Abstract
Case-based reasoning is a technique recently developed to alleviate limitations of the rule-based expert systems. Instead of relying solely on rules, a case-based system maintains old cases in a case base. When a new problem is encountered, the system retrieves similar cases from the case base and constructs a solution to the new problem based on existing solutions. A key issue in case-based reasoning is how to index and retrieve similar cases. In this paper, we present a new approach that integrates fuzzy set concepts into the case indexing and retrieval process. This approach has a few advantages over existing methods. First, it allows numerical features to be converted into fuzzy terms to simplify the matching process. Second, it allows cases in different domains to be comparable. Finally, it allows greater flexibility in the retrieval of candidate cases.
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