Using consistency and abduction based indices in possibilistic causal diagnosis

Olivier de Mouzon (1) , Didier Dubois (1) , Henri Prade (1)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

Causal diagnosis deals with the search for plausible causes which may have produced observed effects. Knowledge about possible effects of a malfunction on a given attribute is represented by a possibility distribution, as well as the possible values of an observed attribute (giving the imprecision of the observation). Any kind of attributes (binary, numerical, etc.) is allowed. In this paper, we restrict to single-fault diagnosis. Two main indices, respectively based on consistency and on abduction, enable one to discriminate the malfunctions. The case where one deals with imprecise information only is first discussed and exemplified. The extension to information pervaded with uncertainty is then studied. Refinements of indices are also considered.

Mots clés: Fuzzy sets; Uncertainty; Sensor phenomena and characterization; Engines; Fault diagnosis; Image sensors; Knowledge representation; Statistics

Domaines: Intelligence artificielle [cs.AI]

Olivier de Mouzon, Didier Dubois, Henri Prade. Using consistency and abduction based indices in possibilistic causal diagnosis. 9th IEEE International Conference on Fuzzy Systems (FUZZ- IEEE 2000), May 2000, San Antonio (Texas), United States. pp.729-734, ⟨10.1109/FUZZY.2000.839122⟩. ⟨hal-03403394⟩