Observability-based Nested Belief Computation for Multiagent Systems and Its Formalization

  • Hideki Isozaki
  • Hirofumi Katsuno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1757)

Abstract

Some agent architectures employ mental states such as belief, desire, goal, and intention. We also know that one often has a belief about someone else’s belief (nested belief), and one’s action is decided based on the nested belief. However, to the best of our knowledge, there is no concrete agent architecture that employs nested beliefs for decision. The reason is simple: we do not have a good model of nested belief change. Hence, interesting technological questions are whether such a model can be devised or not, how it can be implemented, and how it can be used. In a previous paper, we proposed an algorithm for nested beliefs based on observability and logically characterized its output. Here, we propose another algorithm with improved expressiveness and efficiency.

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References

  1. 1.
    Appelt, D.E.: Planning English Sentences. Cambridge University Press, Cambridge (1985)CrossRefGoogle Scholar
  2. 2.
    Ballim, A., Wilks, Y.: Artificial Believers, The Ascription of Belief. Lawrence Erlbaum Associates, Mahwah (1991)Google Scholar
  3. 3.
    Baral, C., Son, T.C.: Extending ConGolog to allowpartial ordering. In: Jennings, N.R. (ed.) ATAL 1999. LNCS (LNAI), vol. 1757, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Baral, C., Son, T.C.: Approximate reasoning about actions in presence of sensing and incomplete information. In: Proceedings of the International Logic Programming Symposium (1997)Google Scholar
  5. 5.
    Brafman, R.I., Tennenholtz, M.: Belief ascription and mental-level modelling. In: Proceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning, pp. 87–98. Morgan Kaufmann, San Francisco (1994)Google Scholar
  6. 6.
    Davis, E.: Inferring ignorance from the locality of visual perception. In: Proceedings of the Seventh National Conference on Artificial Intelligence, pp. 786–790. Morgan Kaufmann, San Francisco (1988)Google Scholar
  7. 7.
    Fagin, R., Halpern, J.Y., Moses, Y., Vardi, M.Y.: Reasoning About Knowledge. MIT Press, Cambridge (1995)MATHGoogle Scholar
  8. 8.
    Ferber, J., Gutknecht, O.: Operational semantics of a role-based agent architecture. In: Jennings, N.R. (ed.) ATAL 1999. LNSC (LNAI), vol. 1757, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Friedman, N., Halpern, J.Y.: Modeling belief in dynamic systems. Part I: Foundations. Artificial Intelligence 95(2), 257–316 (1997)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Friedman, N., Halpern, J.Y.: Modeling belief in dynamic systems. Part II: Revision and update. Journal of Artificial Intelligence Research 10, 117–167 (1999)MATHMathSciNetGoogle Scholar
  11. 11.
    Isozaki, H.: Reasoning about belief based on common knowledge of observability of actions. In: Proceedings of the International Conference on Multi-Agent Systems, pp. 193–200. MIT Press, Cambridge (1995)Google Scholar
  12. 12.
    Isozaki, H., Katsuno, H.: A semantic characterization of an algorithm for estimating others’ beliefs from observation. In: Proceedings of the National Conference on Artificial Intelligence, pp. 543–549. MIT Press, Cambridge (1996)Google Scholar
  13. 13.
    Isozaki, H., Katsuno, H.: A regressive belief estimation algorithm in multiagent environments (in Japanese). Transactions of Information Processing Society of Japan 38(3), 429–442 (1997)Google Scholar
  14. 14.
    Isozaki, H., Shoham, Y.: A mechanism for reasoning about time and belief. In: Proceedings of the International Conference on Fifth Generation Computer Systems, pp. 694–701. Ohmsha (1992)Google Scholar
  15. 15.
    Jaspars, J., Thijsse, E.: Fundamentals of partial modal logic. In: Partiality, Modality, and Nonmonotonicity, pp. 111–141. CSLI Publications (1996)Google Scholar
  16. 16.
    Katsuno, H., Mendelzon, A.O.: On the difference between updating a knowledge base and revising it. In: Belief Revision, pp. 183–203. Cambridge University Press, Cambridge (1992)CrossRefGoogle Scholar
  17. 17.
    Konolige, K.: A Deduction Model of Belief. Morgan Kaufmann Publishers, San Francisco (1986)MATHGoogle Scholar
  18. 18.
    Konolige, K.: Explanatory belief ascription. In: Proceedings of the Third Conference on Theoretical Aspects of Reasoning About Knowledge, pp. 57–72. Morgan Kaufmann, San Francisco (1990)Google Scholar
  19. 19.
    Kraus, S., Lehmann, D.: Knowledge, belief and time. Theoretical Computer Science 58, 155–174 (1988)MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Lespérance, Y., Tam, K., Jenkin, M.: Reactivity in a logic-based robot programming framework. In: Jennings, N.R. (ed.) ATAL 1999. LNCS (LNAI), vol. 1757, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  21. 21.
    Levesque, H.J., Reiter, R., Lespérance, Y., Lin, F., Scherl, R.B.: Golog:A logic programming language for dynamic domains. Journal of Logic Programming 31, 59–84 (1997)MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Lomuscio, A.: An algorithmic approach to knowledge evolution. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13(2) (1992)Google Scholar
  23. 23.
    Lomuscio, A., Ryan, M.: A spectrum of modes of knowledge sharing between agents. In: Jennings, N.R. (ed.) ATAL 1999. LNCS (LNAI), vol. 1757, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  24. 24.
    Moore, R.C.: A formal theory of knowledge and action. In: Hobbs, J.R., Moore, R.C. (eds.) Formal Theories of the Commonsense World, ch. 9, pp. 319–358. Ablex Publishing, Greenwich (1985)Google Scholar
  25. 25.
    Pynadath, D.V., Tambe, M., Chauvat, N., Cavedon, L.: Toward teamoriented programming. In: Jennings, N.R. (ed.) ATAL 1999. LNCS (LNAI), vol. 1757, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  26. 26.
    Rao, A.S.: Decision procedures for propositional linear-time belief desire-intention logics. In: Tambe, M., Müller, J., Wooldridge, M.J. (eds.) IJCAI-WS 1995 and ATAL 1995. LNCS, vol. 1037, pp. 33–48. Springer, Heidelberg (1996)Google Scholar
  27. 27.
    Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs (1995)Google Scholar
  28. 28.
    Scherl, R.B., Levesque, H.J.: The frame problem and knowledge-producing actions. In: Proceedings of the National Conference on Artificial Intelligence, pp. 689–695 (1993)Google Scholar
  29. 29.
    Shoham, Y.: Agent-oriented programming. Artificial Intelligence 60, 51–92 (1993)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Sripada, S.M.: A metalogic programming approach to reasoning about time in knowledge bases. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 860–865. Morgan Kaufmann, San Francisco (1993)Google Scholar
  31. 31.
    van der Meyden, R.: Common knowledge and update in finite environments. I (extended abstract). In: Proceedings of the Fifth Conference on Theoretical Aspects of Reasoning About Knowledge, pp. 225–242. Morgan Kaufmann, San Francisco (1994)Google Scholar
  32. 32.
    van der Meyden, R.: Mutual belief revision. In: Proceedings of the Conference on Principles of Knowledge Representation and Reasoning, pp. 595–606. Morgan Kaufmann, San Francisco (1994)Google Scholar
  33. 33.
    van Eijk, R.M., de Boer, F.S., van der Hoek, W., Meyer, J.-J.C.: Open multi-agent systems: Agent communication and integration. In: Jennings, N.R. (ed.) ATAL 1999. LNCS (LNAI), vol. 1757, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  34. 34.
    van Linder, B., van der Hoek, W., Meyer, J.-J.C.: Seeing is believing—and so are hearing and jumping. In: Dreschler-Fischer, L., Nebel, B. (eds.) KI 1994. LNCS, vol. 861, pp. 402–413. Springer, Heidelberg (1994)Google Scholar
  35. 35.
    Vidal, J.M., Durfee, E.H.: Recursive agent modelling using limited rationality. In: Proceedings of the International Conference on Multi-Agent Systems. MIT Press, Cambridge (1995)Google Scholar
  36. 36.
    Wagner, G.: A logical and operational model of scalable knowledge- and perception-based agents. In: Perram, J., Van de Velde, W. (eds.) MAAMAW 1996. LNCS, vol. 1038. Springer, Heidelberg (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Hideki Isozaki
    • 1
  • Hirofumi Katsuno
    • 1
  1. 1.NTT Communication Science LaboratoriesAtsugi-shi, Kanagawa-kenJapan

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