RIA: Learning Case Adaptation for Case-Base Reasoning

9409348李克案例推理系统根据经验进行推理：它们通过检索相关先前案例并执行案例改编知识来解决新问题，通常由系统开发人员为单一任务手工编码。编码有效的适应规则的困难被广泛认为是基于案例的推理系统发展的一个严重障碍。这项研究通过开发一个自动学习模型来解决这个问题，以改善案例适应。该模型学习记忆搜索过程以实现一般的适应规则。该模型从学习记忆搜索案例中的结果开始，跟踪所使用的记忆搜索过程及其结果。这些记忆搜索案例反映了特定记忆搜索策略对特定适应的适用性，学习的记忆搜索案例被检索以提供特定的记忆搜索指导。学习过程对适应表现的影响是通过一系列实验来评估的，包括“消融”研究和由受试者直接评估适应的质量。

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.