Learning And Reasoning Algorithm In Causality Diagram

The aim of Artificial Intelligence research is no more than to simulate the thought of human brain with machine, the thought of human is various, although many thought phenomena are behaved the disposal of certainty information, and more phenomena are behaved various uncertainty, many phenomena in reality world are uncertain. Therefore, the really Artificial Intelligence system has to reflect the uncertainty of human brain and deal with uncertain information immanence. And then, how to represent and deal with the uncertainty of knowledge is the basis of Artificial Intelligence research, it is a puzzle of Artificial Intelligence must be faced with. Dynamic Causality Diagram was first proposed by professor Zhang Qin in 1994, it is a mathematics tool combined with probability and graph theory, just like the Belief Network, its characteristic is to provide the method of uncertain knowledge representation and agility reasoning, it adopts nodes to represent random variables in the domain and directional edges between nodes to represent causal relationship between variables, linkage intensity to represent the strength of the link between these variables, it supports the forms of reasoning from cause to effect and from effect to cause and together. Dynamic Causality Diagram has some advantages compared with Belief Network, it is more convenience to represent causal relationship, and the superiority is taken on especially in the Fault Diagnosing field. Therefore the more researches on Causality Diagram have not only the academic significance but also practical and economical value.This dissertation discusses and studies to surround the knowledge representation, learning, reasoning, and the main contents include:At the first chapter, some familiar uncertain knowledge representation and reasoning and the difficulties of them: Evidential theory, Certainty factor, Fuzzy logic and fuzzy reasoning, Subjective Bayesian method, Belief network are introduced. We present the basic knowledge, primary reasoning algorithm, complexity of reasoning algorithm, the way of dealing with some problem of Causality Diagram relative and the research direction in Causality Diagram theory particular at the second chapter.Owing to the problem of Causality Diagram don't include self-study mechanism at present and the prior knowledge of reasoning is supplied completely by field experts, some methods of learning Causality Diagram's parameters and structure with statistical approach is presented. At first the structure learning algorithm using bayesial analysis...