RIA: Learning Case Adaptation for Case-Base Reasoning

RIA：基于案例推理的学习案例适应

• 批准号: 9409348
• 负责人: David Leake
• 金额: $ 9万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-07-01 至 1997-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9409348&HistoricalAwards=false
• 关键词: RIA; Learning; Case; Adaptation; Base

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.

9409348泄漏 基于案例的推理（CBR）系统根据经验进行推理： 它们通过检索相关的先前案例和执行案例适应知识来解决新问题，这些知识通常由系统开发人员提供，为单个任务手工编码。 编码有效的适应规则的困难被广泛认为是基于案例推理系统发展的一个严重障碍。 本研究通过开发一个自动学习模型来解决这个问题，以提高案例适应性。 该模型学习记忆搜索程序，以实现一般的适应规则。 该模型从学习记忆搜索案例开始，追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的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提取出来，为记忆搜索提供具体的指导。 学习过程对适应性能的影响进行了评估的一系列实验，包括“消融”的研究和人类受试者的适应质量的直接评估。