The Research On Theory And Algorithm Of Uncertainty Reasoning Based On 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: To state the basic knowledge, primary algorithm and the way of dealing with some problem of Causality Diagram relative particular, after introducing some familiar uncertain knowledge representation and reasoning and the difficulties of them: Evidential theory, Certainty factor, Fuzzy logic and fuzzy reasoning, Rough set theory, Subjective Bayesian method, Belief network. 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, a method of learning Causality Diagram's parameters with statistical approach is presented. It includes the discrete Causality Diagram's parameters learning algorithms: the mathematical expectation of posterior distribution---conditional expectation estimation when the data is complete, and analogous EM algorithm when the data is incomplete, as well as learning correlative degree method with information entropy, and the experiment results show that the algorithms are effective and feasible; Learning Causality Diagram's parameters online algorithm adopt with EM algorithm that contains parameter---EM(η), which makes the leaned parameters can adapt to circumstance, its superiority and difference with offline parameters learning are explained, and its correctness is proved in theory; Learning the continuous Causality Diagram's parameters is presented by classical statistical approaches: parameter estimation, non-parametric estimation, semiparametric estimation; an approach of learning Causality Diagram's structure is present. It solves the knowledge acquisition key problem of Causality Diagram preferably, it is important for abundance Causality Diagram theory and application of Causality Diagram. An algorithm that how converts Causality Diagram into Belief Network is presented, because the Belief Network has many ready reasoning algorithms and applied software. It includes the linkage intensity of Causality Diagram transfer to CPT of Belief Network, and the structure of Causality Diagram transfer to the structure of Belief Network. The channel of solving problem is developed because the model represented by Causality Diagram can be solved by Belief Network model. Production rule knowledge representation is popular and common, but there are many shortages to represent knowledge and reasoning, according to the relation of production rule and Causality Diagram, discuss the method and course are proposed in this paper to convert production rule set into Causality Diagram, correspond to give a method of knowledge acquisition based on Causality Diagram, and give an example to this conversion. An approximate reasoning algorithm that adopt max and min operator based on Causality Diagram's characteristic and matrix transform has been presented to improve the deficiency of logic operation complexity and computation complexity, it can raise the computation velocity in Causality Diagram reasoning. Owing to the occurrence probability of event shows fuzzy and random, the fuzzy number is inducted into the causality diagram in this paper, with the fuzzy number replace the probabilities of basic events and linkage events, and it can solve the difficulty of obtaining the precision probability value as well as solve the problem of the fuzzy and random of the occurrence probability of event. This method was applied to the fault diagnosis of stow machine and pressure vessel.