p-96-03

Linking Adaptation and Similarity Learning

David B. Leake, Andrew Kinley, and David Wilson

Proceedings of the Eighteenth Annual Conference of the Cognitive
Science Society

Abstract

The case-based reasoning (CBR) process solves problems by retrieving
prior solutions and adapting them to fit new circumstances.  Many
studies examine how case-based reasoners learn by storing new cases
and refining the indices used to retrieve cases.  However, little
attention has been given to learning to refine the process for
applying retrieved cases.  This paper describes research investigating
how a case-based reasoner can learn strategies for adapting prior
cases to fit new situations, and how its similarity criteria may be
refined pragmatically to reflect new capabilities for case adaptation.
We begin by highlighting psychological research on the development of
similarity criteria and summarizing our model of case adaptation
learning.  We then discuss initial steps towards pragmatically
refining similarity criteria based on experiences with case



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