Becoming an Expert Case-based Reasoner: Learning to Adapt Prior Cases

Experience plays an important role in the development of human expertise. One computational model of how experience affects expertise is provided by research on casebased reasoning, which examines how stored cases encapsulating traces of specific prior problem-solving episodes can be retrieved and re-applied to facilitate new problemsolving. Much progress has been made in methods for accessing relevant cases, and case-based reasoning is receiving wide acceptance both as a technology for developing intelligent systems and as a cognitive model of a human reasoning process. However, one important aspect of casebased reasoning remains poorly understood: the process by which retrieved cases are adapted to fit new situations. The difficulty of encoding effective adaptation rules by hand is widely recognized as a serious impediment to the development of fully autonomous case-based reasoning systems. Consequently, an important question is how casebased reasoning systems might learn to improve their expertise at case adaptation. We present a framework for acquiring this expertise by using a combination of general adaptation rules, introspective reasoning, and case-based reasoning about the case adaptation task itself.