Knowledge-based handling of design expertise

P. Morizet-Mahoudeaux, Einoshin Suzuki, S. Ohsuga
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Abstract

Research issues in the domain of AI for design can be organized in three categories: decision making, representation and knowledge handling. In the area of knowledge handling, this paper addresses issues concerning the management of design experience to guide a priori the generation of candidate solutions. The approach is based on keeping the trace of a previous design experience as a hierarchical knowledge base. A level in the hierarchy can be viewed as a level of granularity of the description of the design process. A general framework for defining a partial order function between the granularity levels in the knowledge bases of design expertise is proposed. It is then possible to compute the sets of the elements belonging to smaller granularity levels, which are linked to any component of the hierarchy. Thus, it makes it possible to compute the level in the hierarchy that can be reused without modification for the design of a new product. Computation of the appropriate level is mainly based on matching the data corresponding to the new requirements with these sets. The approach has been tested by using a multiple expert systems structure based on using interactively two systems, an expert system development tool for design, KAUS, and an expert system development tool for diagnosing engineering processes, SUPER. The intrinsic properties of SUPER have also been used for improving the design procedure when qualitative and quantitative knowledge is involved.<>
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以知识为基础处理设计专业知识
设计人工智能领域的研究问题可以分为三类:决策、表示和知识处理。在知识处理方面,本文讨论了有关设计经验管理的问题,以指导候选解决方案的先验生成。该方法基于将以前的设计经验作为分层知识库的跟踪。层次结构中的一个级别可以看作是设计过程描述的一个粒度级别。提出了一种用于在设计专业知识知识库的粒度级别之间定义偏序函数的通用框架。然后可以计算属于较小粒度级别的元素集,这些元素与层次结构的任何组件相关联。因此,它可以计算层次结构中的级别,这些级别可以在不修改新产品设计的情况下重用。适当级别的计算主要基于将新需求对应的数据与这些集合进行匹配。该方法已经通过使用基于交互两个系统的多专家系统结构进行了测试,其中一个是用于设计的专家系统开发工具KAUS,另一个是用于诊断工程过程的专家系统开发工具SUPER。当涉及定性和定量知识时,SUPER的内在特性也被用于改进设计过程。
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