Bayesian estimation vs fuzzy logic for heuristic reasoning

In this paper, Bayesian estimation theory and fuzzy logic are used to derive knowledge combination rules for heuristic search algorithms. Such algorithms are typically used with expert systems. Heuristics are used to select candidate solutions or partial solutions of a complex problem whose solution space is too large to be fully explored. The information coming from the various heuristics and from observations made during the search must be combined. Combination and decision rules are first derived based on a probabilistic approach. Then, a fuzzy logic approach is followed and compared with the first approach.