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Journal of Logic and Computation Advance Access published online on February 1, 2008
Journal of Logic and Computation, doi:10.1093/logcom/exm092
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© The Author, 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Original papers

Three Scenarios for the Revision of Epistemic States*

Didier Dubois

IRIT-CNRS, Université de Toulouse, France.

E-mail: dubois{at}irit.fr

*This position article was triggered by discussions with Jerome Lang and Jim Delgrande at a Belief Revision seminar in Dagstuhl, in August 2005, and presented at the 2006 Non-Monotonic Reasoning Workshop, Windermere, UK.



References

  1. E Adams. The Logic of Conditionals (1975) Reidel, UK, Dordrecht, The Netherlands.
  2. Alchourrón C, Gärdenfors P, Makinson D. On the logic of theory change: partial meet contraction and revision functions. Journal of Symbolic Logic (1985) 50:510–530.[CrossRef][ISI]
  3. Ben-Amor N, Benferhat S, Mellouli K. Anytime propagation algorithm for min-based possibilistic graphs. Soft Computing (2003) 8:150–161.[ISI]
  4. Benferhat S, Dubois D, Garcia L, Prade H. On the transformation between possibilistic logic bases and possibilistic causal networks. International Journal of Approximate Reasoning (2002) 29:135–173.[CrossRef][ISI]
  5. Benferhat S, Dubois D, Kaci S, Prade H. Encoding information fusion in possibilistic logic: a general framework for rational syntactic merging. In. In: Proceedings of 14th European Conference on Artificial Intelligence (ECAI2000) (2000) Amsterdam: IOS Press. 3–7.
  6. Benferhat S, Dubois D, Lagrue S, Papini O. Making revision reversible: an approach based on polynomials. Fundamenta Informaticae (2002) 53:251–280.[ISI]
  7. Benferhat S, Dubois D, Prade H. Nonmonotonic reasoning, conditional objects and possibility theory. Artificial Intelligence (1997) 92:259–276.[CrossRef][ISI]
  8. Benferhat S, Dubois D, Prade H. Possibilistic and standard probabilistic semantics of conditional knowledge. Journal of Logic and Computation (1999) 9:873–895.[Abstract/Free Full Text]
  9. Benferhat S, Dubois D, Prade H, Williams M. A. A practical approach to fusing prioritized knowledge bases. In. (1999) Springer, Berlin. 222–236. Proceedings of 9th Portuguese Conference on Artificial Intelligence Lecture Notes in Artificial Intelligence.
  10. Benferhat S, Dubois D, Prade H, Williams M.A. A practical approach to revising prioritized knowledge bases. Studia Logica (2002) 70:105–130.[CrossRef]
  11. Benferhat S, Kaci S. Fusion of possibilistic knowledge bases from a postulate point of view. International Journal of Approximate Reasoning (2003) 33:255–285.[CrossRef][ISI]
  12. Biazzo V, Gilio A, Lukasiewicz T, Sanfilippo G. Probabilistic Logic under Coherence, Model-Theoretic Probabilistic Logic, and Default Reasoning in System P. Journal of Applied Non-Classical Logics (2002) 12:189–213.[CrossRef]
  13. Boutilier C. Revision sequences and nested conditionals. In. In: Proceedings of IJCAI'93 (1993) Menlo Park, CA: AAAI Press. 519–525.
  14. Boutilier C, Brafman R. I, Domshlak C, Hoos H. H, Poole D. Cp-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research (2004) 21:135–191.[ISI]
  15. Boutilier C, Goldszmidt M. Revision by conditionals beliefs. In. (1993) Menlo Park, CA: AAAI Press. 649–654. Proceeding of the 11th National Conference on Artificial Intelligence (AAAI'93).
  16. Darwiche A, Pearl J. On the logic of iterated belief revision. Artificial Intelligence (1997) 89:1–29.[CrossRef][ISI]
  17. Domotor Z. Probability kinematics and representation of belief change. Philosophy of Science (1980) 47:284–403.
  18. Domotor Z. Probability kinematics – conditional and entropy principles. Synthese (1985) 63:74–115.
  19. Dubois D, Fargier H, Prade H. Ordinal and probabilistic representations of acceptance. Journal of Artificial Intelligence Research (2004) 22:23–56.[ISI]
  20. Dubois D, Fargier H, Prade H. Acceptance, conditionals, and belief revision. In. In: Conditionals, Information, and Inference (2005) Springer, Berlin. 38–58. Vol. 3301 of Lecture Notes in Artificial Intelligence.
  21. Dubois D, Lang J, Prade H. Possibilistic logic. Handbook of Logic in Artificial Intelligence and Logic Programming, In—Gabbay DM, Hogger CJ, Robinson JA, eds. (1994) Vol. 3. Oxford: Clarendon Press. 439–513.
  22. Dubois D, Moral S, Prade H. Belief change rules in ordinal and numerical uncertainty theories. In:. Belief Change—Dubois D, Prade H, eds. (1998) Dordrecht: Kluwer Acad. Publ. 311–392. Vol. 3 in Handbook on Defeasible Reasoning and Uncertainty Management Systems.
  23. Dubois D, Prade H. Belief change and possibility theory. In:. In: Belief Revision—Gärdenfors P, ed. (1992) Cambridge UK: Cambridge University Press. 142–182.
  24. Dubois D, Prade H. Non-standard theories of uncertainty in knowledge representation and reasoning. The Knowledge Engineering Review (1994) 9:399–416.
  25. Dubois D, Prade H. A synthetic view of belief revision with uncertain inputs in the framework of possibility theory. International Journal of Approximate Reasoning (1997) 17:295–324.[CrossRef][ISI]
  26. Dubois D, Prade H. Possibility theory in information fusion. In. Data Fusion and Perception (2001) Springer, Berlin. 53–76. Vol. 431 of CISM Courses and Lectures.
  27. Fishburn P. C. The axioms of subjective probabilities. Statistical Science (1986) 1:335–358.[Medline]
  28. Freund M. On the revision of preferences and rational inference processes. Artificial Intelligence (2004) 152:105–137.[CrossRef][ISI]
  29. Friedman N, Halpern J. Belief revision: a critique. In. In: Proceedings of KR'96 (1996) 421–631.
  30. Friedman N, Halpern J. Y. Plausibility measures and default reasoning. In. In: Proceedings of AAAI'96 (1996) International Conference on Principles of Knowledge Representation and Reasoning, Morgan Kaufmann: San Francisco. 1297–1304.
  31. Gärdenfors P. Knowledge in Flux: Modeling the Dynamics of Epistemic States (1988) Menlo Park, Ca: MIT Press, AAAI Press.
  32. Gärdenfors P, Makinson D. Nonmonotonic inference based on expectations. Artificial Intelligence (1994) 65:197–245.[Medline]
  33. Grove A. Two modellings for theory change. Journal of Philosophical Logic (1988) 17:157–170.[ISI]
  34. Maynard-Reid Pedrito II, Shoham Yoav. Journal of Logic, Language and Information (2001) 10:183–209. Belief fusion: aggregating pedigreed belief states.[CrossRef]
  35. Lang J, Delgrande J, Dubois D. Iterated belief revision as prioritized merging. In. In: Proceedings of KR'06, Windermere, UK (2006) International Conference on Principles of Knowledge Representation and Reasoning. Menlo Park, CA: AAAI Press. 210–220.
  36. Jeffrey R. The Logic of Decision (1965) New York: McGraw-Hill.
  37. Jin Y, Thielscher M. Iterated revision, revised. In. Proceedings of IJCAI_05 (2005) 478–483.
  38. Kelly Kevin T. Iterated belief revision, reliability, and inductive amnesia. Erkenntnis (1999) 50:7–53.[CrossRef]
  39. Kern-Isberner G. Conditionals in Nonmonotonic Reasoning and Belief Revision (2001) Berlin: Springer. Vol. 2087 of Lecture Notes in Artificial Intelligence.
  40. Konieczny S, Pino Pérez R. Merging information under constraints: a qualitative framework. Journal of Logic and Computation (2002) 12:773–808.[Abstract/Free Full Text]
  41. Kraus S, Lehmann D, Magidor M. Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence (1990) 44:167–207.[CrossRef][ISI]
  42. Lehmann D. Belief revision, revised. In. In: Proceedings of IJCAI'95 (1995) Menlo Park, Ca: AAAI Press. 1534–1540.
  43. Lehmann D, Magidor M. What does a conditional knowledge base entail? Artificial Intelligence (1992) 55:1–60.[CrossRef][ISI]
  44. Lewis D. Counterfactuals (1973) Basil Blackwell, UK.
  45. Makinson D, Gärdenfors P. Relations between the logic of theory change and nonmonotonic logic. In. In: The Logic of Theory Change (1991) Berlin: Springer. 185–205. Vol. 465 of Lecture Notes in Artificial Intelligence.
  46. Maung I. Two characterizations of a minimum information principle for possibilistic reasoning. In: International Journal of Approximate Reasoning (1995) 12. San Francisco: Morgan Kaufmann. 133–156.[CrossRef][ISI]
  47. Nayak A. Iterated belief change based on epistemic entrenchment. Erkenntnis (1994) 41:353–390.[CrossRef]
  48. Nayak A, Foo N, Pagnucco M, Sattar A. Changing conditional beliefs unconditionally. In. Proceedings of TARK96 (1996) 119–135.
  49. Nayak A, Pagnucco M, Peppas P. Dynamic belief revision operators. In: Artificial Intelligence (2003) 146:193–228.[CrossRef][ISI]
  50. Paris J. The Uncertain Reasoner_s Companion (1994) Cambridge: Cambridge University Press.
  51. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988) San Francisco: Morgan Kaufmann.
  52. Pearl J. System z: a natural ordering of defaults with tractable applications to default reasoning. In. (1990) San Mateo, CA: Morgan & Kaufmann. 121–135. Proceedings of the 3rd Conference on Theoretical Aspects of Reasoning about Knowledge (TARK'90).
  53. Rott H. Change, Choice and Inference (2001) Oxford, UK: Oxford University Press.
  54. Shafer G. A Mathematical Theory of Evidence (1976) Princeton, N.J: Princeton University Press.
  55. Spohn W. Ordinal conditional functions: a dynamic theory of epistemic states. In. In: Causation in Decision, Belief Change and Statistics—Harper William L, Skyrms Brian, eds. (1988) volume 2. Dordrecht, The Netherlands: Kluwer Academic Publishers. 105–134.
  56. van Fraassen B. Rational belief and probability kinematics. (1980) 47:165–187. Philosophy of Science.
  57. van Fraassen B. A problem for relative information minimizers. British Journal of the Philosophy of Science (1981) 33:375–379.
  58. Weydert E. How to revise ranked probabilities. In. (2000) Amsterdam: IOS Press. 38–44. Proceedings of the 14th European Conference on Artificial Intelligence (ECAI2000).
  59. Williams MA. Iterated theory-based change. In. (1995) Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI_95). Menlo Park, Ca: AAAI Press. 1541–1550.
  60. Williams. On the logic of theory base change. In. In: Proceedings of the European Workshop on Logics in Artificial Intelligence (JELIA'95) (1995) Berlin: Springer Verlag. 1541–1550. Vol. 838 of Lecture Notes in Computer Sciences.

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