Advertisement

A Planning under Uncertainty Model

  • Enrique Paniagua-Arís
  • José T. Palma-Méndez
  • Fernando Martín-Rubio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2178)

Abstract

In classical planning, actions are assumed to be deterministic, the initial state is known and the goal is defined by a set of state facts, and the solution consists of a sequence of actions that leads the system from the initial state to the goal state. However, most practical problems, especially in non observable and uncertain contexts, do not satisfy these requirements of complete and deterministic information. The main goal of this work is to develop a generic planning under uncertainty model at the knowledge level enabling plan viability evaluation so that the most possible, effective, and complete plan can be determined. The proposed model in this work is presented at different levels of analysis: meta ontological, ontological, epistemological and logical levels, and applied to the post and ex ante approaches. The planning task is composed of a set of planning subtasks: plan generation, plan prevention, plan support, plan correction, and plan replacement.

Keywords

State Fact Uncertainty Model Knowledge Level Planning Task Plan Replacement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Da Costa, C., Garcia, F., Lang, J., and Martin-Clouaire, R.: Planning with graded nondeterministic actions: A possibilistic approach. International Journal of Intelligent Systems. Special Issue on “Fuzzy Information Engineering”, vol 12 (12) (december 1997)Google Scholar
  2. 2.
    McCarthy, J.: Epistemological problems of AI. Morgan Kaufmann, IJCAI 5, 46–52. Cambridge (1977)Google Scholar
  3. 3.
    Ginsberg, M. L., Smith, D.E.: Reasoning About Action II: The Qualification Problem. CS Standford University, Standford (1988)Google Scholar
  4. 4.
    McCarthy, J.: Some philosophical problems from the standpoint of AI. Machine Intelligence 4. 26–45. New York (1969)Google Scholar
  5. 5.
    Kushmerick, N., Hanks, S. & Weld, D.: An algorithm for probabilistic planning. J. Artificial Intelligence, 76(1–2): 239–86 (1995)Google Scholar
  6. 6.
    Thiébaux, S., Hertzberg, J., Shoaff, W., Schneider, M.: A Stochastic Model of Actions and Plans for Anytime Planning under Uncertainty. TR-93-027, Melbourne (1993)Google Scholar
  7. 7.
    Newell, A.: The Knowledge Level. Artificial Intelligence, 18(1): 82–127 (1982)CrossRefGoogle Scholar
  8. 8.
    Breuker, J., Van de Velde, W.: CommonKADS Library for Expertise Modelling. IOS Press (1994)Google Scholar
  9. 9.
    Fikes, R., Nilsson, N.: STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2 (3/4): 189–208 (1971)zbMATHCrossRefGoogle Scholar
  10. 10.
    McAllester, D., Rosenblitt, D.: Systematic Nonlinear Planning. AAAI-91 (1991)Google Scholar
  11. 11.
    Peot, M. A. and Smith, D. E.: Conditional nonlinear planning. In Proceedings of the First International Conference on Artificial Intelligence Planning Systems, pp. 189–197 College Park, Maryland. Morgan Kaufmann (1992)Google Scholar
  12. 12.
    Pryor, L., Collins, G.: Planning for Contingencies: A Decision-based Approach. Journal of Artificial Intelligence Research 4. 287–339 (1996)Google Scholar
  13. 13.
    Onder, N., and Pollack, M. E.: Contingency Selection in Plan Generation. Proceedings of the 4th European Conference on Planning, Toulouse, France, September (1997)Google Scholar
  14. 14.
    Onder, N., Pollack, M. E., and Horty, J.: A Unified Algorithm for Conditional and Probabilistic Planning. AIPS Workshop on Integrating Planning, Scheduling, and Execution in Dynamic and Uncertain Environments, June, (1998)Google Scholar
  15. 15.
    Russell, S. J., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice Hall (1995).Google Scholar
  16. 16.
    Sacerdoti, E. D.: The nonlinear nature of plans. Proceedings of the Fourth Joint Conference on Artificial Intelligence (IJCAI-75), 206–214, Tbilisi, Georgia (1975)Google Scholar
  17. 17.
    Klir, G. J., Folger, T. A.: Fuzzy Sets, Uncertainty and Information. Prentice Hall (1988)Google Scholar
  18. 18.
    Dubois, D., Prade, H.: Possibility Theory. An approach to computerized processing of uncertainty. Plenum Press (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Enrique Paniagua-Arís
  • José T. Palma-Méndez
  • Fernando Martín-Rubio

There are no affiliations available

Personalised recommendations