Learning More from Experience in Case-Based Reasoning

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Recent concerns about the effects of feedback delays on solution quality in case-based reasoning (CBR) have prompted research interest in feedback propagation as an approach to addressing the problem. We argue in this paper that the ability of CBR systems to learn from experience in the absence of immediate feedback is limited by eager commitment to the adaptation paths used to solve previous problems. Moreover, it is this departure from lazy learning in CBR that creates the need for maintenance interventions such as feedback propagation. We also show that adaptation path length has no direct effect on solution quality in many adaptation methods and examine the implications for problem solving and learning in CBR. For such “path invariant” adaptation methods, we demonstrate the effectiveness of a “lazier” approach to learning/problem solving in CBR that avoids commitment to previous adaptation paths and hence the need for feedback propagation.