Abstract：MultiAgent dynamic influence diagrams (MADIDs) are presented by extending MultiAgent Influence Diagrams (MAIDs) over time. Thus the structural relationships of coordination can be represented in dynamic environment. With the guidance of the strategic relevance, a decomposition approximation method of probability distribution and the approximation of probability distribution in inference are discussed to compute the probability distribution of MADIDs efficiently. The complexity, inducing error and error propagation over time are analyzed. Furthermore, based on the KLdivergence, a function is introduced to establish equilibrium between the precision and the complexity of approximate distribution. Finally, the experimental results on a dynamic coordination model show the validity of the probability distribution approximation method.
姚宏亮，王浩，张佑生，俞奎. 多Agent动态影响图及其概率分布的近似方法*[J]. 模式识别与人工智能, 2007, 20(4): 525-532.
YAO HongLiang, WANG Hao, ZHANG YouSheng, YU Kui. MultiAgent Dynamic Influence Diagrams and Its Approximation of Probability Distribution. , 2007, 20(4): 525-532.
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