Efficient Reasoning About Action and Change in the Presence of Incomplete Information and Its Application in Planning

  • Phan Huy Tu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4079)


Many domains that we wish to model and reason about are subject to change due to the execution of actions. Representing and reasoning about dynamic domains play an important role in AI because they serve as a fundamental basis for many applications, including planning, diagnosis, and modelling. Research in the field focuses on the development of formalisms for reasoning about action and change (RAC). Such a formalism normally consists of two components: a representation language and a reasoning mechanism. It has been well known in the field that two criteria for the success of a formalism are its expressiveness and efficiency. The former means that the representation language is rich enough to describe complicated domains; the latter implies the reasoning mechanism is computationally efficient, making it possible to be implemented on a machine. Besides, in daily life, we have to face the absence of complete information and thus any formalism should take this matter into account.


Incomplete Information Action Theory Logic Programming Goal Preference Representation Language 
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.


  1. 1.
    Baral, C., Kreinovich, V., Trejo, R.: Computational complexity of planning and approximate planning in the presence of incompleteness. AI 122, 241–267 (2000)MATHMathSciNetGoogle Scholar
  2. 2.
    Brafman, R., Chernyavsky, Y.: Planning with goal preferences and constraints. In: ICAPS 2005 (2005)Google Scholar
  3. 3.
    Frühwirth, T.: Theory and practice of constraint handling rules. In: JLP 1998 (1998)Google Scholar
  4. 4.
    Gelfond, M., Morales, R.: Encoding conformant planning in A-PROLOG. In: DRT 2004 (2004)Google Scholar
  5. 5.
    Levesque, H.: What is planning in the presence of sensing? In: Proceedings of the 14th Conference on Artificial Intelligence, pp. 1139–1146. AAAI Press, Menlo Park (1996)Google Scholar
  6. 6.
    Moore, R.: A formal theory of knowledge and action. In: Hobbs, J., Moore, R. (eds.) Formal theories of the commonsense world. Ablex, Norwood (1985)Google Scholar
  7. 7.
    Son, T., Baral, C.: Formalizing sensing actions - a transition function based approach. Artificial Intelligence 125(1-2), 19–91 (2001)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Son, T., Pontelli, E.: Planning with Preferences Using Logic Programming. In: Lifschitz, V., Niemelä, I. (eds.) LPNMR 2004. LNCS (LNAI), vol. 2923, pp. 247–260. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Son, T., Tu, P.: On the Completeness of Approximation Based Reasoning and Planning in Action Theories with Incomplete Information. In: KR 2006 (2006)Google Scholar
  10. 10.
    Thiebaux, S., Hoffmann, J., Nebel, B.: In Defense of PDDL Axioms. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Phan Huy Tu
    • 1
  1. 1.Department of Computer ScienceNew Mexico State UniversityMSC CS, Las CrucesUSA

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