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Integrating case-based and rule-based reasoning in knowledge-based systems development.

机译：将基于案例和基于规则的推理集成到基于知识的系统开发中。

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Case-based reasoning (CBR) and rule-based reasoning (RBR) are two paradigms for building knowledge-based systems. They represent distinct approaches to knowledge-based systems development and distinct cognitive models of human problem solving. To date, they have been viewed as competing, rather than complementary, paradigms. This research shows that combining CBR with RBR leads to a stronger approach to knowledge-based systems development.; The research approach is to construct, compare and contrast two expert systems, one case-based and one rule-based, to perform the same task. While claims have been made as to the relative advantages of each approach, this is the first systematic comparison of independent CBR and RBR systems built in the same domain. Strengths and weaknesses of each system are identified, and the best of both systems are combined in a hybrid system.; The domain of study is nutritional menu planning. This domain: (1) presents an AI challenge. Human experts consistently outperform computer systems in planning nutritious and appetizing menus. (2) poses a difficult problem. Unsuccessful attempts to build computer-assisted menu planners date back thirty years. (3) has supportive experts available to assist with system construction and evaluation.; Contributions of the dissertation are: (1) a new approach to CBR/RBR hybridization, in which cases contribute toward constraint satisfaction and rules contribute toward achievement of personal preference goals; (2) a new CBR metric for identifying and retrieving reusable cases, based on ease of adaptation; (3) a public domain nutritional menu planning system, accessible via the World Wide Web; (4) a framework for building special purpose therapeutic menu planning systems for use in the prevention and treatment of disease; (5) a case base of menus meeting guidelines for sound nutrition and aesthetic standards for color, texture, temperature and taste; (6) a new adaptation strategy for adjusting serving sizes in menus; (7) a deeper understanding of the distinctions between CBR and RBR and the relative strengths and weaknesses of the two paradigms.
机译：基于案例的推理（CBR）和基于规则的推理（RBR）是用于构建基于知识的系统的两个范例。它们代表了基于知识的系统开发的独特方法以及解决人类问题的独特认知模型。迄今为止，它们被视为竞争而非互补的范式。这项研究表明，将CBR与RBR结合使用可以为基于知识的系统开发提供更强大的方法。研究方法是构造，比较和对比两个专家系统，一个基于案例，一个基于规则，以执行相同的任务。尽管已经就每种方法的相对优势提出了要求，但这是在相同域中构建的独立CBR和RBR系统的首次系统比较。确定每个系统的优点和缺点，并将两个系统的优点结合在一个混合系统中。研究领域是营养菜单计划。此领域：（1）提出了AI挑战。人类专家在计划营养丰富且令人垂涎的菜单方面始终胜过计算机系统。 （2）提出了一个难题。建立计算机辅助菜单计划者的尝试失败可以追溯到三十年前。 （3）有支持专家可以协助系统构建和评估。论文的主要贡献是：（1）一种新的CBR / RBR杂交方法，在这种情况下，约束约束满足，规则对个人偏好目标的实现具有贡献。 （2）一种新的CBR度量标准，用于基于适应的难易程度来识别和检索可重用的案例； （3）可通过万维网访问的公共领域营养菜单计划系统； （4）建立用于预防和治疗疾病的特殊目的治疗菜单计划系统的框架； （5）符合基本营养，色泽，质地，温度和口味审美标准准则的菜单案例库； （6）调整菜单中份量的新适应策略； （7）对CBR和RBR之间的区别以及这两种范式的相对优缺点有更深入的了解。

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