汽车诊断专家系统

V. Veluchamy, Y. Hari
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引用次数: 0

摘要

作者概述了一种基于人工智能的专家系统的发展,用于解决汽车故障。该系统建立在概念分层分类的基础上。分层结构中的每个节点代表一个故障假设。知识由前链生成规则表示。规则的组织模仿专家解决问题的方法。使用一种称为“建议-改进”的控制策略,提出一组可能的故障,并根据当前情况对它们进行改进。讨论了判断知识的引入和不确定性管理。系统中嵌入的专业知识是基于从汽车故障排除领域的专家那里获得的判断知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An expert system for automobile car diagnosis
The authors outline the development of an AI-based expert system for trouble-shooting a car. The system is built on the basis of a hierarchical classification of concepts. Each node in the hierarchical structure represents a malfunction hypothesis. The knowledge is represented by forward-chaining production rules. The organization of rules emulates an expert's approach in problem solving. A control strategy termed 'propose-refine' is used that proposes a set of possible malfunctions and refines them depending on the prevailing situation. Incorporation of judgemental knowledge and uncertainty management are discussed. The expertise embedded in the system is based on the judgemental knowledge obtained from the experts in the field of automobile trouble-shooting.<>
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