An Interactive Case-Based Reasoning Model and Its Application to Supporting Machine Adjustment

Abstract

The deductive reasoning (DR), mainly used in expert systems, requires complete and consistent knowledge base. This brings about the difficult problem of knowledge acquisition. For supplementing DR, it is useful to introduce case-based reasoning (CBR) that explicitly uses past cases which are the expert's experiences with success/failure results. This paper clarifies the roles and problems of the domain knowledge used in CBR, and proposes an interactive model of CBR for flexible user's interaction with CBR's process under the incomplete domain knowledge. Based on this model, an interactive CBR system was implemented. This consists of the following functions : problem indexing, case retrieval, case evaluation, case modification, and case repair. An application of this system to a problem of machine adjustment has shown that even if the user cannot find useful adjustment method by the existing approach, the system can present an appropriate method plan.