9409348 Leake Case-based reasoning (CBR) systems reason from experience: They solve new problems by retrieving relevant prior cases and performing case adaptation knowledge is normally provided by the system developer, hand-coded for a single task. The difficulty of encoding effective adaptation rules is widely recognized as a serious impediment to the development of case-based reasoning systems. This research addresses that problem by developing a model of automatic learning to improve case adaptation. The model learns memory search procedures to operationalize general adaptation rules. The model starts results in learning memory search cases tracing the memory search processes used and their results. Those memory search cases reflect the applicability of particular memory search strategies to particular adaption, learned memory search cases are retrieved to provide specific guidance for memory search. The effects of the learning process on adaptation performance are evaluated by a series of experiments including "ablation" studies and direct assessment of the quality of the adaptations by human subjects.
