基于案例的推理系统可靠性研究

Ke Wang, J. Liu, Weimin Ma
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引用次数: 2

摘要

基于案例的推理(Case-based reasoning, CBR)是一种解决问题的方法,它基于先前解决的问题及其关联的解决方案提出新问题的解决方案。这种方法中的一个关键问题是,我们能否始终信任基于案例的推理系统提出的解决方案?本文首先从整体层面对CBR系统的可靠性进行了研究。本节将讨论影响CBR系统可靠性的因素,特别是其案例库是否符合“类似的问题有类似的解决方案”的基本假设。然后,研究了个体建议方案的可靠性。本节将比较一些现有的可用于估计单个解的可靠性的方法。为了说明这些观点,本文还讨论了一些实验和结果。研究表明,如果案例库具有较高的兼容性,则可以获得满意的结果,并且可以通过确定可靠的解决方案来提高CBR系统在整体层面的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on the Reliability of Case-Based Reasoning Systems
Case-based reasoning (CBR) is a methodology for problem solving, which suggests a solution to a new problem based on the previously-solved problems and their associated solutions. A key issue in this methodology is that can we always trust the solutions suggested by a case-based reasoning system? This paper studies the reliability of CBR systems at an overall level first. Factors affecting the reliability of a CBR system are discussed in this section, especially the property that whether its case library is compatible with the foundational assumption that "similar problems have similar solutions." After that, the reliability of an individual suggested solution is studied. Some existing approaches which can be employed to estimate the reliability of a single solution are compared in this section. To illustrate these ideas, some experiments and their results are also discussed in this paper. It is shown that if a case library attains a high compatibility, then a satisfactory result can be expected, and the reliability of a CBR system at an overall level can be improved by identifying the reliable solutions.
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