Proceedings:
AI in Equipment Maintenance and Support
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Papers from the 1999 AAAI Spring Symposium
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Abstract:
In this paper we investigate how the observation of symptoms which do not completely match a modeled fault can be used to find the most likely fault and the degree to which this fault occurs. We start out by setting up fuzzy causal diagrams and then show how with the use of a proper operator the arcs of the causal diagram can be reversed. We introduce a graphical representation for fuzzy belief nets (FBN) and show how both AND and OR connected antecedents and consequents of rules can be accommodated. The paper concludes with an illustrative diagnostic example.
Spring
Papers from the 1999 AAAI Spring Symposium