Focusing vs. Belief revision: A fundamental distinction when dealing with generic knowledge

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1244, Issue , 1997, Pages 96-107

  Dubois, Didier ^a   Prade, Henri ^a  

^a IRIT   (France)

Author keywords

[No Author keywords available]

Indexed keywords

UNCERTAINTY ANALYSIS;

BELIEF FUNCTION; BELIEF REVISION; CONSEQUENCE RELATIONS; FACTUAL INFORMATION; NONMONOTONIC; POSSIBILITY MEASURE; UPPER AND LOWER PROBABILITIES;

FOCUSING;

EID: 84947749482     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0035615     Document Type: Conference Paper

References (34)

1. Benferhat S., Dubois D., Prade H. (1992) Representing default rules in possibilistic logic, Proc. of the 3rd Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR’92), Cambridge, Mass., Oct. 26-29, 673-684.
2. Benferhat S., Dubois D., Prade H. (1996) Beyond counter-examples to nonmonotonic formalisms: A possibility-theoretic analysis. Proc. of the 12th Europ. Conf. on Artificial Intelligence (ECAI’96) (W. Wahlster, ed.), Budapest, Hungary, Aug. 11-16, 1996, John Wiley & Sons, New York, 652-656.
3. Boutilier C., Goldszmidt M. (1993) Revision by conditionals beliefs. Proc. of the 11th National Conf. on Artificial Intelligence (AAAI’93), Washington, DC, July 11-15, 649-654.
4. De Campos L.M., Lamata M.T., Moral S. (1990) The concept of conditional fuzzy measure. Int. J. of Intelligent Systems, 5, 237-246.
5. De Finetti B. (1974) Theory of Probability. Wiley, New York.
6. Dempster A.P. (1967) Upper and lower probabilities induced by a multiple-valued mapping. Annals of Mathematical Statistics, 38, 325-339.
7. Domotor Z. (1985) Probability kinematics — Conditionals and entropy principles. Synthese, 63, 74-115.
8. Dubois D., Prade H. (1992a) Evidence, knowledge, and belief functions. Int. J. of Approximate Reasoning, 6, 295-319.
9. Dubois D., Prade H. (1992b) Belief change and possibility theory. In: Belief Revision (P. Gärdenfors, ed.), Cambridge Univ. Press, Cambridge, UK, 142-182.
10. Dubois D., Prade H. (1994a) Focusing versus updating in belief function theory. In: Advances in the Dempster-Shafer Theory of Evidence (R.R. Yager, M. Fedrizzi, J. Kacprzyk, eds.), Wiley, New York, 71-95.
11. Dubois D., Prade H. (1994b) Conditional objects as nonmonotonic consequence relationships. IEEE Trans, on Systems, Man and Cybernetics, 24(12), 1724-1740.
12. Dubois D., Prade H. (1995) Conditional objects, possibility theory, and default rules. In: Conditional Logics: From Phisolophy to Computer Science (G. Crocco, L. Farinas del Cerro, A. Herzig, eds.), Oxford University Press, 301-336.
13. Dubois D., Prade H. (1996) Focusing vs. revision in possibility theory. Proc. of the 5th IEEE Inter. Conf. on Fuzzy Systems (FUZZ-IEEE’96), New Orleans, LO, Sept. 8-11, 1700-1705.
14. Dubois D., Prade H. (1997) Bayesian conditioning in possibility theory. Fuzzy Sets and Systems, to appear.
15. Dubois D., Prade H., Smets P. (1996) Representing partial ignorance. IEEE Trans, on Systems, Man and Cybernetics, 26(3), 361-377.
16. Fagin R., Halpem J. (1989) A new approach to updating belief. Research Report RJ 7222, IBM Research Division, San Jose, CA.
17. Gärdenfors P. (1986) Belief revision and the Ramsay test for conditionals. Philosophical Review, 91, 81-83.
18. Gärdenfors P. (1988) Knowledge in Flux — Modeling the Dynamics of Epistemic States. The MIT Press, Cambridge, MA.
19. Gardenfors P., Makinson D. (1994) Non-monotonic inference based on expectations. Artificial Intelligence, 65, 197-245.
20. Gilboa I., Schmeidler D. (1992) Updating ambiguous beliefs. Proc. of the 4th Conf. on Theoretical Aspects of Reasoning about Knowledge (TARK’92) (Y. Moses, ed.), Monterey, CA, March 22-25, 143-162.
21. Huber P.J. (1981) Robust Statistics. Wiley, New York.
22. Jaffray J.Y. (1992) Bayesian updating and belief functions. IEEE Trans, on Systems, Man and Cybernetics, 22, 1144-1152.
23. Jeffrey R. (1965) The Logic of Decision. McGraw-Hill, New York. 2nd edition, University of Chicago Press, 1983.
24. Katsuno H., Mendelzon A.O. (1991) On the difference between updating a knowledge base and revising it. Proc. of the 2nd Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR’91) Cambridge, MA, April 22-25, 387-394.
25. Kraus S., Lehmann D., Magidor M. (1990) Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence, 44, 167-207.
26. Kyburg H.E., Jr. (1987) Bayesian and non-Bayesian evidential updating. Artificial Intelligence, 31, 271-293.
27. Lehmann D., Magidor M. (1992) What does a conditional knowledge base entail? Artificial Intelligence, 55(1), 1-60.
28. Levi (1980) The Enterprize of Knowledge. The MIT Press, Cambridge, MA.
29. Makinson D., Gardenfors P. (1991) Relations between the logic of theory change and nonmonotonic logic. The Logic of Theory Change (Proc. of the Workshop, Konstanz, Germany, Oct. 1989) (A. Fuhrmann, M. Morreau, eds.), Lecture Notes in Artificial Intelligence, Vol. 465, Springer Verlag, Berlin, 185-205.
30. Moral S., De Campos L. (1991) Updating uncertain information. In: Uncertainty in Knowledge Bases (B. Bouchon-Meunier, R.R. Yager, L.A. Zadeh, eds.), Springer Verlag, Berlin, 58-67.
31. Shafer G. (1976) A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ.
32. Walley P. (1996) Measures of uncertainty in expert systems. Artificial Intelligence, 83, 158.
33. Williams M.A. (1994) Transmutations of knowledge systems. Proc. of the 4th Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR’94), Bonn, Germany, 619-632.
34. Williams P.M. (1980) Bayesian conditionalization and the principle of minimum information. British J. for the Philosophy of Sciences, 31, 131-144.