Multistrategy Learning to Apply Cases for Case-Based Reasoning

David B. Leake, Andrew Kinley, David Wilson

Investigations of learning in case-based reasoning (CBR) have traditionally focused on learning two types of knowledge: new cases and new indexing criteria for case retrieval. However, there is increasing recognition that other types of knowledge also play crucial roles in the case-based reasoning process. The effectiveness of a CBR system depends not only on having and retrieving relevant cases, but also on selecting which retrieved cases to apply and determining how to adapt them to fit new situations. Consequently, case-based reasoning can benefit from using multiple learning strategies to acquire, in addition to new cases and indices, new case adaptation strategies and similarity c~iteda. This paper describes ongoing research that studies how multiple types of learning can improve the case-based reasoning process and e-amines their interrelationship in contributing to the overall performance of a CBR system.


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