Acceptance, conditionals, and belief revision

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3301, Issue , 2005, Pages 38-58

  Dubois, Didier ^a   Fargier, Hélène ^a   Prade, Henri ^a  

^a UNIVERSITÉ PAUL SABATIER   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; FUNCTIONS; INFORMATION RETRIEVAL; SET THEORY; SOFTWARE AGENTS;

LOGICAL REPRESENTATIONS; PROBABILITY MEASURES;

PROBABILITY;

BELIEF STRUCTURES; COMPARATIVE BELIEF RELATIONS; CONTEXTS; PLAUSIBILITY FUNCTIONS;

EID: 26444529452     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11408017_3     Document Type: Conference Paper

References (46)

1. E.W. Adams, The Logic of Conditionals. Dordrecht: D. Reidel (1975).
2. C.E.P. Alchourrón, P. Gärdenfors, D. Makinson, On the logic of theory change: Partial meet functions for contraction and revision. J. of Symbolic Logic, 50 (1985) 513-530.
3. S. Benferhat, D. Dubois, H. Prade, Representing default rules in possibilistic logic. Proc. of the 3rd Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR'92), Cambridge, MA, (1992) 673-684.
4. S. Benferhat, D. Dubois, H. Prade Nonmonotonic reasoning, conditional objects and possibility theory. Artificial Intelligence, 92 (1997) 259-276.
5. S. Benferhat, D. Dubois, H. Prade Possibilistic and standard probabilistic semantics of conditional knowledge. Journal of Logic and Computation, 9 (1999) 873-895.
6. S. Benferhat, D. Dubois, O. Papini A sequential reversible belief revision method based on polynomials.: Proceedings National American AI conference (AAAI-99), Orlando, Floride (USA) AAAI Press/The MIT Press, (1999) 733-738.
7. S. Benferhat, D. Dubois, S. Lagrue, H. Prade A big-stepped probability approach for discovering default rules. Proc. of the Ninth Int. Conf. Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU 2002), Annecy, France, (2002) 283-289.
8. a revised version is to appear in the Int. J. Uncertainty, Fuzziness and Knowledge-based Systems.
9. C. Boutilier M. Goldszmidt Revision by conditionals beliefs. In: G. Crocco, L. Fariñas del Cerro, A. Herzig, (eds.) Conditionals: From Philosophy to Computer Sciences, Oxford University Press, Oxford, UK, (1995) 267-300.
10. P. Calabrese An algebraic synthesis of the foundations of logic and probability. Information Sciences, 42 (1987) 187-237.
11. L.J. Cohen, The Probable and the Provable. Clarendon Press Oxford (1977).
12. L. J. Cohen, Belief and acceptance. Mind, XCVIII, No. 391 (1989) 367-390.
13. A. Darwishe, J. Pearl On the logic of iterated belief revision. Artificial Intelligence, 89, 1997 (1997) 1-29.
14. D. Dubois Belief structures, possibility theory and decomposable confidence measures on finite sets. Computers and Artificial Intelligence (Bratislava), 5(5) (1986) 403-416.
15. D. Dubois, H. Fargier, H. Prade Comparative uncertainty, belief functions and accepted beliefs. Proc. of 14th Conf. on Uncertainty in Artificial Intelligence (UAI-98), Madison, Wi., (1998) 113-120
16. D. Dubois, H. Fargier, H. Prade, Ordinal and numerical representations of acceptance. Research report 02-20R, IRIT, Toulouse (2002).
17. D. Dubois, S. Moral and H. Prade, Belief change rules in ordinal and numerical uncertainty theories. In D. Dubois H. Prade, (eds.), Belief Change, Handbook on Defeasible Reasoning and Uncertainty Management Systems, Vol. 3: Kluwer Academic Publ., Dordrecht, The Netherlands, (1998) 311-392.
18. D. Dubois, H. Prade, Epistemic entrenchment and possibilistic logic. Artificial Intelligence 50 (1991) 223-239.
19. D. Dubois, H. Prade, Conditional objects as non-monotonic consequence relationships. IEEE Trans, on Systems, Man and Cybernetics, 24 (1994) 1724-1740.
20. D. Dubois, H. Prade, Conditional objects, possibility theory and default rules. In: G. Crocco, L. Farinas del Cerro, A. Herzig (eds.), Conditionals: From Philosophy to Computer Science (Oxford University Press, UK, (1995) 311-346.
21. D. Dubois, H. Prade, Numerical representation of acceptance. Proc. of the 11th Conf. on Uncertainty in Artificial Intelligence, Montréal, Quebec (1995) 149-156
22. D. Dubois, H. Prade, Possibility theory: qualitative and quantitative aspects. P. Smets, editor, Quantified Representations of Uncertainty and Imprecision. Handbook on Defeasible Reasoning and Uncertainty Management Systems, Vol. 1: Kluwer Academic Publ., Dordrecht, The Netherlands (1998) 169-226
23. L.Fariñas del Cerro, A. Herzig A modal analysis of possibility theory. In Ph. Jorrand, J. Kelemen, (eds.) Fundamentals of Artificial Intelligence Research (FAIR'91) Lecture Notes in Computer Sciences, Vol. 535, Springer Verlag, Berlin (1991) 11-18.
24. P. Fishburn, The axioms of subjective probability. Statistical Science, I (1986) 335-358.
25. N. Friedman, J. Halpern, Plausibility measures and default reasoning. Proc of the 13th National Conf. on Artificial Intelligence (AAAI'96), Portland, OR, (1996) 1297-1304.
26. P. Gärdenfors, Knowledge in Flux. MIT press, Cambridge, MA. (1988).
27. I.R. Goodman, H.T. Nguyen, E.A. Walker, Conditional Inference and Logic for Intelligent Systems: a Theory of Measure-Free Conditioning. North-Holland, Amsterdam, The Netherlands (1991).
28. A. Grove, Two modellings for theory change. J. Philos. Logic, 17 (1988) 157-170.
29. J. Halpern Defining relative likelihood in partially-ordered preferential structures. J. AI Research, 1 (1997) 1-24.
30. G. Kern-Isberner, Conditionals in Nonmonotonic Reasoning and Belief Revision. Lecture Notes in Artificial Intelligence, Vol. 2087, Springer Verlag, Berlin (2001).
31. C.H. Kraft, J.W Pratt and A. Seidenberg, Intuitive probability on finite sets. Ann. Math. Stat. 30 (1959) 408-419.
32. K. Kraus, D. Lehmann, M. Magidor, Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence, 44 (1990) 167-207.
33. H. E. Kyburg, Probability and the Logic of Rational Belief. Wesleyan University Press. Middletown, Ct. (1961).
34. H. E. Kyburg, Knowledge. In: Uncertainty in Artificial intelligence, vol 2, J. F. Lemmer and L. N. Kanal eds, Elsevier, Amsterdam (1988) 263-272.
35. D. Lehmann, M. Magidor, What does a conditional knowledge base entail? Artificial Intelligence, 55(1) (1992) 1-60.
36. D. Lehmann Belief revision, revised. Proc. of the 14th Inter. Joint Conf. on Artificial Intelligence (IJCAI'95), Montreal, Quebec, (1995) 1534-1540.
37. D. L. Lewis Counterfactuals. Basil Blackwell, Oxford, UK (1973).
38. D. Makinson and P. Gärdenfors Relations between the logic of theory change and nonmonotonic reasoning. In: The Logic of Theory Change (A. Fürmann, M. Morreau, Eds), LNAI465, Sprnger Verlag, (1991) 185-205.
39. J. Paris, The Uncertain Reasoner's Companion. Cambridge University Press, Cambridge, UK (1994).
40. J. Pearl, Probabilistic Reasoning Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA. (1988).
41. J. Pearl System Z: a natural ordering of defaults with tractable applications to default reasoning. Proc. of the 3rd Conf. on the Theoretical Aspects of Reasoning About Knowledge (TARK'90), Morgan and Kaufmann, San Mateo, CA. (1990) 121-135.
42. D. Poole The effect of knowledge on belief: conditioning, specificity and the lottery paradox in defaut reasoning. Artificial Intelligence, 49 (1991) 281-307
43. M. Roubens, P. Vincke, Preference Modelling. Lecture Notes in Economics and Mathematical Systems, Vol. 250, Springer Verlag, Berlin (1985).
44. P. Snow Diverse confidence levels in a probabilistic semantics for conditional logics. Artificial Intelligence, 113 (1999) 269-279.
45. W. Spohn Ordinal conditional functions: A dynamic theory of epistemic states. In: W.L. Harper, B. Skyrms, (eds.), Causation in Decision, Belief Change, and Statistics, Vol. 2, D. Reidel, Dordrecht (1988) 105-134.
46. M.A. Williams Transmutations of knowledge systems. In: J. Doyle, E. Sandewall, P. Torasso (eds.), Proc. of the 4th Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR'94), Bonn, Germany, (1994) 619-629