Learning to Improve Case Adaptation by Introspective Reasoning and CBR

David B. Leake, Andrew Kinley, and David Wilson. Proceedings of the First International Conference on Case-Based Reasoning, Springer Verlag, Berlin, 1995. 12 pages.

Abstract

In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that may not be available a priori, presenting a serious impediment to endowing CBR systems with the needed adaptation knowledge. This paper describes ongoing research on a method to address this problem by acquiring adaptation knowledge from experience. The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future re-use. We describe the tenets of the approach and the types of knowledge it requires. We sketch initial computer implementation, lessons learned, and open questions for further study.

See http://www.cs.indiana.edu/~leake/INDEX.html for additional publications in the Artificial Intelligence/Cognitive Science report and reprint archive maintained by David Leake.