ABSTRACT
Case-based reasoning research on indexing and retrieval focuses primarily on developing specific retrieval criteria, rather than on developing mechanisms by which such criteria can be learned as needed. This paper presents a framework for learning to refine indexing criteria by introspective reasoning. In our approach, a self-model of desired system performance is used to determine when and how to refine retrieval criteria. We describe the advantages of this approach for focusing learning on useful information even in the absence of explicit processing failures, and support its benefits with experimental results on how an implementation of the model affects performance of a case-based planning system.
