (ADS)

    Trivedi, Mohan M.; Gilmore, John F.

    1986-03-01

    Intelligence evolves out of matter, so said the Sankhya philosophers of ancient India. The discipline of artificial intelligence (Al), which was established some 30 years ago, has confirmed the validity of the above assertion. Recently, a number of AI applications have been successfully demonstrated, generating a great deal of excitement and interest in scientific and technical circles. In this special issue of Optical Engineering a representative set of applications that incorporate Al principles is presented.

  18. Artificial intelligence in cardiology.

    PubMed

    Bonderman, Diana

    2017-10-04

    Decision-making is complex in modern medicine and should ideally be based on available data, structured knowledge and proper interpretation in the context of an individual patient. Automated algorithms, also termed artificial intelligence that are able to extract meaningful patterns from data collections and build decisions upon identified patterns may be useful assistants in clinical decision-making processes. In this article, artificial intelligence-based studies in clinical cardiology are reviewed. The text also touches on the ethical issues and speculates on the future roles of automated algorithms versus clinicians in cardiology and medicine in general.

  19. 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…

  20. 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…

  1. 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.

  2. 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…

  3. Artificial Intelligence Project

    DTIC Science & Technology

    1990-01-01

    Computer Science, New Delhi, India , December 1986. (Also University of Texas, Artificial Intelligence Laboratory AITR-41, August 1987). Kumar, V. and...the Tenth ICAI ) A187-55 Expert Systems for Monitoring and Control, D. Dvorak, May 1987. Many large-scale industrial processes and services are

  4. Artificial intelligence and psychiatry.

    PubMed

    Servan-Schreiber, D

    1986-04-01

    This paper provides a brief historical introduction to the new field of artificial intelligence and describes some applications to psychiatry. It focuses on two successful programs: a model of paranoid processes and an expert system for the pharmacological management of depressive disorders. Finally, it reviews evidence in favor of computerized psychotherapy and offers speculations on the future development of research in this area.

  5. 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.

  6. Artificial intelligence and robotics

    SciTech Connect

    Peden, I.C.; Braddock, J.V.; Brown, W.; Langendorf, R.M.

    1982-09-01

    This report examines the state-of-the-art in artificial intelligence and robotics technologies and their potential in terms of Army needs. Assessment includes battlefield technology, research and technology insertions, management considerations and recommendations related to research and development personnel, and recommendations regarding the Army's involvement in the automated plant.

  7. 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…

  8. 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.

  9. Economics and artificial intelligence

    SciTech Connect

    Roos, J.L.

    1987-01-01

    This volume gives a overview of artificial intelligence and the use of computers in economics. Areas covered include statistics and macro economic forecasting, the use of automated techniques for economic studies and decision-making processes. The book looks at how much computers are used in business, and how far they will affect the design of markets and the structure of organizations in the future.

  10. 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,…

  11. 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,…

  12. THRESHOLD LOGIC IN ARTIFICIAL INTELLIGENCE

    DTIC Science & Technology

    COMPUTER LOGIC, ARTIFICIAL INTELLIGENCE , BIONICS, GEOMETRY, INPUT OUTPUT DEVICES, LINEAR PROGRAMMING, MATHEMATICAL LOGIC, MATHEMATICAL PREDICTION, NETWORKS, PATTERN RECOGNITION, PROBABILITY, SWITCHING CIRCUITS, SYNTHESIS

  13. Northeast Artificial Intelligence Consortium (NAIC). Volume 9. A Planner System for the Application of Indications and Warning

    DTIC Science & Technology

    1990-12-01

    implementation characteristics of the two versions of our system (POPLAR 1.3 and POPLAR 2.0) that were developed. Goals were set for the implementation of the...basis for an experimental testbed in cognitive modelling. 3.1.2. Setting the Stage The POPLAR planner is a component in a model of an intelligent...implementing POPLAR 1.3, we have been conducting extensive psychological experi- ments seeking to establish the set of ’primitive’ personality characteristics

  14. Parallel multi-computers and artificial intelligence

    SciTech Connect

    Uhr, L.

    1986-01-01

    This book examines the present state and future direction of multicomputer parallel architectures for artificial intelligence research and development of artificial intelligence applications. The book provides a survey of the large variety of parallel architectures, describing the current state of the art and suggesting promising architectures to produce artificial intelligence systems such as intelligence systems such as intelligent robots. This book integrates artificial intelligence and parallel processing research areas and discusses parallel processing from the viewpoint of artificial intelligence.

  15. Artificial intelligence in medicine.

    PubMed

    Hamet, Pavel; Tremblay, Johanne

    2017-04-01

    Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application. Copyright © 2017. Published by Elsevier Inc.

  16. 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…

  17. 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,…

  18. 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…

  19. What Is Artificial Intelligence Anyway?

    ERIC Educational Resources Information Center

    Kurzweil, Raymond

    1985-01-01

    Examines the past, present, and future status of Artificial Intelligence (AI). Acknowledges the limitations of AI but proposes possible areas of application and further development. Urges a concentration on the unique strengths of machine intelligence rather than a copying of human intelligence. (ML)

  20. What Is Artificial Intelligence Anyway?

    ERIC Educational Resources Information Center

    Kurzweil, Raymond

    1985-01-01

    Examines the past, present, and future status of Artificial Intelligence (AI). Acknowledges the limitations of AI but proposes possible areas of application and further development. Urges a concentration on the unique strengths of machine intelligence rather than a copying of human intelligence. (ML)

  1. 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.

  2. Artificial intelligence in hematology.

    PubMed

    Zini, Gina

    2005-10-01

    Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms. The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis. Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction. In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems.

  3. Interdisciplinary Study on Artificial Intelligence.

    DTIC Science & Technology

    1983-07-01

    systems, uiophysics of information processing, cognitive science, and traditional artificial intelligence. The objective behi d this objective was to...information processing, cognitive science, and traditional * artificial intelligence. The objective behind this objective was to provide a vehicle for reviewing...Another departure from ’classical’ neurodynamics must be sought in the strong coupling between the micro and macroscopic scales. No other physical mechanism

  4. 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.

  5. 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…

  6. 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…

  7. 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.

  8. 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.

  9. Training Applications of Artificial Intelligence.

    DTIC Science & Technology

    1987-03-23

    nearifest tLer,sclvCs in ELO r operatii.L costs in the life C’VclE Of the ef’uijjteft. E F re\\ lously rcntione6 ey~ arrle of usingF the 1lirefineer...Ibid., p. 35. 4. Avron Barr and Edward Feigenbaum, The Handbook of Artificial Intelligence, Vol. 1, p. 2. 5. Wissam W. Ahmed, "Theories of Artificial...Barr, Avron and Geigenbaum, Edward A. ed. The Handbook of Arti- ficial Intelligence. Vol. 1. Stanford: heuristech Press. 1981. Gevartner, William B

  10. 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)

  11. 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.

  12. Increasing your artificial intelligence quotient

    SciTech Connect

    Hickingbottom-brown, B.

    1984-01-01

    Practical applications of artificial intelligence technology are now beginning to surface. They will change the way HRD professionals do their work and, in fact, may determine what work they do. The most notable features of AI are discussed, with particular reference to the impact of expert systems. 10 references.

  13. Research and applications: Artificial intelligence

    NASA Technical Reports Server (NTRS)

    Chaitin, L. J.; Duda, R. O.; Johanson, P. A.; Raphael, B.; Rosen, C. A.; Yates, R. A.

    1970-01-01

    The program is reported for developing techniques in artificial intelligence and their application to the control of mobile automatons for carrying out tasks autonomously. Visual scene analysis, short-term problem solving, and long-term problem solving are discussed along with the PDP-15 simulator, LISP-FORTRAN-MACRO interface, resolution strategies, and cost effectiveness.

  14. 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.

  15. Hybrid Applications Of Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Borchardt, Gary C.

    1988-01-01

    STAR, Simple Tool for Automated Reasoning, is interactive, interpreted programming language for development and operation of artificial-intelligence application systems. Couples symbolic processing with compiled-language functions and data structures. Written in C language and currently available in UNIX version (NPO-16832), and VMS version (NPO-16965).

  16. Artificial Intelligence: The Expert Way.

    ERIC Educational Resources Information Center

    Bitter, Gary G.

    1989-01-01

    Discussion of artificial intelligence (AI) and expert systems focuses on their use in education. Characteristics of good expert systems are explained; computer software programs that contain applications of AI are described, highlighting one used to help educators identify learning-disabled students; and the future of AI is discussed. (LRW)

  17. Artificial Intelligence: The Expert Way.

    ERIC Educational Resources Information Center

    Bitter, Gary G.

    1989-01-01

    Discussion of artificial intelligence (AI) and expert systems focuses on their use in education. Characteristics of good expert systems are explained; computer software programs that contain applications of AI are described, highlighting one used to help educators identify learning-disabled students; and the future of AI is discussed. (LRW)

  18. Parellel processing and artificial intelligence

    SciTech Connect

    Reeve, M.

    1989-01-01

    This book reports on parallel processing and artificial intelligence. Topics covered include: Myths and realities about neural computing architectures; Bulk-synchronous parallel computers; Information management in Linda; Information-driven parallel pattern recognition through communicating processes - a case study of classification of wallpaper groups; and Fault tolerant transputer network for image processing.

  19. 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…

  20. Applications of artificial intelligence III

    SciTech Connect

    Gilmore, J.F.

    1986-01-01

    This book presents the papers given at a conference on expert systems and artificial intelligence. Topics considered at the conference included an expert system for computer performance management, real-time image understanding, knowledge-based systems, textured image segmentation, knowledge representation, pattern recognition, robotics, and the computer-aided design of integrated circuits.

  1. 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.

  2. 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…

  3. Artificial Intelligence in Education.

    ERIC Educational Resources Information Center

    Ruyle, Kim E.

    Expert systems have made remarkable progress in areas where the knowledge of an expert can be codified and represented, and these systems have many potentially useful applications in education. Expert systems seem "intelligent" because they do not simply repeat a set of predetermined questions during a consultation session, but will have…

  4. Artificial intelligence and the future.

    PubMed

    Clocksin, William F

    2003-08-15

    We consider some of the ideas influencing current artificial-intelligence research and outline an alternative conceptual framework that gives priority to social relationships as a key component and constructor of intelligent behaviour. The framework starts from Weizenbaum's observation that intelligence manifests itself only relative to specific social and cultural contexts. This is in contrast to a prevailing view, which sees intelligence as an abstract capability of the individual mind based on a mechanism for rational thought. The new approach is not based on the conventional idea that the mind is a rational processor of symbolic information, nor does it require the idea that thought is a kind of abstract problem solving with a semantics that is independent of its embodiment. Instead, priority is given to affective and social responses that serve to engage the whole agent in the life of the communities in which it participates. Intelligence is seen not as the deployment of capabilities for problem solving, but as constructed by the continual, ever-changing and unfinished engagement with the social group within the environment. The construction of the identity of the intelligent agent involves the appropriation or 'taking up' of positions within the conversations and narratives in which it participates. Thus, the new approach argues that the intelligent agent is shaped by the meaning ascribed to experience, by its situation in the social matrix, and by practices of self and of relationship into which intelligent life is recruited. This has implications for the technology of the future, as, for example, classic artificial intelligence models such as goal-directed problem solving are seen as special cases of narrative practices instead of as ontological foundations.

  5. 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.

  6. Neuroscience-Inspired Artificial Intelligence.

    PubMed

    Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew

    2017-07-19

    The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields. Copyright © 2017. Published by Elsevier Inc.

  7. Laboratory robotics and artificial intelligence.

    PubMed

    Isenhour, T L; Marshall, J C

    1990-09-01

    Intelligent robots, which incorporate artificial intelligence in their controlling software, are the next step in bringing the laboratory robot to its full potential. The areas currently under study in our laboratory are improved user interfaces for laboratory robotics, the integration of object-oriented databases into robot control programs, and strategies to optimize multi-step procedures. The ultimate goal of this work is the Standard Robotics Method. The Standard Robotics Method we envision would allow a robotic method to be transferred from one laboratory to another.

  8. 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.

  9. Ten Problems in Artificial Intelligence.

    DTIC Science & Technology

    1987-01-01

    REPORT NUMBER -9 dVT ACCES~iIVN NO𔃻 3 RCCIPIENT’S CATALOG NUMGER 4. TITLE (and Subtile) S YEOF REPORT A PERIOO COvEREC Ten ~ .i in Arti’Ficiz1...7 AD-F1183 552 TEN PROBLEMS IN RTIFICIL INTELLIGENCE(U) VLE UNIV j’ N UN HVEN CT DEPT OF COMPUTER SCIENCE RSCHANK ET AL. JAN 8? VALEU/CSD/RR-514...IET VI Ten Problems in Artificial Intelligence Roger C. Schank Christopher C. Owens YALEU/CSD/RR #514 January 1987 I~~~. -- ’ -.... e"- . .I YALE

  10. 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.

  11. 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.

  12. Programming methodology of artificial intelligence

    SciTech Connect

    Johnson, C.K.

    1980-01-01

    Some of the non-numerical methods developed in the branch of computer science known as artificial intelligence (AI) have been designed specifically for the task of encoding empirical rules of good judgment into a form suitable for machine reasoning. This article summarizes the production-rule technique, one of the principal AI programming techniqes currently used in the knowledge engineering area of AI. An illustrative application involving the interpretation of protein electron-density maps is also described. 4 figures, 1 table.

  13. Artificial Intelligence--Applications in Education.

    ERIC Educational Resources Information Center

    Poirot, James L.; Norris, Cathleen A.

    1987-01-01

    This first in a projected series of five articles discusses artificial intelligence and its impact on education. Highlights include the history of artificial intelligence and the impact of microcomputers; learning processes; human factors and interfaces; computer assisted instruction and intelligent tutoring systems; logic programing; and expert…

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Artificial Intelligence in Autonomous Telescopes

    NASA Astrophysics Data System (ADS)

    Mahoney, William; Thanjavur, Karun

    2011-03-01

    Artificial Intelligence (AI) is key to the natural evolution of today's automated telescopes to fully autonomous systems. Based on its rapid development over the past five decades, AI offers numerous, well-tested techniques for knowledge based decision making essential for real-time telescope monitoring and control, with minimal - and eventually no - human intervention. We present three applications of AI developed at CFHT for monitoring instantaneous sky conditions, assessing quality of imaging data, and a prototype for scheduling observations in real-time. Closely complementing the current remote operations at CFHT, we foresee further development of these methods and full integration in the near future.

  2. 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.

  3. The Artificial Intelligence Applications to Learning Programme.

    ERIC Educational Resources Information Center

    Williams, Noel

    1992-01-01

    Explains the Artificial Intelligence Applications to Learning Programme, which was developed in the United Kingdom to explore and accelerate the use of artificial intelligence (AI) technologies in learning in both the educational and industrial sectors. Highlights include program evaluation, marketing, ownership of information, consortia, and cost…

  4. 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)

  5. 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)

  6. Artificial intelligence for subject interviewing

    SciTech Connect

    Tonn, B.E.; Goeltz, R.; Arrowood, L.; Hake, K.

    1990-01-01

    This paper has two goals: to discuss, in general terms, issues related to applying artificial intelligence (AI) techniques to computerized interviewing; and to describe two AI-based interviewing systems developed at Oak Ridge National Laboratory. With respect to the former, AI techniques can be used effectively to collect data of complex representation, provide flexibility in collecting data, and improve data validity through real-time reviews. One AI system, ARK, elicits subjects' beliefs on an open-ended range of issues and topics through menu-driven, dialogue-based interactions. The other system, LES, elicits, uncertainty assessments related to events, statements and propositions and tailors questions for subjects to explore their uncertainty processing heuristics.

  7. 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.

  8. 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.

  9. 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. Copyright © 2015, American Association for the Advancement of Science.

  10. Synthesizing cellular intelligence and artificial intelligence for bioprocesses.

    PubMed

    Patnaik, P R

    2006-01-01

    Microbial processes operated under realistic conditions are difficult to describe by mechanistic models, thereby limiting their optimization and control. Responses of living cells to their environment suggest that they possess some "innate intelligence". Such responses have been modeled by a cybernetic approach. Furthermore, the overall behavior of a bioreactor containing a population of cells may be described and controlled through artificial intelligence methods. Therefore, it seems logical to combine cybernetic models with artificial intelligence to evolve an integrated intelligence-based strategy that is physiologically more faithful than the current approaches. This possibility is discussed, together with practical considerations favoring a hybrid approach that includes some mathematical modeling.

  11. 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)

  12. 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)

  13. 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.

  14. Artificial Intelligence and the Future Classroom.

    ERIC Educational Resources Information Center

    Green, John O.

    1984-01-01

    Discusses how the power and potential of the computer may shape the classroom of the future by presenting a scenario of a classroom in the year 2001. The role of artificial intelligence in this environment is considered. (JN)

  15. Artificial Intelligence and the Future Classroom.

    ERIC Educational Resources Information Center

    Green, John O.

    1984-01-01

    Discusses how the power and potential of the computer may shape the classroom of the future by presenting a scenario of a classroom in the year 2001. The role of artificial intelligence in this environment is considered. (JN)

  16. What Artificial Intelligence Is Doing for Training.

    ERIC Educational Resources Information Center

    Kirrane, Peter R.; Kirrane, Diane E.

    1989-01-01

    Discusses the three areas of research and application of artificial intelligence: (1) robotics, (2) natural language processing, and (3) knowledge-based or expert systems. Focuses on what expert systems can do, especially in the area of training. (JOW)

  17. What Artificial Intelligence Is Doing for Training.

    ERIC Educational Resources Information Center

    Kirrane, Peter R.; Kirrane, Diane E.

    1989-01-01

    Discusses the three areas of research and application of artificial intelligence: (1) robotics, (2) natural language processing, and (3) knowledge-based or expert systems. Focuses on what expert systems can do, especially in the area of training. (JOW)

  18. 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)

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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)

  5. [Biological versus artificial intelligence: a critical approach].

    PubMed

    Sanvito, W L

    1995-09-01

    After brief considerations about intelligence, a comparative study between biologic and artificial intelligence is made. The specialists in Artificial Intelligence found that intelligence is purely a matter of physical symbol manipulation. The enterprise of Artificial Intelligence aims to understand what we might call Brain Intelligence in terms of concepts and techniques of engineering. However the philosophers believed that computer-machine can have syntax, but can never have semantics. In other words, that they can follow rules, such as those of arithmetic or grammar, but not understand what to us are meanings of symbols, such as words. In the present paper it is stressed that brain/mind complex constitutes a monolithic systemic that functions with emergent properties at several levels of hierarchical organization. These hierarchical levels are non-reducible to one another. They are at least three (neuronal, functional, and semantic), and they function within an interactional plan. The brain/mind complex, which transform informations in meanings, deals with problems by means of both logical and non-logical mechanisms; while logic allows the mind to arrange the elements for reasoning, the non-logical mechanisms (fuzzy logic, heuristics, insights) allows the mind to develop strategies to find solutions. The model for construction of the "intelligent machine" is the operating way of the brain/mind complex, which does not always use logical processes. The role of information science in Artificial Intelligence is to search for knowledge itself (virtual knowledge), rather than to simply attempt a logico-mathematical formalization of knowledge.

  6. Resources in Technology: Introduction to Artificial Intelligence.

    ERIC Educational Resources Information Center

    Technology Teacher, 1987

    1987-01-01

    Introduces the concept of artificial intelligence, discusses where it is currently used, and describes an expert computer system that can be used in the technology laboratory. Included is a learning activity that describes ideas for using intelligent computers as problem-solving tools. (Author/CH)

  7. 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)

  8. Artificial intelligence: the clinician of the future.

    PubMed

    Gallagher, S M

    2001-09-01

    Human beings have long been fascinated with the idea of artificial intelligence. This fascination is fueled by popular films such as Stanley Kubrick's 2001: A Space Odyssey and Stephen Spielberg's recent film, AI. However intriguing artificial intelligence may be, Hubert and Spencer Dreyfus contend that qualities exist that are uniquely human--the qualities thought to be inaccessible to the computer "mind." Patricia Benner further investigated the qualities that guide clinicians in making decisions and assessments that are not entirely evidence-based or grounded in scientific data. Perhaps it is the intuitive nature of the human being that separates us from the machine. The state of artificial intelligence is described herein, along with a discussion of computerized clinical decision-making and the role of the human being in these decisions.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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)

  15. Artificial intelligence: Deep neural reasoning

    NASA Astrophysics Data System (ADS)

    Jaeger, Herbert

    2016-10-01

    The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

  16. 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.

  17. 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…

  18. Applications of artificial intelligence and expert systems

    SciTech Connect

    Not Available

    1987-01-01

    This book contains over 30 papers. Some of the titles are: operating systems for CD/ROM; the impact of optical storage technology on education; the future of expert systems in the financial services industry; the future of compact disk/DC-1 explosive ordinance disposal rendered safe information system; and will artificial intelligence improve computer based training (CBT) development process.

  19. Portable AI Lab for Teaching Artificial Intelligence.

    ERIC Educational Resources Information Center

    Rosner, Michael; Baj, Fabio.

    1993-01-01

    Describes the Portable AI Lab, a computing environment containing artificial intelligence (AI) tools, examples, and documentation for use with university AI courses. Two modules of the lab are highlighted: the automated theorem proving module and the natural language processing module, which includes augmented transition networks. (23 references)…

  20. How Artificial Intelligence Impacts Industrial Education.

    ERIC Educational Resources Information Center

    Ruyle, Kim E.

    1986-01-01

    Explores an artificial intelligence expert system authoring tool called EXSYS. Describes the system, which consists of an instruction manual and several floppy disks containing a rule editor for creating, editing, and experimenting with an expert system, a runtime program for running any existing EXSYS expert system, and several tutorial and…

  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, Computational Thinking, and Mathematics Education

    ERIC Educational Resources Information Center

    Gadanidis, George

    2017-01-01

    Purpose: The purpose of this paper is to examine the intersection of artificial intelligence (AI), computational thinking (CT), and mathematics education (ME) for young students (K-8). Specifically, it focuses on three key elements that are common to AI, CT and ME: agency, modeling of phenomena and abstracting concepts beyond specific instances.…

  3. 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.

  4. Counseling, Artificial Intelligence, and Expert Systems.

    ERIC Educational Resources Information Center

    Illovsky, Michael E.

    1994-01-01

    Considers the use of artificial intelligence and expert systems in counseling. Limitations are explored; candidates for counseling versus those for expert systems are discussed; programming considerations are reviewed; and techniques for dealing with rational, nonrational, and irrational thoughts and feelings are described. (Contains 46…

  5. 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)

  6. Artificial intelligence for medical decision making.

    PubMed

    Kumar, A A; Vasudevan, C

    1990-07-01

    Artificial intelligence techniques find extensive applications in medical decision making and other aspects of health care. A number of successful expert systems have been developed in various disciplines of medicine. This paper gives an overview of expert system techniques, describes some practical systems, and discusses the relevance of such systems in clinical diagnosis and management of diseases.

  7. Artificial Intelligence Applications to Fire Management

    Treesearch

    Don J. Latham

    1987-01-01

    Artificial intelligence could be used in Forest Service fire management and land-use planning to a larger degree than is now done. Robots, for example, could be programmed to monitor for fire and insect activity, to keep track of wildlife, and to do elementary thinking about the environment. Catching up with the fast-changing technology is imperative.

  8. Implications of VISIDEPtm For Artificial Intelligence Applications

    NASA Astrophysics Data System (ADS)

    McLaurin, A. P.; Jones, Edwin R.; Cathey, LeConte

    1987-04-01

    VISIDF is a system for generating true three-dimensional displays on flat-screened devices. Hodges and McAllister, in their article, state clearly that this system is the autostereoscopic alternative to PLZT shutter systems for computer-generated graphic appli-cations. This opens the door to consideration of the system as a component of vision for artificial intelligence applications. In order to understand the potentials of VISIDEP one must, in fact, accept several fundamental assumptions. These are: 1. Perception is an intelligent activity rather than purely stimulus/response. 2. Binocular depth cues are of greater importance to accurate depth interpretation than monocular cues. 3. Depth perception does not require object identification. Each of these assumptions is essential to the application of VISIDEP research in practical operations requiring depth interpretation. The relationships between human vision and perception and the parallax induction generated by VISIDEP technology offer depth in real time to artificial intelligence. Through machine operations on incoming data, the perception of depth is generated in much the same way as the stereoptic data enter the human being, thus providing rapidly quantifiable depth interpretation which is very accurate, perhaps more accurate that human perception of depth. The analysis of a mechanical system in relationship to human approaches to depth perceptions offers the potential of many applications of visually competent artificial intelligence. An additional factor is that the system under discussion is user friendly for human operators as well as requiring minimal reconfiguration of existing equipment and relatively simple software.

  9. The Application of Artificial Intelligence to Contract Management.

    DTIC Science & Technology

    1984-08-01

    INTELLIGENCE 2.1 Definition of Artificial Intelligence. .. .. ... .... 7 2.2 History of Artificial Intelligence. .. .. .. ... .... 8 V2.3 Approaches to...Ig d. not by nuebtb hua aror fot.Itelligence hoeeiOoNoesl eie ineteeeit opeieciei 8 2.2 History of Artificial Intelligence Al is a very young field...down into individual components but must be evaluated as a complete sentence. Some examples of propositions are: the truck has four wheels, the poodle

  10. 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

  11. Projective simulation for artificial intelligence

    NASA Astrophysics Data System (ADS)

    Briegel, Hans J.; de Las Cuevas, Gemma

    2012-05-01

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

  12. Projective simulation for artificial intelligence.

    PubMed

    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.

  13. Artificial intelligence in medicine: the challenges ahead.

    PubMed

    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.

  14. 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.

  15. 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.

  16. 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

  17. Computer vision and artificial intelligence in mammography.

    PubMed

    Vyborny, C J; Giger, M L

    1994-03-01

    The revolution in digital computer technology that has made possible new and sophisticated imaging techniques may next influence the interpretation of radiologic images. In mammography, computer vision and artificial intelligence techniques have been used successfully to detect or to characterize abnormalities on digital images. Radiologists supplied with this information often perform better at mammographic detection or characterization tasks in observer studies than do unaided radiologists. This technology therefore could decrease errors in mammographic interpretation that continue to plague human observers.

  18. 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.

  19. 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.

  20. Applications of artificial intelligence in Japan

    SciTech Connect

    Honda, N.; Ohsato, A.

    1988-01-01

    This article presents a comprehensive report on the recent research and development of artificial intelligence (AI) in Japan, focusing especially on industrial applications. First, historical background of AI research and the future trends of AI Marketing in Japan are reported. Then, industrial applications of AI are introduced with respect to three fields: expert systems, machine translation, and applications of fuzzy set theory. Finally, problems for future research projects are outlined. 21 references.

  1. Artificial Intelligence Software Acquisition Program. Volume 1.

    DTIC Science & Technology

    1987-12-01

    Definition To date, many Artificial Intelligence ( AI ) systems have been developed in university and other. research environments with relatively few...for AI systems which had either completed development or were well underway towards completing a prototype. During the Phase I data collection activity...conducting the AI /KBS system case studies. The questionnaire, contained in Appendix B, is divided into four parts: 1. Introduction 2. Background 3

  2. Artificial intelligence techniques for cancer treatment planning.

    PubMed

    Ardizzone, E; Bonadonna, F; Gaglio, S; Marcenò, R; Nicolini, C; Ruggiero, C; Sorbello, F

    1988-01-01

    An artificial intelligence system, NEWCHEM, for the development of new oncology therapies is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to guide experimentation in the design of new optimal protocols. Further work is being carried out, aimed to embody in the system all the basic knowledge of biology, physiopathology and pharmacology, to reason qualitatively from first principles so as to be able to suggest cancer therapies.

  3. Dictionary of Artificial Intelligence and robotics

    SciTech Connect

    Rosenberg, J.M.

    1986-01-01

    A compilation of over 4000 terms and their definitions relevant to artificial intelligence and robotics. It includes multiple and alternative meanings, abbreviations, acronyms, and foreign expressions. This text supplies both general and specialized entries and cites the relationship between robotics, AI, and computer control terms where applicable. Groups entries containing mutual concepts are together alphabetically, by their common term. It also identifies archaic terms and their preferred alternatives.

  4. 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.

  5. 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.

  6. 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.

  7. 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…

  8. 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…

  9. Artificial intelligence applications in the nuclear industry

    SciTech Connect

    Majumdar, D.

    1988-10-01

    This is a state-of-the-art review of artificial intelligence (AI) applications in the nuclear industry. It was initiated as a result of the American Nuclear Society-sponsored conference on ''Artificial Intelligence and Other Innovative Computer Applications in the Nuclear Industry,'' held in Snowbird, Utah, August 1987. This conference brought together a large number of international experts and showed extensive worldwide applications of expert systems in the nuclear industry. This document is a postconference review and a reflection on the current status and the future of AI in the nuclear industry. Because artificial intelligence techniques can analyze large and complex arrays of information, develop smaller sets of higher-level conclusions, incorporate human expertise, and present information suitable for human intelligence, it is very appropriate for applications in complex nuclear power plant operation. Some advances have already been made in several areas. However, among the many applications in the nuclear industry, there does not appear to be any outstanding application to date such as the ones found in the medical or geological fields. What comes out clearly is that the nuclear industry is experimenting in many areas with the expert system technology and determining its usefulness for the industry. On the international scene, the United States is the current leader in knowledge and applications, followed by Japan and France. However, the Japanese appear to have embraced the AI concept more wholeheartedly. This review encompasses a large number of areas including fault diagnosis, reactor control, plant operation, alarm filtering, accident management, robotics, probabilistic risk assessment, and the human element of expert systems. The potential for useful application of AI technology in the nuclear industry is shown to be promising. 383 refs., 13 figs., 11 tabs.

  10. Artificial Intelligence for VHSIC Systems Design (AIVD) User Reference Manual

    DTIC Science & Technology

    1988-12-01

    AD-A259 518 C;ý I RESEARCH TRIANGLE INSTITUTE ARTIFICIAL INTELLIGENCE FOR IVHSIC SYSTEMS DESIGN (AIVD) DTIC USER REFERENCE MANUAL * ScELECTE fl2...Report 14. SUBJECT TERMS IS. MUMBER OF PAGES VHSIC, Software/hardware codesign, Artificial Intelligence graph transformation, ADAS 14. PRICE CODE 17... ARTIFICIAL INTELLIGENCE FOR I VHSIC SYSTEMS DESIGN (AIVD) USER REFERENCE MANUAL December 1988 I Department of the Army ,,’ U.S. Army Electronics Research

  11. Artificial Intelligence Information Sources for the Beginner and Expert

    DTIC Science & Technology

    1991-05-01

    SUBPLEETAR TMS T bepbhdi" Artificial Intelligence ApplictionsforMlitar Expertis SystemsWilasbrVA 527Mrh 91 12a. DSCRIBTION C AIITY 6 STAEENRTY CTO SECb.T...DLSIFC ISTR BUMATION OC Apnclassified pu ncrlase; ituied inlsife unlimited. Artificial Intelligence Information Sources for the Beginner and Expert...mgivenfdsac.dia.mil UUCP: {...).osu-cisidsac!mgiven ABSTRACT A tremendous amount of information on artificial intelligence is available via different

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Quantum neuromorphic hardware for quantum artificial intelligence

    NASA Astrophysics Data System (ADS)

    Prati, Enrico

    2017-08-01

    The development of machine learning methods based on deep learning boosted the field of artificial intelligence towards unprecedented achievements and application in several fields. Such prominent results were made in parallel with the first successful demonstrations of fault tolerant hardware for quantum information processing. To which extent deep learning can take advantage of the existence of a hardware based on qubits behaving as a universal quantum computer is an open question under investigation. Here I review the convergence between the two fields towards implementation of advanced quantum algorithms, including quantum deep learning.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Artificial intelligence applications at the ICPP

    SciTech Connect

    Johnson, C.E.

    1989-08-02

    Westinghouse Idaho Nuclear Company (WINCO) initiated an aggressive program for artificial intelligence (AI) expert system implementations in 1985. The first expert system, Safety Analysis Methods Advisor (SAMA) was completed in 1986 to help operational safety analysts select analysis methodologies for safety analysis reports. The SAMA expert system was implemented as a rule-based system using a commercial expert system shell. The major benefit of the system is for training new safety analysts. The first successful implementation launched three other expert system projects: a process alarm filtering system, a process control advisor, and a mass spectrometer trouble-shooting advisor. This paper describes the current status of these projects. (GHH)

  4. Applications of artificial intelligence/robotics

    SciTech Connect

    Brown, D.R.; Fowler, D.V.; Park, W.T.; Robinson, A.E.

    1982-01-01

    One hundred applications of artificial intelligence technology and robotics in army combat and combat support that may be possible and worthwhile are identified. These possible applications have been divided into ten categories, and one example in each category has been examined in detail. Research and development plans have been developed showing the basic and applied research that would be needed to make the applications possible. Although the number of possible applications is large, the number of key technology elements is relatively small, and many of the same technology elements are required in many different applications. 19 references.

  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. 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.

  7. 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.

  8. Application of artificial intelligence to the management of urological cancer.

    PubMed

    Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

    2007-10-01

    Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

  9. 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.

  10. Artificial intelligence for analyzing orthopedic trauma radiographs.

    PubMed

    Olczak, Jakub; Fahlberg, Niklas; Maki, Atsuto; Razavian, Ali Sharif; Jilert, Anthony; Stark, André; Sköldenberg, Olof; Gordon, Max

    2017-07-06

    Background and purpose - Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods - We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd's Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network's performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results - All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen's kappa under these conditions was 0.76. Interpretation - This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.

  11. 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.

  12. Marine litter prediction by artificial intelligence.

    PubMed

    Balas, Can Elmar; Ergin, Aysen; Williams, Allan T; Koc, Levent

    2004-03-01

    Artificial intelligence techniques of neural network and fuzzy systems were applied as alternative methods to determine beach litter grading, based on litter surveys of the Antalya coastline (the Turkish Riviera). Litter measurements were categorized and assessed by artificial intelligence techniques, which lead to a new litter categorization system. The constructed neural network satisfactorily predicted the grading of the Antalya beaches and litter categories based on the number of litter items in the general litter category. It has been concluded that, neural networks could be used for high-speed predictions of litter items and beach grading, when the characteristics of the main litter category was determined by field studies. This can save on field effort when fast and reliable estimations of litter categories are required for management or research studies of beaches--especially those concerned with health and safety, and it has economic implications. The main advantages in using fuzzy systems are that they consider linguistic adjectival definitions, e.g. many/few, etc. As a result, additional information inherent in linguistic comments/refinements and judgments made during field studies can be incorporated in grading systems.

  13. 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.

  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 analysis of paraspinal power spectra.

    PubMed

    Oliver, C W; Atsma, W J

    1996-10-01

    OBJECTIVE: As an aid to discrimination of sufferers with back pain an artificial intelligence neural network was constructed to differentiate paraspinal power spectra. DESIGN: Clinical investigation using surface electromyography. METHOD: The surface electromyogram power spectra from 60 subjects, 33 non-back-pain sufferers and 27 chronic back pain sufferers were used to construct a back propagation neural network that was then tested. Subjects were placed on a test frame in 30 degrees of lumbar forward flexion. An isometric load of two-thirds maximum voluntary contraction was held constant for 30 s whilst surface electromyograms were recorded at the level of the L(4-5). Paraspinal power spectra were calculated and loaded into the input layer of a three-layer back propagation network. The neural network classified the spectra into normal or back pain type. RESULTS: The back propagation neural was shown to have satisfactory convergence with a specificity of 79% and a sensitivity of 80%. CONCLUSIONS: Artificial intelligence neural networks appear to be a useful method of differentiating paraspinal power spectra in back-pain sufferers.

  16. 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.

  17. 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,…

  18. Expertise, Task Complexity, and Artificial Intelligence: A Conceptual Framework.

    ERIC Educational Resources Information Center

    Buckland, Michael K.; Florian, Doris

    1991-01-01

    Examines the relationship between users' expertise, task complexity of information system use, and artificial intelligence to provide the basis for a conceptual framework for considering the role that artificial intelligence might play in information systems. Cognitive and conceptual models are discussed, and cost effectiveness is considered. (27…

  19. 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,…

  20. 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…

  1. 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…

  2. 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.

  3. Challenges in applying artificial intelligence methodologies to military operations

    SciTech Connect

    Arrowood, L.F.; Hilliard, M.R.; Hwang, H.L.; Emrich, M.L.

    1986-01-01

    Artificial intelligence methodologies are being applied to support decision making at all levels of military operations. Applications being studied include assessing force readiness, reliability and capability; planning complex missions; and integrating data from multiple sources. Unclassified research is addressing the considerable challenges presented by supporting such decision making in time-sensitive environments. We examine current efforts to utilize artificial intelligence in the military, discuss difficulties which need to be resolved before intelligent systems can become fully operational, and identify potential applications of artificial intelligence for the Military Airlift Command of the US Air Force.

  4. Artificial Intelligence Applications to High-Technology Training.

    ERIC Educational Resources Information Center

    Dede, Christopher

    1987-01-01

    Discusses the use of artificial intelligence to improve occupational instruction in complex subjects with high performance goals, such as those required for high-technology jobs. Highlights include intelligent computer assisted instruction, examples in space technology training, intelligent simulation environments, and the need for adult training…

  5. 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.

  6. 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.

  7. 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.

  8. Intelligent machines: An introductory perspective of artificial intelligence and robotics

    SciTech Connect

    Gevarter, W.B.

    1985-01-01

    This book provides an integrated view of the many diverse aspects of the fields of artificial intelligence (AI) and robotics. It incorporates a summary of the basic concepts utilized in each of the many technical areas; a review of the state-of-the-art; research developments and needs, an indiction of the organizations involved; applications; and a 5-10 year forecast of emerging technology. AI and robotics terms are introduced and immediately defined. The book is designed in a modular format with each chapter essentially complete unto itself. It features extensive use of diagrams, charts, tables that illustrate the concepts and provide material in an easy-to-understand form. Simple illustrative examples clarify and make concrete ideas and material presented. It features an extensive glossary of AI terms and many sets of references and sources of further information.

  9. 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.

  10. 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.

  11. Clinical Note Creation, Binning, and Artificial Intelligence.

    PubMed

    Deliberato, Rodrigo Octávio; Celi, Leo Anthony; Stone, David J

    2017-08-03

    The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans. ©Rodrigo Octávio Deliberato, Leo Anthony Celi, David J Stone. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.08.2017.

  12. 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.

  13. 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.

  14. Robotics and artificial intelligence: Jewish ethical perspectives.

    PubMed

    Rappaport, Z H

    2006-01-01

    In 16th Century Prague, Rabbi Loew created a Golem, a humanoid made of clay, to protect his community. When the Golem became too dangerous to his surroundings, he was dismantled. This Jewish theme illustrates some of the guiding principles in its approach to the moral dilemmas inherent in future technologies, such as artificial intelligence and robotics. Man is viewed as having received the power to improve upon creation and develop technologies to achieve them, with the proviso that appropriate safeguards are taken. Ethically, not-harming is viewed as taking precedence over promoting good. Jewish ethical thinking approaches these novel technological possibilities with a cautious optimism that mankind will derive their benefits without coming to harm.

  15. 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.

  16. 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.

  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. Artificial Intelligence/Robotics Applications to Navy Aircraft Maintenance.

    DTIC Science & Technology

    1984-06-01

    AD-A143 219 ARTIFICIAL INTELLIGENCE/ROBOTICS APPLICATIONS TO NAVV T7T AIRCRAFT HAINTENANCE(U) SRI INTERNATIONAL HENLO PARK CA D R BROWN ET AL...34^! * -.’.-. v.’: ;- • ’_*-••.-. - - m i i • i >«••»......» i 0> CO < Final Report ARTIFICIAL INTELLIGENCE/ROBOTICS APPLICATIONS TO NAVY...8217• . . i * • ’ * ’ ** ’.- *_» "-_ _* _- _• -J> . „• „ (ss3 QB e 25 f//w/ Report ARTIFICIAL INTELLIGENCE/ROBOTICS APPLICATIONS TO NAVY

  19. 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…

  20. 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.

  1. Software Reviews. PC Software for Artificial Intelligence Applications.

    ERIC Educational Resources Information Center

    Epp, Helmut; And Others

    1988-01-01

    Contrasts artificial intelligence and conventional programming languages. Reviews Personal Consultant Plus, Smalltalk/V, and Nexpert Object, which are PC-based products inspired by problem-solving paradigms. Provides information on background and operation of each. (RT)

  2. An Artificial Intelligence Approach to Analyzing Student Errors in Statistics.

    ERIC Educational Resources Information Center

    Sebrechts, Marc M.; Schooler, Lael J.

    1987-01-01

    Describes the development of an artificial intelligence system called GIDE that analyzes student errors in statistics problems by inferring the students' intentions. Learning strategies involved in problem solving are discussed and the inclusion of goal structures is explained. (LRW)

  3. 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.

  4. Artificial Intelligence: Threat or Boon to Radiologists?

    PubMed

    Recht, Michael; Bryan, R Nick

    2017-08-19

    The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  5. [Artificial intelligence applied to radiation oncology].

    PubMed

    Bibault, J-E; Burgun, A; Giraud, P

    2017-05-01

    Performing randomised comparative clinical trials in radiation oncology remains a challenge when new treatment modalities become available. One of the most recent examples is the lack of phase III trials demonstrating the superiority of intensity-modulated radiation therapy in most of its current indications. A new paradigm is developing that consists in the mining of large databases to answer clinical or translational issues. Beyond national databases (such as SEER or NCDB), that often lack the necessary level of details on the population studied or the treatments performed, electronic health records can be used to create detailed phenotypic profiles of any patients. In parallel, the Record-and-Verify Systems used in radiation oncology precisely document the planned and performed treatments. Artificial Intelligence and machine learning algorithms can be used to incrementally analyse these data in order to generate hypothesis to better personalize treatments. This review discusses how these methods have already been used in previous studies. Copyright © 2017 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.

  6. 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.

  7. 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.

  8. Artificial intelligence in mitral valve analysis.

    PubMed

    Jeganathan, Jelliffe; Knio, Ziyad; Amador, Yannis; Hai, Ting; Khamooshian, Arash; Matyal, Robina; Khabbaz, Kamal R; Mahmood, Feroze

    2017-01-01

    Echocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements. The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Retrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital. Echocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA). Three examiners analyzed three end-systolic (ES) frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study. A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant. Examiners did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both). We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention.

  9. Artificial intelligence: contemporary applications and future compass.

    PubMed

    Khanna, Sunali

    2010-08-01

    The clinical use of information technology in the dental profession has increased substantially in the past 10 to 20 years. In most developing countries an insufficiency of medical and dental specialists has increased the mortality of patients suffering from various diseases. Employing technology, especially artificial intelligence technology, in medical and dental application could reduce cost, time, human expertise and medical error. This approach has the potential to revolutionise the dental public health scenario in developing countries. Clinical decision support systems (CDSS) are computer programs that are designed to provide expert support for health professionals. The applications in dental sciences vary from dental emergencies to differential diagnosis of orofacial pain, radiographic interpretations, analysis of facial growth in orthodontia to prosthetic dentistry. However, despite the recognised need for CDSS, the implementation of these systems has been limited and slow. This can be attributed to lack of formal evaluation of the systems, challenges in developing standard representations, cost and practitioner scepticism about the value and feasibility of CDSS. Increasing public awareness of safety and quality has accelerated the adoption of generic knowledge based CDSS. Information technology applications for dental practice continue to develop rapidly and will hopefully contribute to reduce the morbidity and mortality of oral and maxillofacial diseases and in turn impact patient care.

  10. 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.

  11. Applications of Artificial Intelligence in Education--A Personal View.

    ERIC Educational Resources Information Center

    Richer, Mark H.

    1985-01-01

    Discusses: how artificial intelligence (AI) can advance education; if the future of software lies in AI; the roots of intelligent computer-assisted instruction; protocol analysis; reactive environments; LOGO programming language; student modeling and coaching; and knowledge-based instructional programs. Numerous examples of AI programs are cited.…

  12. The Potential Role of Artificial Intelligence Technology in Education.

    ERIC Educational Resources Information Center

    Salem, Abdel-Badeeh M.

    The field of Artificial Intelligence (AI) and Education has traditionally a technology-based focus, looking at the ways in which AI can be used in building intelligent educational software. In addition AI can also provide an excellent methodology for learning and reasoning from the human experiences. This paper presents the potential role of AI in…

  13. Applications of Artificial Intelligence in Education--A Personal View.

    ERIC Educational Resources Information Center

    Richer, Mark H.

    1985-01-01

    Discusses: how artificial intelligence (AI) can advance education; if the future of software lies in AI; the roots of intelligent computer-assisted instruction; protocol analysis; reactive environments; LOGO programming language; student modeling and coaching; and knowledge-based instructional programs. Numerous examples of AI programs are cited.…

  14. Artificial Intelligence, Language and the Study of Knowledge.

    ERIC Educational Resources Information Center

    Goldstein, Ira; And Others

    This paper studies the relationship of artificial intelligence (AI) to the study of language and the representation of the underlying knowledge that supports the comprehension process. It develops the view that intelligence is based on the ability to use large amounts of diverse kinds of knowledge in procedural ways, rather than on the possession…

  15. Artificial Intelligence, Language and the Study of Knowledge.

    ERIC Educational Resources Information Center

    Goldstein, Ira; And Others

    This paper studies the relationship of artificial intelligence (AI) to the study of language and the representation of the underlying knowledge that supports the comprehension process. It develops the view that intelligence is based on the ability to use large amounts of diverse kinds of knowledge in procedural ways, rather than on the possession…

  16. Artificial Intelligence and Educational Technology: A Natural Synergy. Extended Abstract.

    ERIC Educational Resources Information Center

    McCalla, Gordon I.

    Educational technology and artificial intelligence (AI) are natural partners in the development of environments to support human learning. Designing systems with the characteristics of a rich learning environment is the long term goal of research in intelligent tutoring systems (ITS). Building these characteristics into a system is extremely…

  17. Artificial Intelligence in the Allocation of Maintenance Resources for Intelligence Systems

    DTIC Science & Technology

    1987-09-01

    Scheduling: An Investigation in Constraint- Directed Reasoning," Proceedings of the Second National Conference on Artiicial Intelligence , 1982, pp. 155-158... Intelligence in the allocation of 5. Report Date maintenance resources for intelligence systems Sept 87 publication 6. 7. Author(s) 8. Performing...Sponsori 13. Type of Report & Period Covered SAME Final AD-A 197 557 14. 15. Supplerr Presented at conference of Artificial Intelligence East 1987 16

  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. 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.

  1. 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.

  2. Artificial Intelligence in Mitral Valve Analysis

    PubMed Central

    Jeganathan, Jelliffe; Knio, Ziyad; Amador, Yannis; Hai, Ting; Khamooshian, Arash; Matyal, Robina; Khabbaz, Kamal R; Mahmood, Feroze

    2017-01-01

    Background: Echocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements. Aim: The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Settings and Design: Retrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital. Materials and Methods: Echocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA). Three examiners analyzed three end-systolic (ES) frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study. Statistical Analysis: A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant. Results: Examiners did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both). Conclusion: We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention. PMID:28393769

  3. Artificial intelligence-methods in decision and control systems

    SciTech Connect

    Lirov, Y.V.

    1987-01-01

    Artificial intelligence methods were applied to the design and implementation of some decision and control systems. A so-called semantic approach to control and decisions was developed and artificial intelligence methods were used to provide a realizable implementation. These concepts were tested using applications from robust identification and control of time-varying systems, intelligent navigation, and intelligent simulation of differential games. An aspect of a generalized travelling-salesman problem was solved and intelligent simulation of differential games was implemented in Prolog using an example system for automated learning by tactical decision systems in air combat. These implementations were successful and provide several advantages over traditional approaches. The limitations of these concepts were identified and suggestions for future work are made.

  4. Artificial-intelligence methods in decision and control systems

    SciTech Connect

    Lirov, Y.V.

    1987-01-01

    Artificial-intelligence methods were applied to the design and implementation of some decision and control systems. A so-called semantic approach to control and decisions was developed and artificial-intelligence methods were used to provide a realizable implementation. These concepts were tested using applications from robust identification and control of time-varying systems, intelligent navigation, and intelligent simulation of differential games. An aspect of a generalized traveling-salesman problem was solved, and intelligent simulation of differential games was implemented in Prolog using an example system for automated learning by tactical decision systems in air combat. These implementations were successful and provide several advantages over traditional approaches. The limitations of these concepts were identified and suggestions for future work are made.

  5. 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

  6. A Framework for Intelligent Instructional Systems: An Artificial Intelligence Machine Learning Approach.

    ERIC Educational Resources Information Center

    Becker, Lee A.

    1987-01-01

    Presents and develops a general model of the nature of a learning system and a classification for learning systems. Highlights include the relationship between artificial intelligence and cognitive psychology; computer-based instructional systems; intelligent instructional systems; and the role of the learner's knowledge base in an intelligent…

  7. 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

  8. 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.

  9. Tuberculosis control, and the where and why of artificial intelligence.

    PubMed

    Doshi, Riddhi; Falzon, Dennis; Thomas, Bruce V; Temesgen, Zelalem; Sadasivan, Lal; Migliori, Giovanni Battista; Raviglione, Mario

    2017-04-01

    Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB.

  10. 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.

  11. Tuberculosis control, and the where and why of artificial intelligence

    PubMed Central

    Falzon, Dennis; Thomas, Bruce V.; Temesgen, Zelalem; Sadasivan, Lal; Raviglione, Mario

    2017-01-01

    Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB. PMID:28656130

  12. [Artificial intelligence--the knowledge base applied to nephrology].

    PubMed

    Sancipriano, G P

    2005-01-01

    The idea that efficacy efficiency, and quality in medicine could not be reached without sorting the huge knowledge of medical and nursing science is very common. Engineers and computer scientists have developed medical software with great prospects for success, but currently these software applications are not so useful in clinical practice. The medical doctor and the trained nurse live the 'information age' in many daily activities, but the main benefits are not so widespread in working activities. Artificial intelligence and, particularly, export systems charm health staff because of their potential. The first part of this paper summarizes the characteristics of 'weak artificial intelligence' and of expert systems important in clinical practice. The second part discusses medical doctors' requirements and the current nephrologic knowledge bases available for artificial intelligence development.

  13. Artificial intelligence approaches for rational drug design and discovery.

    PubMed

    Duch, Włodzisław; Swaminathan, Karthikeyan; Meller, Jarosław

    2007-01-01

    Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.

  14. 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.

  15. 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.

  16. Artificial intelligence (AI) systems for interpreting complex medical datasets.

    PubMed

    Altman, R B

    2017-05-01

    Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability. © 2017 ASCPT.

  17. 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.

  18. 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.

  19. 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.

  20. Artificial intelligence and large scale computation: A physics perspective

    NASA Astrophysics Data System (ADS)

    Hogg, Tad; Huberman, B. A.

    1987-12-01

    We study the macroscopic behavior of computation and examine both emergent collective phenomena and dynamical aspects with an emphasis on software issues, which are at the core of large scale distributed computation and artificial intelligence systems. By considering large systems, we exhibit novel phenomena which cannot be foreseen from examination of their smaller counterparts. We review both the symbolic and connectionist views of artificial intelligence, provide a number of examples which display these phenomena, and resort to statistical mechanics, dynamical systems theory and the theory of random graphs to elicit the range of possible behaviors.

  1. 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)

  2. 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)

  3. Artificial Intelligence Applications to Information Warfare.

    DTIC Science & Technology

    2007-11-02

    still is work to be done, intelligent agents may someday manage the information flow, be the core technology in network firewalls, and contribute to overall network security through continuous Red Team vulnerability assessments.

  4. Artificial intelligence and synthetic biology: A tri-temporal contribution.

    PubMed

    Bianchini, Francesco

    2016-10-01

    Artificial intelligence can make numerous contributions to synthetic biology. I would like to suggest three that are related to the past, present and future of artificial intelligence. From the past, works in biology and artificial systems by Turing and von Neumann prove highly interesting to explore within the new framework of synthetic biology, especially with regard to the notions of self-modification and self-replication and their links to emergence and the bottom-up approach. The current epistemological inquiry into emergence and research on swarm intelligence, superorganisms and biologically inspired cognitive architecture may lead to new achievements on the possibilities of synthetic biology in explaining cognitive processes. Finally, the present-day discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of notions such as "life", "cognition", "artificial" and "natural", as well as their interconnections in theoretical synthetic biology.

  5. 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.

  6. 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.

  7. 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…

  8. 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…

  9. 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.…

  10. 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)

  11. Application of artificial intelligence to risk analysis for forested ecosystems

    Treesearch

    Daniel L. Schmoldt

    2001-01-01

    Forest ecosystems are subject to a variety of natural and anthropogenic disturbances that extract a penalty from human population values. Such value losses (undesirable effects) combined with their likelihoods of occurrence constitute risk. Assessment or prediction of risk for various events is an important aid to forest management. Artificial intelligence (AI)...

  12. A Course in Artificial Intelligence in Process Engineering.

    ERIC Educational Resources Information Center

    Venkatasubramanian, V.

    1986-01-01

    Describes a course on artificial intelligence (AI) in process engineering taught at Columbia University to chemical engineering students, using an AI methodology known as Knowledge-Based Expert Systems (KBES). Provides a description of the course, the lecture topics, and a synopsis of some of the student projects. (TW)

  13. 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…

  14. 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.

  15. 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…

  16. 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.

  17. 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…

  18. 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…

  19. 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…

  20. 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…

  1. Teaching Artificial Intelligence and Expert Systems: Concepts in Library Curricula.

    ERIC Educational Resources Information Center

    Kranch, Douglas A.

    1992-01-01

    Survey of institutions offering a bachelor's or higher degree in library science showed that the higher the level of the program, the more likely that artificial intelligence/expert systems (AI/ES) courses would be offered. Study concludes that all master's and doctoral level programs should include AI/ES units, and that greater emphasis should be…

  2. An Artificial Intelligence Course for Liberal Arts Students.

    ERIC Educational Resources Information Center

    Skala, Helen

    1988-01-01

    Outlines a course in artificial intelligence for liberal arts students that has no programing prerequisites. Topics and projects included in the course are described, including problem solving; natural language; expert systems; image understanding, or character recognition; and robotic systems. (28 references) (Author/LRW)

  3. 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…

  4. Social Studies and Emerging Paradigms: Artificial Intelligence and Consciousness Education.

    ERIC Educational Resources Information Center

    Braun, Joseph A., Jr.

    1987-01-01

    Asks three questions: (1) Are machines capable of thinking as people do? (2) How is the thinking of computers similar and different from human thinking? and (3) What exactly is thinking? Examines research in artificial intelligence. Describes the theory and research of consciousness education and discusses an emerging paradigm for human thinking…

  5. Artificial intelligence techniques for the control of cancer cells.

    PubMed

    Nicolini, C; Gaglio, S; Ruggiero, C

    1989-04-01

    NEWCHEM, an artificial intelligence system for the control of cancer cell growth, is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to develop optimal strategies for the selective control of cancer cell through qualitative reasoning from first principles at cellular level.

  6. 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…

  7. 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…

  8. Proceedings of the third conference on artificial intelligence applications

    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 a knowledge-based imaging system for electromagnetic testing, natural language processing, diagnostic techniques, knowledge acquisition, manufacturing, real-time programming, robotics, search, uncertainty, design, planning, and software.

  9. An Artificial Intelligence Course for Liberal Arts Students.

    ERIC Educational Resources Information Center

    Skala, Helen

    1988-01-01

    Outlines a course in artificial intelligence for liberal arts students that has no programing prerequisites. Topics and projects included in the course are described, including problem solving; natural language; expert systems; image understanding, or character recognition; and robotic systems. (28 references) (Author/LRW)

  10. 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…

  11. 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…

  12. 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.

  13. Artificial intelligence technologies for power system operations. Final report

    SciTech Connect

    Talukdar, S.N.; Cardozo, E.

    1986-01-01

    Researchers in this study examined the potential of artificial intelligence (AI) technologies for improving problem-solving strategies in 16 power system operations. To demonstrate the use of AI in the area they considered most promising, contingency selection-security assessment, they also developed two programs - one to simulate network protection schemes, the other to diagnose faults.

  14. 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.…

  15. 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…

  16. 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)

  17. The Seeds of Artificial Intelligence. SUMEX-AIM.

    ERIC Educational Resources Information Center

    Research Resources Information Center, Rockville, MD.

    Written to provide an understanding of the broad base of information on which the artificial intelligence (AI) branch of computer science rests, this publication presents a general view of AI, the concepts from which it evolved, its current abilities, and its promise for research. The focus is on a community of projects that use the SUMEX-AIM…

  18. Ethical Implications of an Experiment in Artificial Intelligence.

    ERIC Educational Resources Information Center

    Levinson, Stephen E.

    2003-01-01

    Revisits the classic debate on whether there can be an artificial creation that behaves and uses language with intelligence and agency. Argues that many moral and spiritual objections to this notion are not grounded either ethically or empirically. (Author/VWL)

  19. 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.

  20. 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,…

  1. 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…

  2. The Army’s Activities in Artificial Intelligence/Robotics,

    DTIC Science & Technology

    1982-08-27

    approach is to cast artificial intelligence as advanced computer software applicable to classes of nondeterministic problems such as natural language ...Semi-autonomous RPV target aquisition system - Unattended Forward observer et - - Expert maintenance systems - Manufacturing (GOCO, GOGO plants...Automated message analysis and distribution - Semi-autonomous air defense systems - Natural language query to C31 data bases - Computer-assisted

  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. Methodology Investigation of AI(Artificial Intelligence) Test Officer Support Tool. Volume 1

    DTIC Science & Technology

    1989-03-01

    American Association for Artificial inteligence A! ............. Artificial inteliigence AMC ............ Unt:ed States Army Maeriel Comand ASL...block number) FIELD GROUP SUB-GROUP Artificial Intelligence, Expert Systems Automated Aids to Testing 9. ABSTRACT (Continue on reverse if necessary and...identify by block number) This report covers the application of Artificial Intelligence-Techniques to the problem of creating automated tools to

  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 Intelligence Applied to the Command, Control, Communications, and Intelligence of the U.S. Central Command.

    DTIC Science & Technology

    1983-06-06

    these components will be presented. 4.17 °°,. CHAPTER III FOOTNOTES 1. Arron Barr and Edward A. Feigenbaum, eds., Te Handbook gf Artificial Inteligence ol...RD-R137 205 ARTIFICIAL INTELLIGENCE APPLIED TO THE COMIMAND CONTROL i/i COMMUNICATIONS RND..(U) ARMY WAR COLL CARLISLE BARRACKS U PA J N ENVART 06...appropriate mlitary servic or *swesmment aency. ARTIFICIAL INTELLIGENCE APPLIED TO THE COMMAND, CONTROL, COMMUNICATIONS, AND INTELLIGENCE OF THE U.S. CENTRAL

  8. 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.

  9. Teaching artificial intelligence to read electropherograms.

    PubMed

    Taylor, Duncan; Powers, David

    2016-11-01

    Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells us about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. A technique that lends itself well to such a task of classification in the face of vast amounts of data is the use of artificial neural networks. These networks, inspired by the workings of the human brain, have been increasingly successful in analysing large datasets, performing medical diagnoses, identifying handwriting, playing games, or recognising images. In this work we demonstrate the use of an artificial neural network which we train to 'read' electropherograms and show that it can generalise to unseen profiles. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. Modelling fuel cell performance using artificial intelligence

    NASA Astrophysics Data System (ADS)

    Ogaji, S. O. T.; Singh, R.; Pilidis, P.; Diacakis, M.

    Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed.

  11. 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.

  12. 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.

  13. 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.

  14. Artificial intelligence and robotics in high throughput post-genomics.

    PubMed

    Laghaee, Aroosha; Malcolm, Chris; Hallam, John; Ghazal, Peter

    2005-09-15

    The shift of post-genomics towards a systems approach has offered an ever-increasing role for artificial intelligence (AI) and robotics. Many disciplines (e.g. engineering, robotics, computer science) bear on the problem of automating the different stages involved in post-genomic research with a view to developing quality assured high-dimensional data. We review some of the latest contributions of AI and robotics to this end and note the limitations arising from the current independent, exploratory way in which specific solutions are being presented for specific problems without regard to how these could be eventually integrated into one comprehensible integrated intelligent system.

  15. 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.

  16. Learning comunication strategies for distributed artificial intelligence

    NASA Astrophysics Data System (ADS)

    Kinney, Michael; Tsatsoulis, Costas

    1992-08-01

    We present a methodology that allows collections of intelligent system to automatically learn communication strategies, so that they can exchange information and coordinate their problem solving activity. In our methodology communication between agents is determined by the agents themselves, which consider the progress of their individual problem solving activities compared to the communication needs of their surrounding agents. Through learning, communication lines between agents might be established or disconnected, communication frequencies modified, and the system can also react to dynamic changes in the environment that might force agents to cease to exist or to be added. We have established dynamic, quantitative measures of the usefulness of a fact, the cost of a fact, the work load of an agent, and the selfishness of an agent (a measure indicating an agent's preference between transmitting information versus performing individual problem solving), and use these values to adapt the communication between intelligent agents. In this paper we present the theoretical foundations of our work together with experimental results and performance statistics of networks of agents involved in cooperative problem solving activities.

  17. 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.

  18. Northeast Artificial Intelligence Consortium (NAIC). Volume 7. Automatic Photointerpretation

    DTIC Science & Technology

    1990-12-01

    in genetic search. The transformation that produces the temporal sequence of messages is based upon a cellular automata simulation of wavefronts...ansformng spatial structure into temporal structure. The algorithm is based upon a cellular automata simulation of waverronts emanating from point sources...fronts permits the computation of non-planar graph structures. A significant aspect of the algorithm is its use of cellular automata transformations

  19. Summary of the Northeast Artificial Intelligence Consortium (NAIC)

    DTIC Science & Technology

    1991-09-01

    rwwmg ranjac , s ,ct vm V w- ta so’l-e- gattwrig anl r %w-,"g Tor d"n reec " ,T rr wid rmwmV tlcdeax, of rformrou Serd cTrrts r wdeg u- s b’u-d e,te cr m...y Orw aspec c: ’ded=of iormr ru.dig & ux for redc"- & ,ase to WastVcn Hoec e*tts S wuvc DcIaraoe for forz Operg us a iRepl . 12s 5 .-e-i-c D Nis HkwW... S l* 1204. Arkxpcr VA 202432, n to &is Office of Mwvgrr rt and BLcxg Ppwwork Reducr1 Prj ;t (0704-010 ). WsagwigtoR OC 205C3 . AGENCY USE ONLY

  20. Synthetic biology routes to bio-artificial intelligence

    PubMed Central

    Zaikin, Alexey; Saka, Yasushi; Romano, M. Carmen; Giuraniuc, Claudiu V.; Kanakov, Oleg; Laptyeva, Tetyana

    2016-01-01

    The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular ‘teachers’ and ‘students’ is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI). PMID:27903825

  1. 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.

  2. Evaluation of artificial intelligence based models for chemical biodegradability prediction.

    PubMed

    Baker, James R; Gamberger, Dragan; Mihelcic, James R; Sabljić, Aleksandar

    2004-12-31

    This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

  3. 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.

  4. 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.

  5. Northeast Artificial Intelligence Consortium Annual Report 1986: Artificial Intelligence Applications to Speech Recognition. Volume 7

    DTIC Science & Technology

    1988-06-01

    Multivariate Statistical Methods ............. 7-29 7.3.4 Speaker-Independent Vowel Classification ..... 732 7.3.5 Results of Recognition Tests...therefore, provide reliable categories for classification. Other traditional phoneme classes (e.g. vowels , glides) are harder to pin down and lead to...typical string might contain phonemes like "f sound" followed by "a glide" followed by "something vowel -like" followed by "not a stop." A pure

  6. Northeast Artificial Intelligence Consortium Annual Report 1986. Volume 3. Distributed Artificial Intelligence for Communication Network Management

    DTIC Science & Technology

    1988-06-01

    Director delegates the task of controlling Cast member activity to the Grip. The job of routing messages to and from Cast members is given to the Messenger...and s-goals it has instantiated. Relative to each of its goals, it knows a number of alternatives for goal 0 satisfiction . An alternative is comprised

  7. Northeast Artificial Intelligence Consortium Annual Report 1987. Volume 6. Artificial Intelligence Applications to Speech Recognition

    DTIC Science & Technology

    1989-03-01

    Waibel (1981), in addition to connected word recognition by Myers et al. (1981), Ney (1984), and Watari (1986). An unknown utterance and a reference...Techniques in Isolated Word Speech Recognition Systems", Research paper, Carnegie-Mellon University, CMU-CS-81-125, June 1981. Watari , M. "New DP

  8. 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.

  9. 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.

  10. 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.

  11. Design Issues in Parallel Architectures for Artificial Intelligence.

    DTIC Science & Technology

    1983-11-01

    control-driven, data-driven, and demand-driven styles of computation. In a related development, investigation of a shared financial account example...actors with changing local states. While working with the shared financial account example. we implemented a new approach to actor state changes...Lab, 1970. [’Theriault &1.1 Theriault.. D. b1rnies in the fleqign and Implementation of Act-2. Technic0l Reprt 728, MIT Artificial Intelligence Laboratory, June, 1983. IDAT.I

  12. 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.

  13. JPRS Report, Science & Technology, Japan, 7th Artificial Intelligence Symposium.

    DTIC Science & Technology

    1988-09-14

    294030 JPRS-JST-88-020 14 SEPTEMBER 1988 ■■■■■I ■■■■■fl FOREIGN BROADCAST INFORMATION SERVICE , JPRS Report— Science & Technology Japan...INSPECTED 6 SPRINGFIELD, VA. 22161 JPRS-JST-88-020 14 SEPTEMBER 1988 SCIENCE & TECHNOLOGY JAPAN 7th ARTIFICIAL INTELLIGENCE SYMPOSIUM...43063809a Tokyo DAINANAKAI CHISHIKI KOGAKU SYMPOSIUM in Japanese 22-23 Mar 88 pp 1-6 [Article by Shigenbu Kobayashi, Tokyo Institute of Technology , and

  14. 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.

  15. [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.

  16. 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.

  17. Advanced Methods of Online Searching for Artificial Intelligence Information

    DTIC Science & Technology

    1991-05-01

    wide basis. It will be shown how these networks are used in discussion groups, mailing lists, file transfers, mailservers , and databases. 14ru RMs 15...in discussion groups, mailing lists, file transfers, mailservers , and databases. INTRODUCTION A person working in artificial intelligence today has...34 one, and get either the file "internet.library" (ASCII format) or "internet.library.ps" (in Postscript format). LIDO mailserver The University of

  18. 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.

  19. 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.

  20. Application of temporal LNC logic in artificial intelligence

    NASA Astrophysics Data System (ADS)

    Adamek, Marek; Mulawka, Jan

    2016-09-01

    This paper presents the temporal logic inference engine developed in our university. It is an attempt to demonstrate implementation and practical application of temporal logic LNC developed in Cardinal Stefan Wyszynski University in Warsaw.1 The paper describes the fundamentals of LNC logic, architecture and implementation of inference engine. The practical application is shown by providing the solution for popular in Artificial Intelligence problem of Missionaries and Cannibals in terms of LNC logic. Both problem formulation and inference engine are described in details.

  1. Application of Artificial Intelligence to Improve Aircraft Survivability.

    DTIC Science & Technology

    1985-12-01

    AD-A164 172 APPLICATION OF ARTIFICIAL INTELLIGENCE TO IMPROVE 1/1 AIRCRAFT SURVASRILITY(U) NAVAL POSTGRADUATE SCHOOL UNCR7SIFEDMONTEREY CA N L DECKER...4 5- * . . . . . . 5~5~ * . . - -- &:~~-::-~&~ S.- ~ ~ S. . ~ ~.’ ~ VV ~ NAVAL POSTGRADUATE SCHOOL Monterey, California DTIC FEB 1406 D4 THESIS... School 6 7 Naval Postgraduate School 6C ADDRESS (City, State. and ZIP Code) 7b. ADDRESS (City, State, and ZIP Code) Monterey, California 93943-5100

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Artificial intelligence in medical diagnosis: the INTERNIST/CADUCEUS approach.

    PubMed

    Banks, G

    1986-01-01

    The development of computers has provided a potential tool to assist in the management of the information explosion in medicine. The field of medical diagnosis is intellectually challenging and has attracted the attention of computer scientists interested in building expert systems using artificial intelligence techniques. This paper reviews some of the problems of medical diagnosis and discusses examples of programs representing different approaches to solving these problems. The programs developed in our laboratory, INTERNIST-1/CADUCEUS, are discussed in some detail.

  9. 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

  10. Distinct neurocognitive strategies for comprehensions of human and artificial intelligence.

    PubMed

    Ge, Jianqiao; Han, Shihui

    2008-07-30

    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.

  11. Artificial intelligence and engineering curricula - are changes needed

    SciTech Connect

    Jenkins, J.P.

    1988-01-01

    The purpose of this paper is to identify the expected impact of artificial intelligence (AI) on curricula and training courses. From this examination, new elements are proposed for the academic preparation and training of engineers who will evaluate and use these systems and capabilities. Artificial intelligence, from an operational viewpoint, begins with a set of rules governing the operation of logic, implemented via computer software and userware. These systems apply logic and experience to handling problems in an intelligent approach, especially when the number of alternatives to problem solution is beyond the scope of the human user. Usually, AI applications take the form of expert systems. An expert system embodies in the computer the knowledge-based component of an expert, such as domain knowledge and reasoning techniques, in such a form that the system can offer intelligent advice and, on demand, justify its own line of reasoning. Two languages predominate, LISP and Prolog. The AI user may interface with the knowledge base via one of these languages or by means of menu displays, cursor selections, or other conventional user interface methods.

  12. 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

  13. Artificial neural network intelligent method for prediction

    NASA Astrophysics Data System (ADS)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  14. Artificial intelligence in IVF: a need.

    PubMed

    Siristatidis, Charalampos; Pouliakis, Abraham; Chrelias, Charalampos; Kassanos, Dimitrios

    2011-08-01

    Predicting the outcome of in-vitro fertilization (IVF) treatment is an extremely semantic issue in reproductive medicine. Discrepancies in results among reproductive centres still exist making the construction of new systems capable to foresee the desired outcome a necessity. As such, artificial neural networks (ANNs) represent a combination of a learning, self-adapting, and predicting machine. In this review hypothesis paper we summarize the past efforts of the ANNs systems to predict IVF outcomes. This will be considered together with other statistical models, such as the ensemble techniques, Classification And Regression Tree (CART) and regression analysis techniques, discriminant analysis, and case based reasoning systems. We also summarize the various inputs that have been employed as parameters in these studies to predict the IVF outcome. Finally, we report our attempt to construct a new ANN architecture based on the Learning Vector Quantizer promising good generalization: a system filled by a complete data set of our IVF unit, formulated parameters most commonly used in similar studies, trained by a network expert, and evaluated in terms of predictive power.

  15. 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.

  16. Computer Vision for Artificially Intelligent Robotic Systems

    NASA Astrophysics Data System (ADS)

    Ma, Chialo; Ma, Yung-Lung

    1987-04-01

    In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts -- position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed bye the main control unit. In Pulse-Echo Signal Process Unit, we ultilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by u law coding method, and this data together with delay time T, angle information OH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main

  17. Perspectives on Research in Artificial Intelligence and Artificial General Intelligence Relevant to DoD

    DTIC Science & Technology

    2017-01-01

    Intelligence (AI) is conventionally, if loosely, defined as intelligence exhibited by machines . Operationally, it can be defined as those areas of R&D...Knowledge Representation and Reasoning (KRR). The field of Machine Learning (ML) is a foundational basis for AI. While this is not a complete list, it...researchers or total funding, that seeks to build machines that can successfully perform any task that a human might do. Where AI is oriented around

  18. 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.

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

    PubMed

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

    2016-04-12

    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.

  20. 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

  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. The Heuristic of George Polya and Its Relation to Artificial Intelligence

    DTIC Science & Technology

    1981-07-01

    episodic memory. Psychological Review, 1973, 80, 352-373. Waterman, D. & Newell, A. Protocol analysis as a task for artficial intelligence . Artificial...THE HEURISTIC OF GEORGE POLYA AND ITS RELATION TO ARTIFICIAL INTELLIGENCE Allen Newell July 1981 DEPARTMENT of COMPUTER SCIENCE DT1C...81 11 03 196 ^JjJ CMU-CS-81- 133 THE ÜEURISTIC OF GEORGE POLYA AND ITS RELATION TO ARTIFICIAL INTELLIGENCE 10 i Allen/Newell TÄsr s

  3. 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.

  4. A Novel Artificial Intelligence System for Endotracheal Intubation.

    PubMed

    Carlson, Jestin N; Das, Samarjit; De la Torre, Fernando; Frisch, Adam; Guyette, Francis X; Hodgins, Jessica K; Yealy, Donald M

    2016-01-01

    Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/artificial intelligence has helped to automate the detection of other visual structures but its utility with ETI is unknown. We sought to test the accuracy of various computer algorithms in identifying the glottic opening, creating a tool that could aid successful intubation. We collected a convenience sample of providers who each performed ETI 10 times on a mannequin using a video laryngoscope (C-MAC, Karl Storz Corp, Tuttlingen, Germany). We recorded each attempt and reviewed one-second time intervals for the presence or absence of the glottic opening. Four different machine learning/artificial intelligence algorithms analyzed each attempt and time point: k-nearest neighbor (KNN), support vector machine (SVM), decision trees, and neural networks (NN). We used half of the videos to train the algorithms and the second half to test the accuracy, sensitivity, and specificity of each algorithm. We enrolled seven providers, three Emergency Medicine attendings, and four paramedic students. From the 70 total recorded laryngoscopic video attempts, we created 2,465 time intervals. The algorithms had the following sensitivity and specificity for detecting the glottic opening: KNN (70%, 90%), SVM (70%, 90%), decision trees (68%, 80%), and NN (72%, 78%). Initial efforts at computer algorithms using artificial intelligence are able to identify the glottic opening with over 80% accuracy. With further refinements, video laryngoscopy has the potential to provide real-time, direction feedback to the provider to help guide successful ETI.

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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

  10. 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.

  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. 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.

  14. 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.

  15. 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.

  16. Effect of altering local protein fluctuations using artificial intelligence

    NASA Astrophysics Data System (ADS)

    Nishiyama, Katsuhiko

    2017-03-01

    The fluctuations in Arg111, a significantly fluctuating residue in cathepsin K, were locally regulated by modifying Arg111 to Gly111. The binding properties of 15 dipeptides in the modified protein were analyzed by molecular simulations, and modeled as decision trees using artificial intelligence. The decision tree of the modified protein significantly differed from that of unmodified cathepsin K, and the Arg-to-Gly modification exerted a remarkable effect on the peptide binding properties. By locally regulating the fluctuations of a protein, we may greatly alter the original functions of the protein, enabling novel applications in several fields.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Natural language processing in psychiatry. Artificial intelligence technology and psychopathology.

    PubMed

    Garfield, D A; Rapp, C; Evens, M

    1992-04-01

    The potential benefit of artificial intelligence (AI) technology as a tool of psychiatry has not been well defined. In this essay, the technology of natural language processing and its position with regard to the two main schools of AI is clearly outlined. Past experiments utilizing AI techniques in understanding psychopathology are reviewed. Natural language processing can automate the analysis of transcripts and can be used in modeling theories of language comprehension. In these ways, it can serve as a tool in testing psychological theories of psychopathology and can be used as an effective tool in empirical research on verbal behavior in psychopathology.

  2. 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.

  3. 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.

  4. Outlier Detection with a Hybrid Artificial Intelligence Method

    NASA Astrophysics Data System (ADS)

    Mejía-Lavalle, Manuel; Obregón, Ricardo Gómez; Vivar, Atlántida Sánchez

    We propose a simple and efficient hybrid artificial intelligence method to detect exceptional data. The proposed method includes a novel end-user explanation feature. After various attempts, the best design was based on an unsupervised learning schema, which uses an hybrid adaptation of the Artificial Neural Network paradigms, the Case Based Reasoning methodology, the Data Mining area, and the Expert System shells. In our method, the cluster that contains the smaller number of instances is considered as outlier data. The method provides an explanation to the end user about why this cluster is exceptional regarding to the data universe. The proposed method has been tested and compared successfully not only with well-known academic data, but also with a real and very large financial database that contains attributes with numerical and categorical values.

  5. Synthetic biology routes to bio-artificial intelligence.

    PubMed

    Nesbeth, Darren N; Zaikin, Alexey; Saka, Yasushi; Romano, M Carmen; Giuraniuc, Claudiu V; Kanakov, Oleg; Laptyeva, Tetyana

    2016-11-30

    The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular 'teachers' and 'students' is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI). © 2016 The Author(s). This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

  6. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. 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

  8. An advanced artificial intelligence tool for menu design.

    PubMed

    Khan, Abdus Salam; Hoffmann, Achim

    2003-01-01

    The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hints to the system in the context of actual problem instances during menu design using menus stored in a so-called Case Base. Our system incorporates Case-Based Reasoning (CBR), an Artificial Intelligence (AI) technique developed to mimic human problem solving behaviour. Ripple Down Rules (RDR) are a proven technique for the acquisition of classification knowledge from expert directly while they are using the system, which complement CBR in a very fruitful way. This combination allows the incremental improvement of the menu design system while it is already in routine use. We believe MIKAS allows better dietary practice by leveraging a dietitian's skills and expertise. As such MIKAS has the potential to be helpful for any institution where dietary advice is practised.

  9. On working through: a model from artificial intelligence.

    PubMed

    Galatzer-Levy, R M

    1988-01-01

    Working through is centrally important to clinical psychoanalysis. It is inadequately explained in analytic theory. An artificial intelligence model of the process is proposed. Models of problem solving show that the complexity of necessary computation is an important determinant of how a problem is solved. Not optimal, but only good enough solutions are usually found. The quality of solutions depends on the time and resources available. Generally it is far easier to use existing methods than to develop new approaches. When problems must be solved in an emergency fashion, as in trauma, poor solutions are likely to emerge. In studying the annealing of metals and other complex optimization problems, a process, the Boltzman algorithm, was discovered, which continues the search for better solutions while gradually developing a coherent structure of the overall solution. The algorithm provides a model both for psychoanalytic working through and for the normally ongoing process of psychological development and reworking whose deficiency is characteristic of much psychopathology. Working through in the analytic situation is the reactivation of this normal process, and a good analytic outcome is achieved when the process can continue without the analyst. Properties of the Boltzman algorithm clarify such concepts as "optimal" frustration and anxiety which correspond to working in the area where the stable but not rigid structures emerge in the algorithms operation. These studies are an example of how computer science and artificial intelligence are a potentially rich source for psychoanalytic theory.

  10. Artificial intelligence-assisted occupational lung disease diagnosis.

    PubMed

    Harber, P; McCoy, J M; Howard, K; Greer, D; Luo, J

    1991-08-01

    An artificial intelligence expert-based system for facilitating the clinical recognition of occupational and environmental factors in lung disease has been developed in a pilot fashion. It utilizes a knowledge representation scheme to capture relevant clinical knowledge into structures about specific objects (jobs, diseases, etc) and pairwise relations between objects. Quantifiers describe both the closeness of association and risk, as well as the degree of belief in the validity of a fact. An independent inference engine utilizes the knowledge, combining likelihoods and uncertainties to achieve estimates of likelihood factors for specific paths from work to illness. The system creates a series of "paths," linking work activities to disease outcomes. One path links a single period of work to a single possible disease outcome. In a preliminary trial, the number of "paths" from job to possible disease averaged 18 per subject in a general population and averaged 25 per subject in an asthmatic population. Artificial intelligence methods hold promise in the future to facilitate diagnosis in pulmonary and occupational medicine.

  11. 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.

  12. 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…

  13. 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…

  14. "It's Going to Kill Us!" and Other Myths about the Future of Artificial Intelligence

    ERIC Educational Resources Information Center

    Atkinson, Robert D.

    2016-01-01

    Given the promise that artificial intelligence (AI) holds for economic growth and societal advancement, it is critical that policymakers not only avoid retarding the progress of AI innovation, but also actively support its further development and use. This report provides a primer on artificial intelligence and debunks five prevailing myths that,…

  15. 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…

  16. Artificial Intelligence and Its Potential as an Aid to Vocational Training and Education.

    ERIC Educational Resources Information Center

    Aleksander, I.; And Others

    This document contains a series of papers which attempt to de-mystify the subject of artificial intelligence and to show how some countries in the European Community (EC) are approaching the promotion of development and application of artificial intelligence systems that can be used as an aid in vocational training programs, as well as to…

  17. 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

  18. Artificial intelligence in sports biomechanics: new dawn or false hope?

    PubMed

    Bartlett, Roger

    2006-12-15

    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 PointsExpert 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.

  19. 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.

  20. Knowledge Based Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle.

    DTIC Science & Technology

    1988-04-13

    Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle Mark S. Fox, Nizwer Husain, Malcolm...McRoberts and Y.V.Reddy CMU-RI-TR-88-5 Intelligent Systems Laboratory The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania D T T 13...years of research in the application of Artificial Intelligence to Simulation. Our focus has been in two areas: the use of Al knowledge representation

  1. 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.

  2. A Model for Comparing Game Theory and Artificial Intelligence Decision Making Processes

    DTIC Science & Technology

    1989-12-01

    Matrix 7.5 Summary This chapter discussed an initial comparison of the game theory and artiicial intelligence decision techniques. The measure of...00 DTIC V ELECTE INJ DEC 15 1989 ID A MODEL FOR COMPARING C;.GME THEORY AND ARTIFICIAL INTELLIGENCE DECISION MAKING PROCESSES THESIS ’A Paul R. Andr...lic release: distribution unlii ted A F IT/;SO/ENS,/89D- 1 A MODEL FOR COMPARING GAME THEORY AND ARTIFICIAL INTELLIGENCE DECISION MAKING PROCESSES

  3. Artificial Intelligence: An Analysis of Potential Applications to Training, Performance Measurement, and Job Performance Aiding.

    DTIC Science & Technology

    1983-09-01

    AD-Ali33 592 ARTIFICIAL INTELLIGENCE: AN ANALYSIS OF POTENTIAL 1/1 APPLICATIONS TO TRAININ..(U) DENVER RESEARCH INST CO JRICHARDSON SEP 83 AFHRL-TP...83-28 b ’ 3 - 4. TITLE (aied Suhkie) 5. TYPE OF REPORT & PERIOD COVERED ARTIFICIAL INTEL11GENCE: AN ANALYSIS OF Interim POTENTIAL APPLICATIONS TO...8217 sde if neceseamy end ides*f by black naumber) artificial intelligence military research * computer-aided diagnosis performance tests computer

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  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. Artificial intelligence for breast cancer screening: Opportunity or hype?

    PubMed

    Houssami, Nehmat; Lee, Christoph I; Buist, Diana S M; Tao, Dacheng

    2017-09-19

    Interpretation of mammography for breast cancer (BC) screening can confer a mortality benefit through early BC detection, can miss a cancer that is present or fast growing, or can result in false-positives. Efforts to improve screening outcomes have mostly focused on intensifying imaging practices (double instead of single-reading, more frequent screens, or supplemental imaging) that may add substantial resource expenditures and harms associated with population screening. Less attention has been given to making mammography screening practice 'smarter' or more efficient. Artificial intelligence (AI) is capable of advanced learning using large complex datasets and has the potential to perform tasks such as image interpretation. With both highly-specific capabilities, and also possible un-intended (and poorly understood) consequences, this viewpoint considers the promise and current reality of AI in BC detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Artificial Intelligence Tools for Data Mining in Large Astronomical Databases

    NASA Astrophysics Data System (ADS)

    Longo, Giuseppe; Donalek, Ciro; Raiconi, Giancarlo; Staiano, Antonino; Tagliaferri, Roberto; Pasian, Fabio; Sessa, Salvatore; Smareglia, Riccardo; Volpicelli, Alfredo

    The federation of heterogeneous large astronomical databases foreseen in the framework of the AVO and NVO projects will pose unprecedented data mining and visualization problems which may find a rather natural and user friendly answer in artificial intelligence (A.I.) tools based on neural networks, fuzzy-C sets or genetic algorithms. We shortly describe some tools implemented by the AstroNeural collaboration (Napoli-Salerno) aimed to perform complex tasks such as, for instance, unsupervised and supervised clustering and time series analysis. Two very different applications to the analysis of photometric redshifts of galaxies in the Sloan Early Data Release and to the telemetry of the TNG (telescopio nazionale Galileo) are also discussed as template cases.

  12. 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.

  13. 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.

  14. 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.

  15. Application of Artificial Intelligence for Bridge Deterioration Model

    PubMed Central

    Chen, Zhang; Wu, Yangyang; 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

  16. 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.

  17. The use of artificial intelligence for safeguarding fuel reprocessing plants

    SciTech Connect

    Wachter, J.W.; Forgy, C.L.

    1987-01-01

    Recorded process data from the ''Minirun'' campaigns conducted at the Barnwell Nuclear Fuel Plant (BNFP) in Barnwell, South Carolina during 1980 to 1981 have been utilized to study the suitability of computer-based Artificial Intelligence (AI) methods for process monitoring for safeguards purposes. The techniques of knowledge engineering were used to formulate the decision-making software which operates on the process data customarily used for process operations. The OPS5 AI language was used to construct an Expert System for this purpose. Such systems are able to form reasoned conclusions from incomplete, inaccurate or otherwise ''fuzzy'' data, and to explain the reasoning that led to them. The programs were tested using minirun data taken during simulated diversions ranging in size from 1 to 20 L of solution that had been monitored previously using conventional procedural techniques. 13 refs., 3 figs.

  18. 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.

  19. 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.

  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 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.

  1. 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.

  2. 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.

  3. Reducing unnecessary lab testing in the ICU with artificial intelligence.

    PubMed

    Cismondi, F; Celi, L A; Fialho, A S; Vieira, S M; Reti, S R; Sousa, J M C; Finkelstein, S N

    2013-05-01

    To reduce unnecessary lab testing by predicting when a proposed future lab test is likely to contribute information gain and thereby influence clinical management in patients with gastrointestinal bleeding. Recent studies have demonstrated that frequent laboratory testing does not necessarily relate to better outcomes. Data preprocessing, feature selection, and classification were performed and an artificial intelligence tool, fuzzy modeling, was used to identify lab tests that do not contribute an information gain. There were 11 input variables in total. Ten of these were derived from bedside monitor trends heart rate, oxygen saturation, respiratory rate, temperature, blood pressure, and urine collections, as well as infusion products and transfusions. The final input variable was a previous value from one of the eight lab tests being predicted: calcium, PTT, hematocrit, fibrinogen, lactate, platelets, INR and hemoglobin. The outcome for each test was a binary framework defining whether a test result contributed information gain or not. Predictive modeling was applied to recognize unnecessary lab tests in a real world ICU database extract comprising 746 patients with gastrointestinal bleeding. Classification accuracy of necessary and unnecessary lab tests of greater than 80% was achieved for all eight lab tests. Sensitivity and specificity were satisfactory for all the outcomes. An average reduction of 50% of the lab tests was obtained. This is an improvement from previously reported similar studies with average performance 37% by [1-3]. Reducing frequent lab testing and the potential clinical and financial implications are an important issue in intensive care. In this work we present an artificial intelligence method to predict the benefit of proposed future laboratory tests. Using ICU data from 746 patients with gastrointestinal bleeding, and eleven measurements, we demonstrate high accuracy in predicting the likely information to be gained from proposed future

  4. 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.

  5. Eclipse Power -- Advances From Ancient Times to Artificial Intelligence

    NASA Astrophysics Data System (ADS)

    Guinan, E. F.; Engle, S. G.; Devinney, E. J.

    2007-05-01

    From ancient times to the present, eclipses and related occultations have been pivotal in the development of Astronomy and in the advancement of our understanding of the physical world. As discussed here, in modern astrophysics eclipsing binaries play major roles by returning a wealth of fundamental information and basic data about the physical properties of stars, as well as providing vital tests of stellar structure and evolution, accurate distances and so much more. Eclipsing binaries also serve as testbeds of various aspects of modern physics and astrophysics including General Relativity, nuclear and atomic physics and plasma physics. Eclipsing binaries in nearby galaxies are now even important in in cosmology by serving as first class ``standard candles'' that are leading to a significant improvement in the extragalactic distance scale. Also, eclipsing star-planet systems (ten discovered so far) are providing important properties of extrasolar planets (masses, radii and densities) that cannot be obtained by any other means. Moreover, from wide-field photometric surveys, the number of eclipsing binaries has greatly increased from a few thousand to over ten thousand known systems today. However, the pace of the discovery of new eclipsing systems is expected to explode during the next decade. Ground-based and orbiting wide-field programs that include Pan-STARRS, the Large Synoptic Survey Telescope (LSST), COROT, Kepler, Gaia and several others are expected to generate several million additional binary systems! To cope with analyzing and scientifically exploiting these overwhelming data, non-personal automatic and semi-autonomous approaches to light curve analysis are being developed. In particular, a new approach to this problem being developed by us and our colleagues is discussed that utilizes Artificial Intelligence (AI) / Neural Networks (NN) to find the best light curve solutions. This is part of a new program known as ``Eclipsing Binaries with Artificial

  6. AI (artificial intelligence) as the ultimate enhancer of protocol design

    SciTech Connect

    Cohen, D.; Finnegan, J.

    1987-04-24

    Most computer-communication protocols do not use the available resources very efficiently. For example, they pay expensive performance penalties in order to achieve reliable connections. This paper demonstrates how standard artificial-intelligence techniques applied to message transmission can result in vast improvements by making those mechanisms smart. Such an Expert System, coupled with various heuristics common in the communication domain, yields high-performance reliable connections for message interchange. This protocol is the first serious use of AI techniques to result in significant and substantial improvement of communication technology. The protocol is designed to maximize the well known CIQ - Communication Intelligence Quotient -- function. The CIQ is defined as the radio of the amount of information conveyed by a communication transaction (e.g., a message) and the amount of resources consumed in successfully communicating it. The new protocol is called HighQ, because it is designed to achieve a very high CIQ in message communication. The authors start by defining CIQ, the objective function that we wish to optimize. Then it is shown how conventional optimization techniques provide slight benefit the really large gains require sophisticated AI techniques and their use drastically minimizes the transmission cost, even eliminating it in some cases.

  7. Artificial Intelligence Methodologies and Their Application to Diabetes.

    PubMed

    Rigla, Mercedes; García-Sáez, Gema; Pons, Belén; Hernando, Maria Elena

    2017-05-01

    In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.

  8. 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.

  9. Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

    NASA Astrophysics Data System (ADS)

    Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

    2017-08-01

    In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

  10. [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.

  11. Comparison of Artificial Intelligence Techniques for river flow forecasting

    NASA Astrophysics Data System (ADS)

    Firat, M.

    2008-01-01

    The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting.

  12. 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

  13. Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma.

    PubMed

    Rajpara, S M; Botello, A P; Townend, J; Ormerod, A D

    2009-09-01

    Dermoscopy improves diagnostic accuracy of the unaided eye for melanoma, and digital dermoscopy with artificial intelligence or computer diagnosis has also been shown useful for the diagnosis of melanoma. At present there is no clear evidence regarding the diagnostic accuracy of dermoscopy compared with artificial intelligence. To evaluate the diagnostic accuracy of dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis and to compare the diagnostic accuracy of the different dermoscopic algorithms with each other and with digital dermoscopy/artificial intelligence for the detection of melanoma. A literature search on dermoscopy and digital dermoscopy/artificial intelligence for melanoma diagnosis was performed using several databases. Titles and abstracts of the retrieved articles were screened using a literature evaluation form. A quality assessment form was developed to assess the quality of the included studies. Heterogeneity among the studies was assessed. Pooled data were analysed using meta-analytical methods and comparisons between different algorithms were performed. Of 765 articles retrieved, 30 studies were eligible for meta-analysis. Pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%; P = 0.076). Pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%; P < 0.001). Pooled diagnostic odds ratio was 51.5 for dermoscopy and 57.8 for artificial intelligence, which were not significantly different (P = 0.783). There were no significance differences in diagnostic odds ratio among the different dermoscopic diagnostic algorithms. Dermoscopy and artificial intelligence performed equally well for diagnosis of melanocytic skin lesions. There was no significant difference in the diagnostic performance of various dermoscopy algorithms. The three-point checklist, the seven-point checklist and Menzies score had better diagnostic odds ratios than the

  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.

  15. 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.

  16. Natural and Artificial Intelligence in Neurosurgery: A Systematic Review.

    PubMed

    Senders, Joeky T; Arnaout, Omar; Karhade, Aditya V; Dasenbrock, Hormuzdiyar H; Gormley, William B; Broekman, Marike L; Smith, Timothy R

    2017-09-07

    Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence." A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P -value was provided or calculated, ML models outperformed clinical experts ( P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance ( P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models ( P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.

  17. 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

  18. The socio-organizational age of artificial intelligence in medicine.

    PubMed

    Stefanelli, M

    2001-08-01

    The increasing pressure on Health Care Organizations (HCOs) to ensure efficiency and cost-effectiveness, balancing quality of care and cost containment, will drive them towards a more effective management of medical knowledge derived from research findings. The relation between science and health services has until recently been too casual. The primary job of medical research has been to understand the mechanisms of disease and produce new treatments, not to worry about the effectiveness of the new treatments or their implementation. As a result many new treatments have taken years to become part of routine practice, ineffective treatments have been widely used, and medicine has been opinion rather than evidence based. This results in suboptimal care for patients. Knowledge management technology may provide effective approaches in speeding up the diffusion of innovative medical procedures whose clinical effectiveness have been proved: the most interesting one is represented by computer-based utilization of evidence-based clinical guidelines. As researchers in Artificial Intelligence in Medicine (AIM), we are committed to foster the strategic transition from opinion to evidence-based decision making. Reviews of the effectiveness of various methods of guideline dissemination show that the most predictable impact is achieved when the guideline is made accessible through computer-based and patient specific reminders that are integrated into the clinician's workflow. However, the traditional single doctor-patient relationship is being replaced by one in which the patient is managed by a team of health care professionals, each specializing in one aspect of care. Such shared care depends critically on the ability to share patient-specific information and medical knowledge easily among them. Strategically there is a need to take a more clinical process view of health care delivery and to identify the appropriate organizational and information infrastructures to support

  19. 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.

  20. 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)

  1. On the Need for Artificial Intelligence and Advanced Test and Evaluation Methods for Space Exploration

    NASA Astrophysics Data System (ADS)

    Scheidt, D. H.; Hibbitts, C. A.; Chen, M. H.; Paxton, L. J.; Bekker, D. L.

    2017-02-01

    Implementing mature artificial intelligence would create the ability to significantly increase the science return from a mission, while potentially saving costs in mission and instrument operations, and solving currently intractable problems.

  2. 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)

  3. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

    DTIC Science & Technology

    1987-10-01

    This was an instrumentation grant to purchase equipment of support of research in neural networks, information science , artificial intelligence, and applied mathematics. Computer lab equipment, motor control and robotics lab equipment, speech analysis equipment and computational vision equipment were purchased.

  4. On Some Supposed Contributions of Artificial Intelligence to the Scientific Study of Language

    ERIC Educational Resources Information Center

    Dresher, B. Elan; Hornstein, Norbert

    1976-01-01

    While computers have a definite role to play, current language research involving artificial intelligence is headed in a wrong direction and is unlikely to contribute to a scientific theory of language. (Author/MV)

  5. Nuclear fuel pellet quality control using artificial intelligence techniques

    NASA Astrophysics Data System (ADS)

    Song, Xiaolong

    Inspection of nuclear fuel pellets is a complex and time-consuming process. At present, quality control in the fuel fabrication field mainly relies on human manual inspection, which is essentially a judgement call. Considering the high quality requirement of fuel pellets in the nuclear industry, pellet inspection systems must have a high accuracy rate in addition to a high inspection speed. Furthermore, any inspection process should have a low rejection rate of good pellets from the manufacturer point of view. It is very difficult to use traditional techniques, such as simple image comparison, to adequately perform the inspection process of the nuclear fuel pellet. Knowledge-based inspection and a defect-recognition algorithm, which maps the human inspection knowledge, is more robust and effective. A novel method is introduced here for pellet image processing. Three artificial intelligence techniques are studied and applied for fuel pellet inspection in this research. They are an artificial neural network, fuzzy logic, and the decision tree method. A dynamic reference model is located on each input fuel pellet image. Then, those pixels that belong to the abnormal defect are enhanced with high speed and high accuracy. Next, the content-based features for the defect are extracted from those abno1mal pixels and used in the inspection algorithm. Finally, an automated inspection prototype system---Visual Inspection Studio---which combines machine vision and these three AI techniques, is developed and tested. The experimental results indicate a very successful system with a high potential for on-line automatic inspection process.

  6. Computer-aided diagnosis and artificial intelligence in clinical imaging.

    PubMed

    Shiraishi, Junji; Li, Qiang; Appelbaum, Daniel; Doi, Kunio

    2011-11-01

    Computer-aided diagnosis (CAD) is rapidly entering the radiology mainstream. It has already become a part of the routine clinical work for the detection of breast cancer with mammograms. The computer output is used as a "second opinion" in assisting radiologists' image interpretations. The computer algorithm generally consists of several steps that may include image processing, image feature analysis, and data classification via the use of tools such as artificial neural networks (ANN). In this article, we will explore these and other current processes that have come to be referred to as "artificial intelligence." One element of CAD, temporal subtraction, has been applied for enhancing interval changes and for suppressing unchanged structures (eg, normal structures) between 2 successive radiologic images. To reduce misregistration artifacts on the temporal subtraction images, a nonlinear image warping technique for matching the previous image to the current one has been developed. Development of the temporal subtraction method originated with chest radiographs, with the method subsequently being applied to chest computed tomography (CT) and nuclear medicine bone scans. The usefulness of the temporal subtraction method for bone scans was demonstrated by an observer study in which reading times and diagnostic accuracy improved significantly. An additional prospective clinical study verified that the temporal subtraction image could be used as a "second opinion" by radiologists with negligible detrimental effects. ANN was first used in 1990 for computerized differential diagnosis of interstitial lung diseases in CAD. Since then, ANN has been widely used in CAD schemes for the detection and diagnosis of various diseases in different imaging modalities, including the differential diagnosis of lung nodules and interstitial lung diseases in chest radiography, CT, and position emission tomography/CT. It is likely that CAD will be integrated into picture archiving and

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Color regeneration from reflective color sensor using an artificial intelligent technique.

    PubMed

    Saracoglu, Ömer Galip; Altural, Hayriye

    2010-01-01

    A low-cost optical sensor based on reflective color sensing is presented. Artificial neural network models are used to improve the color regeneration from the sensor signals. Analog voltages of the sensor are successfully converted to RGB colors. The artificial intelligent models presented in this work enable color regeneration from analog outputs of the color sensor. Besides, inverse modeling supported by an intelligent technique enables the sensor probe for use of a colorimetric sensor that relates color changes to analog voltages.

  12. 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.

  13. The Application of Neural Networks with Artificial Intelligence Technique in the Modeling of Industrial Processes

    NASA Astrophysics Data System (ADS)

    Saini, K. K.; Saini, Sanju

    2008-10-01

    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.

  14. Artificial Intelligence in Precision Cardiovascular Medicine.

    PubMed

    Krittanawong, Chayakrit; Zhang, HongJu; Wang, Zhen; Aydar, Mehmet; Kitai, Takeshi

    2017-05-30

    Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  15. The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading.

    PubMed

    Hainc, Nicolin; Federau, Christian; Stieltjes, Bram; Blatow, Maria; Bink, Andrea; Stippich, Christoph

    2017-01-01

    Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine.

  16. Clinical sequencing in leukemia with the assistance of artificial intelligence.

    PubMed

    Tojo, Arinobu

    2017-01-01

    Next generation sequencing (NGS) of cancer genomes is now becoming a prerequisite for accurate diagnosis and proper treatment in clinical oncology. Because the genomic regions for NGS expand from a certain set of genes to the whole exome or whole genome, the resulting sequence data becomes incredibly enormous and makes it quite laborious to translate the genomic data into medicine, so-called annotation and curation. We organized a clinical sequencing team and established a bidirectional (bed-to-bench and bench-to-bed) system to integrate clinical and genomic data for hematological malignancies. We also started a collaborative research project with IBM Japan to adopt the artificial intelligence Watson for Genomics (WfG) to the pipeline of medical informatics. Genomic DNA was prepared from malignant as well as normal tissues in each patient and subjected to NGS. Sequence data was analyzed using an in-house semi-automated pipeline in combination with WfG, which was used to identify candidate driver mutations and relevant pathways from which applicable drug information was deduced. Currently, we have analyzed more than 150 patients with hematological disorders, including AML and ALL, and obtained many informative findings. In this presentation, I will introduce some of the achievements we have made so far.

  17. 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.

  18. 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.

  19. 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.

  20. Artificial intelligence approach for spot application project system design

    NASA Astrophysics Data System (ADS)

    Lefevre, M. J.; Fisse, G.; Martin, E.; de Boissezon, H.; Galaup, M.

    1993-11-01

    Over the past four years, CNES has been engaged in a major programme focusing on the development of SPOT Operational Application Projects. With a total of sixty projects now complete, we can draw a number of meaningful conclusions and identify a number of objectives to be satisfied by advanced remote sensing methodology. One of the main conclusions points to the importance of human vision in studies on natural complex space imagery. This being so, visual recognition must be one of the main phases of the ``Pilot Project for the Application of Remote Sensing to Agricultural Statistics'': only human experts have the ability to make a meaningful analysis of Spot TM imagery. Non-expert operators will not be able to manage the subsequent rational production phase alone. The first part of this paper describes an approach to the formalization and modelling of expert know-how based on the use of artificial intelligence. The second part puts forward a cooperative operator/computer system based on a cognitive structure. Our proposal comprises 1) a specific knowledge base, 2) an ergonomic interface associated with functional software that is based on automatic image enhancement coupled with perception support functions.

  1. An artificial intelligence system for computer-assisted menu planning.

    PubMed

    Petot, G J; Marling, C; Sterling, L

    1998-09-01

    Planning nutritious and appetizing menus is a complex task that researchers have tried to computerize since the early 1960s. We have attempted to facilitate computer-assisted menu planning by modeling the reasoning an expert dietitian uses to plan menus. Two independent expert systems were built, each designed to plan a daily menu meeting the nutrition needs and personal preferences of an individual client. One system modeled rule-based, or logical, reasoning, whereas the other modeled case-based, or experiential, reasoning. The 2 systems were evaluated and their strengths and weaknesses identified. A hybrid system was built, combining the best of both systems. The hybrid system represents an important step forward because it plans daily menus in accordance with a person's needs and preferences; the Reference Daily Intakes; the Dietary Guidelines for Americans; and accepted aesthetic standards for color, texture, temperature, taste, and variety. Additional work to expand the system's scope and to enhance the user interface will be needed to make it a practical tool. Our system framework could be applied to special-purpose menu planning for patients in medical settings or adapted for institutional use. We conclude that an artificial intelligence approach has practical use for computer-assisted menu planning.

  2. Artificial intelligence and its impact on combat aircraft

    NASA Technical Reports Server (NTRS)

    Ott, Lawrence M.; Abbot, Kathy; Kleider, Alfred; Moon, D.; Retelle, John

    1987-01-01

    As the threat becomes more sophisticated and weapon systems more complex to meet the threat, the need for machines to assist the pilot in the assessment of information becomes paramount. This is particularly true in real-time, high stress situations. The advent of artificial intelligence (AI) technology offers the opportunity to make quantum advances in the application of machine technology. However, if AI systems are to find their way into combat aircraft, they must meet certain criteria. The systems must be responsive, reliable, easy to use, flexible, and understandable. These criteria are compared with the current status used in a combat airborne application. Current AI systems deal with nonreal time applications and require significant user interaction. On the other hand, aircraft applications require real time, minimum human interaction systems. In order to fill the gap between where technology is now and where it must be for aircraft applications, considerable government research is ongoing in NASA, DARPA, and three services. The ongoing research is briefly summarized. Finally, recognizing that AI technology is in its embryonic stage, and the aircraft needs are very demanding, a number of issues arise. These issues are delineated and findings are provided where appropriate.

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. A grounded theory of abstraction in artificial intelligence.

    PubMed

    Zucker, Jean-Daniel

    2003-07-29

    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.

  9. 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. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. 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.

  11. (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.

  12. 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.

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    1993-03-01

    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 "don't care" perceptron network; (5) the "don't care" 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 "don't care" 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 (≥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.

  15. 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.

  16. On prognostic models, artificial intelligence and censored observations.

    PubMed

    Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

    2001-03-01

    The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

  17. 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.

  18. Can Artificial Intelligences Suffer from Mental Illness? A Philosophical Matter to Consider.

    PubMed

    Ashrafian, Hutan

    2017-04-01

    The potential for artificial intelligences and robotics in achieving the capacity of consciousness, sentience and rationality offers the prospect that these agents have minds. If so, then there may be a potential for these minds to become dysfunctional, or for artificial intelligences and robots to suffer from mental illness. The existence of artificially intelligent psychopathology can be interpreted through the philosophical perspectives of mental illness. This offers new insights into what it means to have either robot or human mental disorders, but may also offer a platform on which to examine the mechanisms of biological or artificially intelligent psychiatric disease. The possibility of mental illnesses occurring in artificially intelligent individuals necessitates the consideration that at some level, they may have achieved a mental capability of consciousness, sentience and rationality such that they can subsequently become dysfunctional. The deeper philosophical understanding of these conditions in mankind and artificial intelligences might therefore offer reciprocal insights into mental health and mechanisms that may lead to the prevention of mental dysfunction.

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. [The application and development of artificial intelligence in medical diagnosis systems].

    PubMed

    Chen, Zhencheng; Jiang, Yong; Xu, Mingyu; Wang, Hongyan; Jiang, Dazong

    2002-09-01

    This paper has reviewed the development of artificial intelligence in medical practice and medical diagnostic expert systems, and has summarized the application of artificial neural network. It explains that a source of difficulty in medical diagnostic system is the co-existence of multiple diseases--the potentially inter-related diseases. However, the difficulty of image expert systems is inherent in high-level vision. And it increases the complexity of expert system in medical image. At last, the prospect for the development of artificial intelligence in medical image expert systems is made.

  4. 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

  5. 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

  6. 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…

  7. 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…

  8. 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…

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. A new and cheap medical examination system with artificial intelligence.

    PubMed

    Zhang, X; Cai, F; Liu, F; Bao, X; Liu, Y

    1995-01-01

    This is a medical examination system with artificial intelligence. It is used to find persons who want to obtain higher professional posts in the medical field, including doctors, dentists, nurses, pharmaceutists, etc. It has a question bank of 536,000 multiple choice questions (MCQs), of which 59,500 have pictures. Ten percent of the MCQs is updated every year; the old MCQs in the question bank are used as exercises for the persons who want to obtain higher professional posts. The system, with three distinctive degrees of difficulty, is applicable to three different professional posts: the assistant physician, the resident, and the attending physician. It ensures the general level of assessment for the same professional post, regardless of when one wants to take the system. In addition, the system also guarantees the consistency of the following three factors: the constituent of sub-professions, the classification of MCQs, and the degree of difficulty of each MCQ. Another feature of the system is that the passing score need not be the traditional standard passing score of 60. The passing score is determined by the system according to the requirements of the different professional posts and the degree of difficulty of the MCQs in the system. Therefore, the new method for passing the system is more rational than the traditional one. Moreover, the final score given by the system will be standardized to eliminate sampling errors. The reliability of the system will also be tested after each examination. If the coefficient of reliability is lower than 0.85, the scores will be canceled and examination will be repeated. The system can accept 96 persons simultaneously taking the examination with on-line computers. The people who work in remote places can take examination in their hospital with a personal computer linked via telephone. The system has already been approved by the relevant authoritative departments in China. It has been running successfully for more than six

  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 Intelligence and Its Use in Cost Type analyses with an Example in Cost Performance Measurement.

    DTIC Science & Technology

    1985-01-01

    7-Ai6i 817 ARTIFICIAL INTELLIGENCE AND ITS USE IN COST TYE1/I ANALYSES WdITH ANt EXAMPLE IN COST PERFORMANCE I MERSUREMENT(U) DEFENSE SYSTEMS...INTELLIGENCE-THE EMERGING TECHNOLOGY/ NATURAL LANGUAGE PROCESSORS K ~ With the advent of ARTIFICAL INTELLEGENCE (AI), we are entering into a new era of...language processor which is commerically available is INTELLECT, by Artifical Intellegence Incorporated, Waltham, Mass. To illustrate what a natural

  17. An overview of the artificial intelligence and expert systems component of RICIS

    NASA Technical Reports Server (NTRS)

    Feagin, Terry

    1987-01-01

    Artificial Intelligence and Expert Systems are the important component of RICIS (Research Institute and Information Systems) research program. For space applications, a number of problem areas that should be able to make good use of the above tools include: resource allocation and management, control and monitoring, environmental control and life support, power distribution, communications scheduling, orbit and attitude maintenance, redundancy management, intelligent man-machine interfaces and fault detection, isolation and recovery.

  18. 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.

  19. 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.

  20. An Artificial Intelligence Technique to Generate Self-Optimizing Experimental Designs.

    DTIC Science & Technology

    1983-02-01

    pattern or a binary chopping technique in the space of decision variables while carrying out a sequence of contiroLled experiments on the strategy ...7 AD-A127 764 AN ARTIFICIAL INTELLIGENCE TECHNIQUE TO GENERATE 1/1 SELF-OPTIMIZING EXPERIME. .(U) ARIZONA STATE UNIV TEMPE GROUP FOR COMPUTER STUDIES...6 3 A - - II 1* Ii.LI~1 11. AI-. jMR.TR- 3 0 3 37 AN ARTIFICIAL INTELLIGENCE TECHNIQUE TO GENERATE SELF-OPTIMIZING EXPERIMENTAL DESIGNS Nicholas V