Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-26T20:57:01.786Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  01 September 2008

AVELINO J. GONZALEZ  and
PATRICK BRÉZILLON
AVELINO J. GONZALEZ
Affiliation:
School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL, USA; email: gonzalez@ucf.edu
PATRICK BRÉZILLON
Affiliation:
Laboratoire d’Informatique de Paris 6, Universite Pierre & Marie Curie, Paris, France; email: Patrick.Brezillon@lip6.fr
Get access Rights & Permissions [Opens in a new window]

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.

Type
Articles
Information
The Knowledge Engineering Review , Volume 23 , Issue 3 , September 2008 , pp. 295 - 315
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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. 1997 ACT-R: a theory of higher level cognition and its relation to visual attention. Human Computer Interaction 12(4), 439–462.CrossRefGoogle 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. 1999 Context in problem solving: A survey. The Knowledge Engineering Review 14(1), 1–34.CrossRefGoogle Scholar
Brézillon, P. 2003 Representation of procedures and practices in contextual graphs. The Knowledge Engineering Review 18(2),147–174.CrossRefGoogle 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. 55–68Google 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. 1998 Context-based representation of intelligent behavior in training simulations. Transactions of the Society of Computer Simulation 15(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. 2006 Automating the acquisition of tactical knowledge for military missions. Journal of Defense Modeling and Simulation 3(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. 1987 Soar: an architecture for general intelligence. Artificial Intelligence 33(1), 1–64.CrossRefGoogle Scholar
McCarthy, J. 1993 Notes on formalizing context. In Proceedings of the 13th IJCAI, 1, pp. 555–560.Google Scholar
McDermott, J. 1982 R1: a rule-based configurer of computer systems. Artificial Intelligence 19(1), 39–88.CrossRefGoogle 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.-Ch 2000 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. 2000 A framework for learning implicit expert knowledge through observation. Transactions of the Society for Computer Simulation 17(2), 54–72.Google Scholar
Sowa, J. F. 1984 Conceptual structures information processing in mind and machine. Reading, MA: Addison-Wesley Publishing Company.Google Scholar
Sowa, J. F. 2000 Knowledge 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