Case-Match Reduction Through the Integration of Rule-based and Case-based Reasoning Procedures

Huei-Pi (Ruby) Chen and Larry Wilkinson

Case-based reasoning utilizes past experiences as a key data resource for future problem solving and is considered an innovative technique in the development of Artificial Intelligence. However, the large storage requirements needed for such procedures result in a heavy load in the ease base and indeed, problems of efficiency arise in case retrieval. The proposed method aims to improve case retrieval through reducing the case memory load. The authors propose a cognitive notion as a framework for reducing case match. Using a cognition framework, objective knowledge is represented in the rule-based part of the systems and subjective knowledge in the case-based reasoning part. In this way, the comparisons in case match can be significantly reduced. A loan authorisation system, ECLAS, demonstrates the feasibility of the cognitive framework, the significance both of the efficiency of system performance and the reduction in the calculation of the case matches.

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