Possibilistic causality consistency problem based on asymmetrically-valued causal model

Fuzzy Sets and Systems

Volumn 132, Issue 1, 2002, Pages 33-48

  Yamada, Koichi ^a  

^a NAGAOKA UNIVERSITY OF TECHNOLOGY   (Japan)

Author keywords

Causal network; Causal reasoning; Possibility theory; Uncertain reasoning

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; FUZZY SETS; KNOWLEDGE BASED SYSTEMS; PROBABILISTIC LOGICS; PROBABILITY; UNCERTAIN SYSTEMS;

CASUAL REASONING; UNCERTAIN REASONING;

PROBLEM SOLVING;

EID: 0037121227     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(02)00099-4     Document Type: Article

References (24)

1. Cheng P.W. From covariation to causation. a causal power theory Psychol. Rev. 104:1997;367-405.
2. Cooper G.F. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence. 42:1990;393-405.
3. Dagum P., Luby M. Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence. 60:1993;141-153.
4. Dubois D., Grabisch M., De Mouzon O., Prade H. A possibilistic framework for single-fault causal diagnosis under uncertainty. Internat. J. Gen. Systems. 30(2):2001;167-192.
5. Dubois D., Prade H. Possibilistic inference under matrix form. Prade H., Negoita C.V. Fuzzy Logic in Knowledge Engineering. 1986;112-126 Verlag, TÜT Rheinland, Köln.
6. Dubois D., Prade H. Possibility Theory. 1988;Plenum Press, New York.
7. Dubois D., Prade H. Epistemic entrenchment and possibilistic logic. Artificial Intelligence. 50:1991;223-239.
8. Dubois D., Prade H. A survey of belief revision and updating rules in various uncertainty models. Internat. J. Intelligent Systems. 9:1994;61-100.
9. Dubois D., Prade H. Fuzzy relation equations and causal reasoning. Fuzzy Sets and Systems. 75:1995;119-134.
10. Dubois D., Prade H. A synthetic view of belief revision with uncertain inputs in the framework of possibility theory. Internat. J. Approx. Reason. 17:1997;295-324.
11. Glymour C. Learning causes. psychological explanations of causal explanation Minds and Machines. 8:1998;39-60.
12. D. Heckerman, C. Meek, Models and selection criteria for regression and classification, Proc. 13th Conf. on Uncertainty in AI, Providence, 1997, pp. 223-228.
13. Hisdal E. Conditional possibilities independence and non-interaction. Fuzzy Sets and Systems. 1:1978;283-297.
14. J.R. Nail, C.S. Wallace, K.B. Korb, Learning Bayesian networks with restricted causal interactions, Proc. 15th Conf. on Uncertainty in AI, Stockholm, 1999, pp. 486-493.
15. Pearl J. Fusion, propagation, and structuring in belief networks. Artificial Intelligence. 29:1986;241-288.
16. Pearl J. Probabilistic Reasoning in Intelligent Systems. Networks of Plausible Inference:1988;Morgan Kaufmann, London.
17. Peng Y., Reggia J.A. A probabilistic causal model for diagnostic problem solving - Part I: integrating symbolic causal inference with numeric probabilistic inference. IEEE Trans. Systems, Man, Cybernet. SME-17(2):1987;146-162.
18. Peng Y., Reggia J.A. Abductive Inference Models for Diagnostic Problem-Solving. 1990;Springer, Berlin.
19. Yamada K. Conditional causal possibility and its application to inverse problem of causalities. J. Japan Soc. Fuzzy Theory Systems. 11:1999;104-111. (in Japanese).
20. K. Yamada, Causal reasoning using conditional causal possibilities to express uncertainty of causalities, IFSA World Congress 99, Taipei (2) (1999) 665-669.
21. K. Yamada, Causality consistency problem for two different possibilistic causal models, Internat. Symp. on Medical Informatics and Fuzzy Technology, 1999, Hanoi, pp. 182-189.
22. K. Yamada, Possibilistic causality consistency problem with compound causal relationships, The 4th Asian Fuzzy Systems Symposium, Tsukuba, 2000, pp. 89-94.
23. K. Yamada, M. Honda, A method of diagnosis using possibility theory, Proc. IEA-AIE 96, Fukuoka (1996) 149-154.
24. Zadeh L.A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems. 1:1978;3-28.