知识表示的超拓扑结构研究

On Hyper-Topological Structure for Knowledge Representation(HTSKR)

针对 90年代以来人工智能 (AI)研究逐步转入低潮的局面 ,提出了关于 AI的新理论 ,主要研究了知识表示的超拓扑结构 (HTSKR)。认识到超拓扑结构 (HTS)是客观世界中事物的普遍现象 ,抽象出 HTS=HN+HL。提出了 HTS的状态空间与解路径 ,定义了解路径的承接超结点。对HTSKR进行了形式化的定义 ,研究了超结点的包容性、包容度、连接性、命名性、分类性等。论述了新的知识表示方法—— HTSKR,其优点是包容度更大 。

In this paper,we for the first time present a new method——hyper topological structure(HTS) for knowledge representation in artificial intelligence(AI). Our main contributions are as follows:① We realized that HTS is common in almost everything; ② We generalized that HTS consisted of hyper node(HN) and hyper line(HL); ③ We defined the state space of HTS, and its solution path; ④ We studied the comprehensiveness, compatibility, connective capacity, namability, and the classification of HN. Our new method greatly extended the space of knowledge representation, and at the same time it is compatible with the existing methods for knowledge representation.