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RESEARCH ARTICLE   Open Access    

Comparing two context-driven approaches for representation of human tactical behavior

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  • Abstract: This paper describes an investigation that compared Context-based Reasoning (CxBR) and Contextual Graphs (CxG), two well-known context-driven approaches used to represent human intelligence and decision-making. The specific objective of this investigation was to compare and contrast both approaches to increase the readers’ understanding of each approach. We also identify which, if any, excels in a particular area, and to look for potential synergism between them. This comparison is presented according to 10 different criteria, with some indication of which one excels at each particular facet of performance. We focus the comparison on how each would represent human tactical behavior, either in a simulation or in the real world. Conceptually, these two context-driven approaches are not at the same representational level. This could provide an opportunity in the future to combine them synergistically.
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  • Aihe D. and Gonzalez A. J.2004 Context-driven Reinforcement Learning. In Proceedings of the Second Swedish-American Workshop on Modeling and Simulation, Cocoa Beach, FL, 2–3 February.

    Google Scholar

    Anderson J. R., Matessa M. and Lebiere C.1997ACT-R: a theory of higher level cognition and its relation to visual attention. Human Computer Interaction12(4), 439–462.

    Google Scholar

    Barrett G. C. and Gonzalez A. J.2004 Expanding knowledge representation within context based reasoning to facilitate modeling collaborative behaviors. In Proceedings of the European Simulation Interoperability Workshop, Euro-SIW, Edinburgh, Scotland.

    Google Scholar

    Brézillon P.1999Context in problem solving: A survey. The Knowledge Engineering Review14(1), 1–34.

    Google Scholar

    Brézillon P.2003Representation of procedures and practices in contextual graphs. The Knowledge Engineering Review18(2),147–174.

    Google Scholar

    Brézillon P.2005 Task-realization models in Contextual Graphs. Dey A., Kokinov B., Leake D., Turner R.(eds.) Modeling and Using Context (CONTEXT-05), Springer Verlag, LNCS 3554, pp. 55–68

    Google Scholar

    Brézillon P., Brézillon J. and Pomerol J.-Ch. 2006 Decision making at a crossroad: a negotiation of contexts. In Proceedings of the Joint International Conference on Computing and Decision Making in Civil and Building Engineering, pp. 2574–2583.

    Google Scholar

    Brown J.1994 Application and evaluation of the context-based reasoning paradigm. Master’s Thesis, Department of Electrical and Computer Engineering, University of Central Florida, July.

    Google Scholar

    Fernlund H.2004 Evolving models from observed human performance. Doctoral dissertation, Department of Electrical and Computer Engineering, University of Central Florida, Spring.

    Google Scholar

    Fox M. S., Kleinosky P. and Lowenfeld S.1983 Techniques for sensor-based diagnosis. In Proceedings on the Eighth International Joint Conference on Artificial Intelligence, Karlsruhe, Germany.

    Google Scholar

    Gonzalez A. J.2004 Presentation to faculty at Air Force Institute of Technology. December, Wright-Patterson Air Force Base.

    Google Scholar

    Gonzalez A. J. and Ahlers R. H.1993 Concise representation of autonomous intelligent platforms in a simulation through the use of scripts. In Proceedings of the Sixth Annual Florida Artificial Intelligence Research Symposium, Ft. Lauderdale, FL, April.

    Google Scholar

    Gonzalez A. J. and Ahlers R.1998Context-based representation of intelligent behavior in training simulations. Transactions of the Society of Computer Simulation15(4), 153–166.

    Google Scholar

    Gonzalez F. G., Grejs P. and Gonzalez A. J.2000 Autonomous automobile behavior through context-based reasoning. In Proceedings of the International FLAIRS Conference, Orlando, FL, May.

    Google Scholar

    Gonzalez A. J., Castro J. and Gerber W. E.2006Automating the acquisition of tactical knowledge for military missions. Journal of Defense Modeling and Simulation3(1), 145–160.

    Google Scholar

    Guha R. V.1991 Contexts: a formalization and some applications. MCC Technical Report ACT-CYC-423-91 December.

    Google Scholar

    Gumus I.1998 A threat prioritization algorithm for multiple intelligent entities in a simulated environment. Master’s Thesis, Department of Electrical and Computer Engineering, University of Central Florida, Summer.

    Google Scholar

    Henninger A. E.2001 The use of neural network based movement models to improve the predictive utility of entity state synchronization methods for distributed simulations. Doctoral Dissertation, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL, Spring.

    Google Scholar

    Henninger A. E. and Gonzalez A. J.1997 Automated acquisition tool for tactical knowledge. In Proceedings of the 10th Annual Florida Artificial Intelligence Research Symposium, May, pp. 307–311.

    Google Scholar

    Kokinov B. and Yoveva M.1996 Context effects on problem solving. In Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.

    Google Scholar

    Laird J. E., Newell A. and Rosenbloom P. S.1987Soar: an architecture for general intelligence. Artificial Intelligence33(1), 1–64.

    Google Scholar

    McCarthy J.1993 Notes on formalizing context. In Proceedings of the 13th IJCAI, 1, pp. 555–560.

    Google Scholar

    McDermott J.1982R1: a rule-based configurer of computer systems. Artificial Intelligence19(1), 39–88.

    Google Scholar

    Norlander L.1998 A framework for efficient implementation of context-based reasoning in intelligent simulations. Master’s Thesis, Department of Electrical and Computer Engineering, University of Central Florida, 1998.

    Google Scholar

    Pasquier L.2000 Raisonnements basés sur le contexte: Contextes procéduralisés, graphes contextuels et schèmes d'action. Research Report LIP6 N.2000-010, Université de Paris 6, France.

    Google Scholar

    Pasquier L., Brézillon P. and Pomerol J.-Ch2000 From representation of operational knowledge to practical decision making in operations. In Carlsson S., Brezillon P., Humphreys P., Lundberg B. G., McCosh A. and Rajkovic V. (eds.). Decision Support through Knowledge Management. Akademitryck AB, Edsbruk, Sweden, pp. 301–320.

    Google Scholar

    Proenza R.1997 A framework for multiple agents and memory recall within the context-based reasoning paradigm. Master’s Thesis, Department of Electrical and Computer Engineering, University of Central Florida, Spring.

    Google Scholar

    Sherwell B. W., Gonzalez A. J. and Nguyen J.2005 Contextual implementation of human problem-solving knowledge in a real-world decision support system. In Proceedings of the Conference on Behavior Representation in Modeling and Simulation, Los Angeles, CA, May.

    Google Scholar

    Sidani T. A. and Gonzalez A. J.2000A framework for learning implicit expert knowledge through observation. Transactions of the Society for Computer Simulation17(2), 54–72.

    Google Scholar

    Sowa J. F.1984Conceptual structures information processing in mind and machine. Reading, MA: Addison-Wesley Publishing Company.

    Google Scholar

    Sowa J. F.2000Knowledge Representation: Logical, Philosophical, and Computational Foundations. Pacific Grove, CA: Brooks Cole Publishing Co.

    Google Scholar

    Stensrud B. S.2005 FAMTILE: an algorithm for learning high-level tactical behavior from observation. Doctoral Dissertation, Department of Electrical and Computer Engineering, University of Central Florida, May.

    Google Scholar

    Thorndike P. W. and Wescourt K. T.1984 Modeling time-stressed situation assessment and planning for intelligent opponent simulation. Final Technical Report PPAFTR-1124-84-1, sponsored by the Office of Naval Research, July.

    Google Scholar

    Turner R. M.1998 Context-mediated behavior for AI applications. In Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE-98, Vol. 1, 1–4 June, Castell, Spain, pp. 538–545.

    Google Scholar

  • Cite this article

    AVELINO J. GONZALEZ, PATRICK BRÉZILLON. 2008. Comparing two context-driven approaches for representation of human tactical behavior. The Knowledge Engineering Review. 23:1355 doi: 10.1017/S0269888908001355
    AVELINO J. GONZALEZ, PATRICK BRÉZILLON. 2008. Comparing two context-driven approaches for representation of human tactical behavior. The Knowledge Engineering Review. 23:1355 doi: 10.1017/S0269888908001355

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RESEARCH ARTICLE   Open Access    

Comparing two context-driven approaches for representation of human tactical behavior

The Knowledge Engineering Review  23 Article number: 10.1017/S0269888908001355  (2008)  |  Cite this article

Abstract: Abstract: This paper describes an investigation that compared Context-based Reasoning (CxBR) and Contextual Graphs (CxG), two well-known context-driven approaches used to represent human intelligence and decision-making. The specific objective of this investigation was to compare and contrast both approaches to increase the readers’ understanding of each approach. We also identify which, if any, excels in a particular area, and to look for potential synergism between them. This comparison is presented according to 10 different criteria, with some indication of which one excels at each particular facet of performance. We focus the comparison on how each would represent human tactical behavior, either in a simulation or in the real world. Conceptually, these two context-driven approaches are not at the same representational level. This could provide an opportunity in the future to combine them synergistically.

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    Cite this article
    AVELINO J. GONZALEZ, PATRICK BRÉZILLON. 2008. Comparing two context-driven approaches for representation of human tactical behavior. The Knowledge Engineering Review. 23:1355 doi: 10.1017/S0269888908001355
    AVELINO J. GONZALEZ, PATRICK BRÉZILLON. 2008. Comparing two context-driven approaches for representation of human tactical behavior. The Knowledge Engineering Review. 23:1355 doi: 10.1017/S0269888908001355
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