Using consistency and abduction based indices in possibilistic causal diagnosis

• DOI: 10.1109/fuzzy.2000.839122
• Publication Date: 2002-11-07T15:11:41Z
• Authors: O. De Mouzon; D. Dubois; H. Prade
• Keywords: Fuzzy sets; Knowledge representation; Statistics; Uncertainty; 0202 electrical engineering, electronic engineering, information engineering; Image sensors; Engines; 02 engineering and technology; Sensor phenomena and characterization; Fault diagnosis; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

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