An Experimental Analysis of Possibilistic Default Reasoning

Principles of Knowledge Representation and Reasoning: Proceedings of the 9th International Conference, KR 2004

Volumn , Issue , 2004, Pages 130-139

  Benferhat, Salem ^a   Bonnefon, Jean François ^b   Da Silva Neves, Rui ^b  

^a UNIVERSITÉ D'ARTOIS   (France)

^b UNIVERSITÉ TOULOUSE JEAN JAURÈS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

CONSEQUENCE RELATIONS; DEFAULT REASONING; DEFAULT RULE; EXPERIMENTAL ANALYSIS; EXPERIMENTAL TEST; HUMAN REASONING; NONMONOTONIC; POSSIBILISTIC; POSSIBILITY THEORY FRAMEWORK; PROPERTY;

UNCERTAINTY ANALYSIS;

EID: 45149132976     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Adams, E. W. (1975) The logic of conditionals. Reidel, Dordrecht.
2. Benferhat S., Bonnefon J. F., and Da Silva Neves R. (2004). An overview of possibilistic handling of default reasoning: applications and empirical studies. To appear in Synthese. A short version appears in 1st Salzburg workshop on Paradigms of Cognition July 18-20, 2002.
3. Benferhat S., Cayrol C., Dubois D., Lang J., Prade H. (1993). Inconsistency management and prioritized syntax-based entailment. Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI'93) Chambéry, France, pp, 640-645.
4. Benferhat S., Dubois D., Prade H. (1997). Nonmonotonic reasoning, conditional objects and possibility theory. Artificial Intelligence Journal, 92, pp. 259-276, 1997.
5. Benferhat S., Saffiotti A., Smets P.(2000) Belief functions and default reasoning. Artificial Intelligence Journal, vol. 122/1-2, 2000, pp. 1-69.
6. Benferhat S., Kaci S., Le Berre D., Williams M. A. (2001b). Weakening conflicting information for iterated revision and knowledge integration. Proc. of the 17th Intern. Joint Conf. on Artificial Intelligence IJCAI01, p. 109-115, 2001.
7. Da Silva Neves R.M., Bonnefon J.F. and Raufaste E. (2002). An empirical test of patterns for nonmonotonic inference. Annals of Mathematics and Artificial Intelligence, 34: 107-130.
8. Dubois, D., Lang, J. and Prade, H. (1994) Possibilistic logic. In: Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 3 (D.M. Gabbay, C.J. Hogger, J.A. Robinson, D. Nute, eds.), Oxford University Press, 439-513.
9. Didier Dubois, Henri Prade. Conditional objects, possibiliy theory and default rules. Dans: Conditionals: From Philosophy to Computer Sciences. Crocco, G. et Fariñas del Cerro, L. et Herzig, A. (Eds.), Oxford University Press, 301-336, 1995.
10. Elio, R., and Pelletier, F.J. (1993). Human Benchmarks on AI's benchmark problems. In Proc. of the 15th Conference of the Cognitive Science Society, Boulder, CO, pp. 406-411.
11. Gabbay, D. M. (1985) Theoretical foundations for nonmonotonic reasoning in expert systems. In: K.R. Apt (Ed.) Logics and models of Concurrent Systems. Springer Verlag, Berlin, 439-457.
12. Gärdenfors, P. and Makinson D. (1994) Nonmonotonic inference based on expectations. Artificial Intelligence, 65, 197-245.
13. Hewson, C. and Vogel, C. (1994). Psychological evidence for assumptions of path-based inheritance reasoning. In Proc. of the 16th Conference of the Cognitive Science Society, Atlanta, pp. 403-414.
14. Gabriele Kern-Isberner (1991a) Handling Conditionals Adequately in Uncertain Reasoning. Proceedings of 6th European Conference Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2001, pp. 604-615, Lecture Notes in Computer Science 2143 Springer.
15. Gabriele Kern-Isberner (1991b) Representing and Learning Conditional Information in Possibility Theory. Computational Intelligence, Theory and Applications, International Conference, 7th Fuzzy Days, Lecture Notes in Computer Science 2206 Springer, pp. 194-217.
16. Kraus, S., Lehmann, D. and Magidor, M. (1990) Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence 44, 167-207.
17. J. Lang (1996) Conditional desires and utilities - An alternative logical framework for qualitative decision theory. Proceedings of the 12th European Conference on Artificial Intelligence (ECAI'96), 318-322
18. D. Lehman (1995) Another perspective on default reasoning, Annals of Mathematics and Artificial Intelligence: 15(1) (1995) pp. 61-82
19. Lehmann, D. and Magidor, M. (1992) What does a conditional knowledge base entail? Artificial Intelligence, 55, 1-60.
20. Pearl J. (1990) System Z: A natural ordering of defaults with tractable applications to default reasoning. Procs. of Theoretical Aspects of Reasoning about Knowledge, 121-135.
21. Pelletier F.J., and Elio R. (1997). What should reasoning be, by default ? Computational Intelligence, Volume 13, Number 2, pp. 165-187.