Studia Logica

, Volume 70, Issue 1, pp 105–130 | Cite as

A Practical Approach to Revising Prioritized Knowledge Bases

  • Salem Benferhat
  • Didier Dubois
  • Henri Prade
  • Mary-Anne Williams
Article

Abstract

This paper investigates simple syntactic methods for revising prioritized belief bases, that are semantically meaningful in the frameworks of possibility theory and of Spohn's ordinal conditional functions. Here, revising prioritized belief bases amounts to conditioning a distribution function on interpretations. The input information leading to the revision of a knowledge base can be sure or uncertain. Different types of scales for priorities are allowed: finite vs. infinite, numerical vs. ordinal. Syntactic revision is envisaged here as a process which transforms a prioritized belief bases into a new prioritized belief base, and thus allows a subsequent iteration.

belief revision Spohn ordinal conditional functions possibility theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Benferhat, S., D. Dubois, H. Prade. How to infer from inconsistent beliefs without revising?. Proc. of IJCAI'95, pp. 1449-1455, 1995.Google Scholar
  2. Benferhat, S., D. Dubois, H. Prade, and M. Williams. A practical approach to fusing and revising prioritized belief bases. Procs. EPIA-99, LNAI no 1695, Springer Verlag, pp. 222-236, 1999.Google Scholar
  3. Benferhat, S., D. Dubois, H. Prade, M.-A. Williams. A practical approach to revising prioritized knowledge bases. In Procs. 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems, KES'99, Adelaide (Australia), IEEE Press, pp. 170-174, 1999.Google Scholar
  4. Boutilier, C. Revision sequences and nested conditionals. In Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI'93), Chambéry, pp. 519-525, 1993.Google Scholar
  5. Boutilier, C., and M. Goldszmidt. Revision by conditional beliefs. In Proceedings of AAAI'93, pp. 649-654, 1993.Google Scholar
  6. Darwiche D., and J. Pearl. On the logic of iterated belief revision. Artificial Intelligence 89, 1-29, 1997.Google Scholar
  7. Cholvy, L. Reasoning about merging information. In Handbook of Defeasible Reasoning and Uncertainty Management Systems (D. Gabbay, Ph. Smets, eds.) Vol. 3: Belief Change (D. Dubois and H. Prade, eds.), pp. 233-263, Kluwer Academic Press, 1998.Google Scholar
  8. Dubois, D., J. Lang, H. Prade. Possibilistic logic. In: Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 3, (D. Gabbay et al., eds.) pp. 439-513, 1994.Google Scholar
  9. Dubois, D., H. Prade. Epistemic entrenchment and possibilistic logic. Artificial Intelligence 50, 223-239, 1991.Google Scholar
  10. Dubois, D., H. Prade. Belief change and possibility theory. In: Belief Revision, (P. Gärdenfors, ed.), pp. 142-182, 1992.Google Scholar
  11. Dubois, D., H. Prade. A synthetic view of belief revision with uncertain inputs in the framework of possibility theory. International Journal of Approximate Reasoning 17, 295-324, 1997.Google Scholar
  12. Dubois, D., H. Prade. Possibility theory: qualitative and quantitative aspects. In: Handbook of Defeasible Reasoning and Uncertainty Management Systems (D. Gabbay, Ph. Smets, eds.) Vol. 1: Quantified Representation of Uncertainty and Imprecision (Ph. Smets, ed.), pp. 169-226, Kluwer Academic Press, 1998.Google Scholar
  13. Gärdenfors, P. Knowledge in Flux — Modeling the Dynamic of Epistemic States, MIT Press, 1988.Google Scholar
  14. Nebel, B. Base revision operator and schemes: semantics representation and complexity. Proceedings of 11th European Conference on Artificial Intelligence (ECAI'94), pp. 341-345, 1994.Google Scholar
  15. Papini, O. Iterated revision operations stemming from the history of an agent's observations. In Frontiers of Belief Revision, H. Rott and M. Williams, eds. 2001.Google Scholar
  16. Peppas, P., and M.-A. Williams. Constructive modelings for theory change. Notre Dame Journal of Formal Logic 36(1):120-133, 1995.Google Scholar
  17. Spohn, W. Ordinal conditional functions: A dynamic theory of epistemic states. In: Causation in Decision, Belief Change, and Statistics, Vol. 2 (W. L. Harper, B. Skyrms, eds.), D. Reidel, Dordrecht, pp. 105-134, 1998.Google Scholar
  18. Williams, M.-A. Transmutations of knowledge systems. Proceedings of 4th International Conference of Principles of Knowledge Representation and Reasoning (KR'94), pp. 619-629, 1994.Google Scholar
  19. Williams, M.-A. Iterated theory base change: A computational model. In Procs. IJCAI-95, Montreal, pp. 1541-1550, 1995.Google Scholar
  20. Williams, M.-A. On the logic of theory base change. In Logics in Artifical Intelligence, Lecture Note Series in Computer Science No 838, Springer Verlag, pp. 86-105, 1994.Google Scholar
  21. Zadeh, L. A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3-28, 1978.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Salem Benferhat
    • 1
  • Didier Dubois
    • 1
  • Henri Prade
    • 1
  • Mary-Anne Williams
    • 2
  1. 1.Institut de Recherche en Informatique de Toulouse (IRIT)Université Paul SabatierToulouse Cedex 4France
  2. 2.Information Systems School of ManagementUniversity of NewcastleAustralia

Personalised recommendations