基于因果图的一种知识获取方法  

A Method of Knowledge Acquisition Based on Causality Diagram

作　　者：王洪春[1] 

机构地区：[1]重庆大学自动化学院

出　　处：《计算机仿真》2006年第3期126-128,共3页Computer Simulation

基　　金：国家高等学校博士点专项基金(99061116);重庆市科技攻关项目(5990)资助

摘　　要：产生式规则和因果图是知识表示的两种方法,鉴于产生式规则在表达知识和推理方面的缺陷或不足,因此寻找一种能更好地表达知识和推理的方法非常必要,而因果图具有表达知识直观,推理灵活、方便等特点。论文根据模糊式产生式规则与因果图,以及合成式模糊产生式规则与含与门、或门的因果图的对应关系,给出了将模糊产生式规则集表示的知识转换成更紧凑、直观因果图表示的方法和过程,相应的也得到了一个因果图知识的获取方法,并给了一个其转换的实例。Production rule and Causality Diagram are two methods for representing knowledge. Because using production rule to represent knowledge and reason has many shortages, it is necessary to find a method that can represent knowledge and reason better than production rule. The Causality Diagram takes on many characteristic such as intuitional knowledge representation, flexible reasoning and convenience. In this paper, according to the relationship between production rule and Causality Diagram, a method is proposed to convert the production rule set into Causality Diagram, a method of knowledge acquisition based on Causality Diagram, is discussed and an example for this conversion is given.

关 键 词：因果图 模糊式产生式规则 知识表示 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]