Learning to Improve Case Adaptation
by Introspective Reasoning and CBR*

David B. Leake, Andrew Kinley, and David Wilson

Computer Science Department
Lindley Hall 215, Indiana University
Bloomington, IN 47405, U.S.A.



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 reuse. 
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.
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