Modeling plan recognition for decision support

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 867)


Plan recognition consists of building an interpretation of an observed behavior in terms of the plans and goals that can be attributed to an agent. It can be thus considered as a form of understanding. Plan recognition is often compared with planning—they are considered as opposite processes according to two criteria: the mechanism employed to reach the solution—selection or construction — and the knowledge involved in both forms of plan reasoning. On these grounds, plan recognition has often been considered as an ill-defined “understanding task”, different from problem solving. Also, while knowledge-level models of planning can be found in the literature, the main modeling approaches have made no attempt to rationalize plan recognition. In this paper, plan recognition is analyzed as problem solving behavior, and an interpretation model for this task is proposed. The situation considered is one of keyhole recognition from low-level data in a dynamic environment, with the aim of providing decision support in a critical domain.


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  1. 1.
    Allen, J.F., Kautz, H.A., Pelavin, R.N., and Tenenberg, J.D.: Reasoning about Plans. Morgan Kaufmann, 1991.Google Scholar
  2. 2.
    Barès, M.: Aide à la décision dans les systèmes informatisés de commandement: rôle de l'information symbolique. DRET Report, July 1989, Paris.Google Scholar
  3. 3.
    Barès, M., Cañamero, D., Delannoy, J.-F., and Kodratoff, Y.: XPlans: Case-Based Reasoning for Plan Recognition. Applied Artificial Intelligence 8, Vol. 3, (1994) (forthcoming).Google Scholar
  4. 4.
    Breuker, J. (ed), Wielinga, B., van Someren, M., de Hoog, R., Schreiber, G., de Greef, P., Bredeweg, B., Wielemaker, J., and Billaut, J.-P.: Model Driven Knowledge Acquisition: Interpretation Models. Deliverable A1, ESPRIT Project 1098, Memo 87, SWI, University of Amsterdam, 1987.Google Scholar
  5. 5.
    Canamero, D., Delannoy, J.-F., and Kodratoff, Y.: Building Explanations in a Plan Recogñition System for Decision Support. Proceedings of the ECAI Workshop on Improving the Use of Knowledge-Based Systems with Explanations, Vienna, Austria, August 1992; Rapport LAFORIA 92/21, Université Paris VII, France, 35–45.Google Scholar
  6. 6.
    Carberry, S. and Pope, A.: Plan Recognition Strategies for Natural Language Understanding. Int. J. Man-Machine Studies 39 (1993) 529–577.Google Scholar
  7. 7.
    Clancey, W.J.: Heuristic Classification. Artificial Intelligence 27 (1985) 289–350.Google Scholar
  8. 8.
    Cohen, P.R., Perrault, C.R., and Allen, J.F.: Beyond question answering. In W. Lenhert and M. Ringle (eds.): Strategies for Natural Language Understanding. Lawrence Erlbaum, 1981, 245–274.Google Scholar
  9. 9.
    Delannoy, J.-F., Cañamero, D., and Kodratoff, Y.: Causal Interpretation from Events in a Robust Plan Recognition System for Decision Support. Proceedings of the Fifth International Symposium on Knowledge Engineering, Sevilla, Spain, October 1992, 179–189.Google Scholar
  10. 10.
    Hayes-Roth, F., Waterman, D., and Lenat, D. (eds.): Building Expert Systems. Addison-Wesley, 1983.Google Scholar
  11. 11.
    Hoc, J.-M: Psychologie cognitive de la planification. Presses Universitaires de Grenoble, France, 1987.Google Scholar
  12. 12.
    Mooney R.J.: Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition. Cognitive Science 14 (1990) 483–509.Google Scholar
  13. 13.
    Newell, A: The Knowledge Level. Artificial Intelligence 18 (1982) 87–127.Google Scholar
  14. 14.
    Pennington, N. and Hastie, R.: Reasoning in Explanation-Based Decision Making. Cognition 40 (1993) 123–163.Google Scholar
  15. 15.
    Richard, J.-F.: Les activités mentales: Comprendre, raisonner, trouver des solutions. Armand Colin, Paris, 1990.Google Scholar
  16. 16.
    Schank, R.C.: Explanation Patterns: Understanding Mechanically and Creatively. Lawrence Erlbaum Associates, 1986.Google Scholar
  17. 17.
    Steels, L: Components of Expertise. AI Magazine 11 (1990, Summer) 28–49.Google Scholar
  18. 18.
    Steels, L: Reusability and Knowledge Sharing. In L. Steels and B. Lepape (eds.): Enhancing the Knowledge Engineering Process—Contributions from ESPRIT. Amsterdam, North-Holland, 1992, 240–268.Google Scholar
  19. 19.
    van Beek, P. and Cohen, R: Resolving plan ambiguity for cooperative response generation. Proceedings of the 10th AAAI, 1991, 938–944.Google Scholar
  20. 20.
    van de Velde, W.: Inference Structure as a Basis for Problem Solving. Proceedings of the 8th European Conference on Artificial Intelligence (ECAI-88). London, Pitman, 1988, 196–207.Google Scholar
  21. 21.
    van de Velde, W.: Issues in Knowledge-Level Modeling. In J.-M. David, J.-P. Krivine, and R. Simmons (eds.): Second Generation Expert Systems. Springer-Verlag, 1993, 211–231.Google Scholar
  22. 22.
    Vilain, M.: Getting serious about parsing plans: A grammatical analysis of plan recognition. Proceedings of the 9th AAAI, Boston, MA, 1990, 190–197.Google Scholar
  23. 23.
    von Martial, F.: Coordinating Plans of Autonomous Agents. Springer-Verlag, LNAI 610, 1992.Google Scholar
  24. 24.
    Wielinga, B.J., Schreiber, A.Th., and Breuker, J.A.: KADS: A Modeling Approach to Knowledge Engineering. Knowledge Acquisition 4 (1992) 5–53.Google Scholar
  25. 25.
    Wilensky, R.: Meta-Planning: Representing and Using Knowledge About Planning in Problem Solving and Natural Language Understanding. Cognitive Science 5 (1981) 197–233.Google Scholar
  26. 26.
    Wilensky, R.: Planning and Understanding: A Computational Approach to Human Reasoning. Addison-Wesley, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  1. 1.CNRS-LRIUniversité Paris-SudOrsay CedexFrance

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