Learning Case Adaptation :: Technology Case-Based Reasoning Essays

Learning Case AdaptationComputer models of mooring-based reasoning (CBR) generally pull in shield allowance use a fixed set of fitting rules. A difficult practical caper is how to identify the knowledge ask to guide reading for particular tasks. Likewise, an open issue for CBR as a cognitive model is how showcase adaptation knowledge is learned. We see a new approach to acquiring case adaptation knowledge. In this approach, adaptation problems ar initially solved by reasoning from scratch, using abstract rules about structural transformations and general stock search heuristics. Traces of the touch used for successful rule-based adaptation ar stored as cases to enable early adaptation to be done by case-based reasoning. When similar adaptation problems ar encountered in the future, these adaptation cases provide task- and athletic field- particular guidance for the case adaptation process. We get the tenets of the approach concerning the relationship between memory search and case adaptation, the memory search process, and the storage and reuse of cases representing adaptation episodes. These points are discussed in the mount of ongoing research on DIAL, a computer model that learns case adaptation knowledge for case-based disaster response planning. 1 Introduction The constitutional principle of case-based reasoning (CBR) for problem-solving is that new problems are addressed by retrieving stored records of foregoing problem-solving episodes and adapting their solutions to fit new situations. In most case-based reasoning systems, the case adaptation process is guided by fixed case adaptation rules. hard-nosed experience developing CBR systems has shown that it is difficult to establish appropriate case adaptation rules (e.g., Allemang, 1993 Leake, 1994). In defining adaptation rules, a key problem is the classic operationality/generality tradeoff that was first observed in research on explanation-based learning (e.g., Segre, 1987) Specif ic rules are easy to apply and are reliable, provided only apply to a narrow range of adaptation problems abstract rules span a broad range of potential adaptations but are often hard and expensive to apply because they do non provide task- and domain-specific guidance. In those CBR systems that do perform case adaptation, specific rules are often used, requiring that the developer perform difficult analysis of the task and domain to determine which rules will be needed. In practice, the problems of defining adaptation rules are so acute that many CBR applications simply omit case adaptation (e.g., Barletta, 1994). This paper presents a new method by which a case-based reasoning system can learn adaptation knowledge from experience.