Abstract
This paper introduces fuzzy belief nets (FBN). The ability to invert arcs between nodes is key to solving belief nets. The inversion is accomplished by defining closeness measures which allow diagnostic reasoning from observed symptoms to cause of failures. The closeness measures are motivated by a Lukasiewicz operator which takes into account the distance from an observed symptom set to the modeled symptom set for all failure combinations. Hypothesized failures are then ranked according to maximum closeness measure and minimum cover, i.e., number of faults. Within the realm of fuzzy logic we show the graphical representation and solution of fuzzy belief nets.
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