Proceedings of the Annual Meeting of the Cognitive Science Society

The case-based reasoning (CBR) process solves problems by retrieving prior solutions and adapting them to fit new circumstances. Many studies examine how casebased 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 adaptation.