Learning Bayesian Network Structures And Research On Model Of Multi-Agent System

Bayesian technology and Bayesian networks are used to process uncertain problems of Artificial Intelligence domain. Bayesian technology is incorporated in Agent technology, so a very promising new domain is formed.Bayesian theory and Bayesian models are system introduced in the thesis. It is a hotspot that Learning Bayesian network structures based on data driven. The GAMDL Algorithms is developed which is based on Genetic Algorithms and Minimum Description Length principle. The advantages of this learning algorithm are that it can efficiently void over-fitting, solve NP-hard problem, and learning process has adaptability. The algorithm has efficient learning performance.Dynamic Bayesian Networks (DBNs) are a powerful methodology for representing and computing with uncertain problem of stochastic processes. Influence diagrams are a useful tool that it is used to deal with decision problem. Actions of three or more Agents are modeled by combining Agent technology with Bayesian theory. An approach of decomposition and incorporation is developed to resolve the problem which is intractable for exact calculations about multi-agent system based on Dynamic Bayesian networks. The approach improves ability of model representing.