Evidential reasoning in expert systems
The Dempster-Shafer (D-S) theory of evidence has attracted much attention in the AI community in recent years because it suggests a coherent approach, which is sometimes called evidential reasoning, to aggregate evidence bearing on hypothesis groups in expert systems. However, there are two major difficulties in applications of the theory: (1) the certainty degrees of rules are difficult to represent; (2) the theory can not handle evidence bearing on vague concepts. To overcome these difficulties, two extensions to the D-S theory for its application to reasoning in expert systems were made. First, The multivalued mapping in the D-S theory is extended to a probabilistic one to represent rules' certainty degrees. Dempster's rule is then modified to combine belief update rather than absolute belief. The result is consistent with Bayes' theorem and is justified with conditional independence assumptions that are weaker than those of PROSPECTOR and MYCIN. In the second extension, the D-S theory is formulated as an optimization problem where a basic probability assignment (bpa) constrains the underlying probability distributions. First, the probabilistic constraints imposed by a fuzzy bpa are defined and upper probabilities of a fuzzy set are then obtained by maximizing or minimizing probability of the set. The optimization problem is partitioned into subproblems, which are solved using a linear time algorithm.
- Research Organization:
- California Univ., Berkeley (USA)
- OSTI ID:
- 5701123
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
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