p-96-02

Multistrategy Learning to Apply Cases for Case-Based
Reasoning

David B. Leake, Andrew Kinley, and David Wilson

Proceedings of the Third International Workshop on
Multistrategy Learning

Abstract

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 criteria.  This paper describes ongoing
research that studies how multiple types of learning can improve the
case-based reasoning process and examines their interrelationship in
contributing to the overall performance of a CBR system.



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