Development of expert system tools with knowledge compilation function based on deep knowledge

基于深度知识的具有知识编译功能的专家系统工具开发

基本信息

批准号：
03452290
负责人：
MIZOGUCHI Riichiro
金额：
$ 0.45万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1991
资助国家：
日本
起止时间：
1991 至 1992
项目状态：
已结题

项目摘要

The major objective of this research is to develop innovative knowledge processing techniques which contribute to overcoming the limitation of the current technology such as brittleness and lack of adaptability. Human experts have fundamental knowledge and domain principles(deep knowledge) in addition to heuristics(shallow knowledge) which is used in solving ordinary problems. When they are given unfamiliar problems they cannot solve by using heuristics, they try to solve it by using above mentioned deep knowledge. Based on this observation, the investigator conducted the research on deep knowledge-based expert system and knowledge compilation of shallow knowledge. One of the main results obtained is a knowledge compilation system based on five kinds of deep knowledge for diagnostic expert systems. A prototype systems has been successfully implemented in Common ESP on Sparkstation.This research has a tight relation to model-based reasoning which is a research on environment of the behavior of qualitatively modeled dynamic systems. One of the shortcomings of the qualitative reasoning is ambiguity of the inference which prevents the method to be used in practical problems. Another objective of this research is to develop a new qualitative reasoning method for decreasing the ambiguity. A modeling method and inference method have been designed for cooling subsystem of a nuclear power plant.The third objective of this research is to develop a methodology for building reusable knowledge base. Although not completed yet, a first version of the methodology has been designed for the knowledge base for restoration of substations.

这项研究的主要目的是开发创新的知识处理技术，这有助于克服当前技术的限制，例如脆性和缺乏适应性。除了启发式知识（浅知识）外，人类专家还具有基本知识和领域原则（深度知识），该原则用于解决普通问题。当他们有陌生的问题时，他们无法通过使用启发式方法来解决，他们会尝试通过使用上述深度知识来解决它。基于这一观察结果，研究者对基于知识的深度专家系统和浅层知识的知识进行了研究。获得的主要结果之一是基于诊断专家系统的五种深入知识的知识汇编系统。原型系统已在SparkStation上成功实施。这项研究与基于模型的推理有着密切的关系，这是对定性建模动态系统行为环境的研究。定性推理的缺点之一是推论的歧义，这种推论阻止了用于实际问题的方法。这项研究的另一个目的是开发一种新的定性推理方法来减少歧义。已经设计了一种建模方法和推理方法，用于核电站的冷却子系统。这项研究的第三个目标是开发一种建立可重复使用的知识库的方法。尽管尚未完成，但该方法的第一版是为恢复变电站的知识库而设计的。