A new justification of the unnormalized dempster's rule of combination from the least commitment principle

Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21

Volumn , Issue , 2008, Pages 666-671

  Pichon, Frédéric ^a,b   Denœux, Thierry ^a  

^a UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

^b THALES RESEARCH AND TECHNOLOGY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FUNCTION EVALUATION;

BELIEF FUNCTIONS; DEMPSTER'S RULES; NEUTRAL ELEMENTS; WEIGHT FUNCTIONS;

PROBABILITY DENSITY FUNCTION;

EID: 55849113663     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Denœux, T. 2008. Conjunctive and disjunctive combination of belief functions induced by non distinct bodies of evidence. Artificial Intelligence 172:234-264.
2. Destercke, S.; Dubois, D.; and Chojnacki, E. 2007. Cautious conjunctive merging of belief functions. In Mellouli, K., ed., Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2007), 332-343. Hammamet, Tunisia: Springer Verlag.
3. Dubois, D., and Prade, H. 1986a. On the unicity of dempster rule of combination. International Journal of Intelligent Systems 1:133-142.
4. Dubois, D., and Prade, H. 1986b. A set-theoretic view of belief functions: logical operations and approximations by fuzzy sets. International Journal of General Systems 12:193-226.
5. Dubois, D.; Prade, H.; and Smets, P. 2001. New semantics for quantitative possibility theory. In Benferhat, S., and Besnard, P., eds., Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2001), 410-421. Toulouse, France: Springer Verlag.
6. Klawonn, F., and Smets, P. 1992. The dynammic of belief in the transferable belief model and specialization-generalization matrices. In Dubois, D.; Wellman, M. P.; D'Ambrosio, B.; and Smets, P., eds., Uncertainty in Artificial Intelligence 92, 130-137. Morgan Kaufman, San Mateo, Ca.
7. Klement, E. P.; Mesiar, R.; and Pap, E. 2000. Triangular norms. Kluwer Academic Publishers, Dordrecht.
8. Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton Univ. Press. Princeton, NJ.
9. Shenoy, P. P., and Shafer, G. 1990. Axioms for probability and belief-function propagation. In Shafer, G., and Pearl, J., eds., Readings in uncertain reasoning. San Mateo, CA: Morgan Kaufmann. 575-610.
10. Smets, P., and Kennes, R. 1994. The transferable belief model. Artificial Intelligence 66:191-234.
11. Smets, P. 1993. Belief functions: the disjunctive rule of combination and the generalized bayesian theorem. International Journal of Approximate Reasoning 9:1-35.
12. Smets, P. 1995. The canonical decomposition of a weighted belief. In Int. Joint Conference on Artificial Intelligence, 1896-1901. San Mateo, Ca: Morgan Kaufmann.
13. Smets, P. 1998. The transferable belief model for quantified belief representation. In Gabbay, D. M., and Smets, P., eds., Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol. I. Doordrecht, The Netherlands: Kluwer. 267-301.
14. Yager, R. R., and Rybalov, A. 1996. Uninorm aggregation operators. Fuzzy Sets and Systems 80:111-120.
15. Yager, R. R. 1986. The entailment principle for dempstershafer granules. International Journal of Intelligent Systems 1:247-262.