Modelling inertia in action languages

Extended report
  • Mikhail Prokopenko
  • Pavlos Peppas
Reasoning with Changing and Incomplete Information
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1359)

Abstract

Logic-based approaches to reasoning about actions, change and causality, highlight efficient representation and processing of domain background knowledge as an important task. Action theories recently developed in the framework of action languages with inertia and ramifications [20,14] not only adopt the principle of minimal change reinforced with the policy of categorisation (assigning different degrees of inertia to language elements) but also try to incorporate background causal knowledge. In this paper we aim to trace the evolution of action languages and to explore interactions between ontological characteristics of action domains such as inertia and causality. Such an analysis should clarify how possible solutions to the frame and the ramification problems axe affected by applying the policy of categorisation to causal domains. We first attempt to identify conditions (more precisely, restrictions) which preserve the meaning of domain descriptions when moving among various analysed languages. Relaxing such restrictions can help in evaluating the role of the frame concept (and policy of categorisation, in general) in an action language with fluent-triggered causality.

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References

  1. 1.
    Baker, A.B.: Nonmonotonic reasoning in the framework of situation calculus. Artificial Intelligence 49 (1991) 5–23MATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Brewka, G., Hertzberg, J.: How to do things with worlds: on formalizing actions and plans. J. Logic Computat. Vol. 3, 5 (1993) 517–532MATHMathSciNetGoogle Scholar
  3. 3.
    Fikes, R., Nilsson, N.J.: STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2 (1971) 189–208MATHCrossRefGoogle Scholar
  4. 4.
    Finger, J.J.: Exploiting constraints in design synthesis. PhD Thesis. Stanford University, Stanford, CA (1987)Google Scholar
  5. 5.
    Gelfond, M., Lifschitz, V.: Representing action and change by logic programs. The Journal of Logic Programming 17 (1993) 301–322MATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Ginsberg, M.L., Smith, D.E.: Reasoning about action I: A possible worlds approach. Artificial Intelligence 35 (1988) 165–195MATHMathSciNetCrossRefGoogle Scholar
  7. 7.
    Giunchiglia E., Kartha, G.N., Lifschitz, V.: Actions with indirect effects (Extended abstract). In Working Notes of the AAAI Spring Symposium on Extending Theories of Action (1995) 80–85Google Scholar
  8. 8.
    Giunchiglia, E., Lifschitz, V.: Dependent fluents. In Proceedings of International Joint Conference on Artificial Intelligence, Montreal (1995) 1964–1969Google Scholar
  9. 9.
    Kartha, G.N.: Soundness and completeness theorems for three formalizations of action. In Proceedings of International Joint Conference on Artificial Intelligence, Montreal (1993) 724–729Google Scholar
  10. 10.
    Kartha, G.N.: On the range of applicability of Baker's approach to the frame problem. In Proceedings of the Third Symposium on Logical Formalizations of Commonsense Reasoning (1996)Google Scholar
  11. 11.
    Kartha, G.N., Lifschitz, V.: Actions with indirect effects (Preliminary report). In Proceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning, Bonn (1994) 341–350Google Scholar
  12. 12.
    Lin, F.: Embracing causality in specifying the indirect effects of actions. In Proceedings of International Joint Conference on Artificial Intelligence, Montreal (1995) 1985–1991Google Scholar
  13. 13.
    Lifschitz, V.: Nested abnormality theories. Artificial Intelligence 74 (1995) 351–365MATHMathSciNetCrossRefGoogle Scholar
  14. 14.
    Lifschitz, V.: Two components of an action language. In Proceedings of the Third Symposium on Logical Formalizations of Commonsense Reasoning, Stanford (1996)Google Scholar
  15. 15.
    McCain, N., Turner, H.: A causal theory of ramifications and qualifications. In Proceedings of International Joint Conference on Artificial Intelligence, Montreal (1995) 1978–1984Google Scholar
  16. 16.
    McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of artificial intelligence. In Machine Intelligence IV, edited by B. Meltzer and D. Michie (1969) 463–502Google Scholar
  17. 17.
    Peppas, P.: Belief change and reasoning about action. An axiomatic approach to modelling inert dynamic worlds and the connection to the logic of theory change. PhD thesis. Dept. of Computer Science, University of Sydney (1993)Google Scholar
  18. 18.
    Prokopenko, M., Lindley, C., Kumar, V.R.: The application of reasoning about action techniques to dispatch management. In Proceedings of the AI'95 First Australian Workshop on Commonsense Reasoning, Canberra (1995) 74–88Google Scholar
  19. 19.
    Thielscher, M.: Computing ramification by postprocessing. In Proceedings of International Joint Conference on Artificial Intelligence, (1995) 1994–2000Google Scholar
  20. 20.
    Turner, H.: Representing actions in default logic: a situation calculus approach. In Proceedings of the Third Symposium on Logical Formalizations of Commonsense Reasoning, Stanford (1996)Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Mikhail Prokopenko
    • 1
  • Pavlos Peppas
    • 2
  1. 1.CSIRO Mathematical and Information SciencesNorth RydeAustralia
  2. 2.Knowledge Systems Group Department of Computing, School of MPCEMacquarie UniversityAustralia

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