Planning under uncertainty: A qualitative approach

  • Nikos I. Karacapilidis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 990)


Hierarchical planners create descriptions of abstract states and divide their planning task into subproblems for refining these states. In spite of their success in reducing the search space, they classically assume the existence of certain and complete information. In real world planning instances, one has to select among alternative strategies at each abstract state, observing both incomplete knowledge of the attributes that each strategy may pose, and partial ordering of these attributes. In addition, reasoning is defeasible: further information may cause another alternative to be more preferable than what seems optimal at the moment. This work presents a planning framework based on qualitative value decision making formalisms. Sketching the appropriate strategy operator schemata for hierarchical planning, it focuses on aspects of uncertainty handling by combining abilities of constraint programming languages with the introduced concepts of credulous and skeptical conclusions of an issue. The topics of argumentation among the planning agents and common conflicts in the ordering of defaults are discussed by means of a real planning example.


planning qualitative reasoning uncertainty 


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  1. 1.
    Allen, J.F., Schubert, L.K., Ferguson, G., Heeman, P., Hwang, C.H., Kato, Ts., Light, M., Martin, N., Miller, Br., Poesio, M., Traum, D.R.: The TRAINS project: a case study in building a conversational planning agent. Journal of Experimental and Theoretical Artificial Intelligence 7, (1995) 7–48.Google Scholar
  2. 2.
    Brewka, G.: Preferred Subtheories — An Extended Logical Framework for Default Reasoning. Proceedings of IJCAI-89, Detroit (1989) 1043–1048.Google Scholar
  3. 3.
    Brewka, G.: Reasoning about Priorities in Default Logic. Proceedings of AAAI-94, Seattle (1994) 940–945.Google Scholar
  4. 4.
    Brewka, G., Gordon, Th.: How to Buy a Porsche: An Approach to Defeasible Decision Making. Working Notes of AAAI-94 Workshop on Computational Dialectics, Seattle (1994) 28–38 (available in zeno2.html).Google Scholar
  5. 5.
    Brewka, G., Gordon, Th., Karacapilidis, N.I.: Mediating Systems for Group Decision Making: the Zeno System. KI-95 (German National AI Conference) Workshop on Computational Dialectics: Models of Argumentation, Negotiation and Decision Making, Bielefeld, Germany (1995); to appear.Google Scholar
  6. 6.
    Doyle, J., Shoham, Y., Wellman, M.P.: A Logic of Relative Desire. in Z.W. Ras and M. Zemankova (eds.) Proceedings of the 6th Int. Symposium on Methodologies for Intelligent Systems, ISMIS-91 (1991) 16–31 (available in Scholar
  7. 7.
    Gordon, Th.: Computational Dialectics. Proceedings of Workshop Kooperative Juristische Informationssysteme, Wien, Austria, GMD Studien Nr. 241 (1994) 25–36 (available in Scholar
  8. 8.
    Haddawy, P., Hanks, St.: Utility Models for Goal-Directed Decision-Theoretic Planners. Technical Report TR 93-06-04, University of Washington, Dept. of CS and Engineering (1993); also under review in JAIR.Google Scholar
  9. 9.
    Karacapilidis, N.I., Gordon, Th.: Dialectical Planning. Proceedings of 14th International Joint Conference on Artificial Intelligence (IJCAI-95) Workshop on Intelligent Manufacturing Systems, Montreal (1995); to appear.Google Scholar
  10. 10.
    Karacapilidis, N.I., Pappis, C.P., Adamopoulos, G.I.: Designation of abstraction levels for handling uncertain and incomplete information in planning problems. 14th European Conference on Operational Research (EURO XIV), Jerusalem, Israel (1995); to appear.Google Scholar
  11. 11.
    Kushmerick, N., Hanks, St., Weld, D.: An Algorithm for Probabilistic Least-Commitment Planning. Proceedings of AAAI-94, Seattle (1994) 1073–1078.Google Scholar
  12. 12.
    Mansell, T.M.: A method for planning given uncertain and incomplete information. Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence, Washington (1993) 350–358.Google Scholar
  13. 13.
    Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann Publishers, San Mateo, CA (1988).Google Scholar
  14. 14.
    Petrie, C.: Constrained Decision Revision. Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI-92, San Hose (1992) 393–400.Google Scholar
  15. 15.
    Sacerdoti, E.D.: Planning in a Hierarchy of Abstraction Spaces. Artificial Intelligence 5 (1974) 115–135.CrossRefGoogle Scholar
  16. 16.
    Tan, S., Pearl, J.: Qualitative Decision Theory. Proceedings of AAAI-94, Seattle (1994) 928–933.Google Scholar
  17. 17.
    Wellman, M.P., Doyle, J.: Preferential Semantics for Goals. Proceedings of AAAI-91 (1991) 698–703 (available in Scholar
  18. 18.
    Wilkins, D.E., Myers, K.L.: A Common Knowledge Representation for Plan Generation and Reactive Execution. SRI AI Center Technical Note 532R (1994); also to appear in Journal of Logic and Computation (available in Scholar
  19. 19.
    Williamson, M., Hanks, St.: Optimal Planning with a Goal-Directed Utility Model. Proceedings of 2nd International Conference on Artificial Intelligence Planning Systems, Chicago (1994) 176–181.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Nikos I. Karacapilidis
    • 1
  1. 1.Artificial Intelligence Research Division Institute for Applied Information TechnologyGMD - German National Research Center for Computer Science Schloss BirlinghovenSankt AugustinGermany

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