学术
摘 要:
针对多Agent影响图不能建模动态环境和多Agent马尔可夫决策过程难以表示Agents之间结构关系的问题，提出一种新决策模型——多Agent动态影响图（MADIDs）．为了能有效地对MADIDs进行推理，提出一种扩展的BK（EBK）近似推理算法，其扩展体现在三个方面：在BK算法中加入效用结点的边际化操作，加入分割团来减小BK算法的推理误差，使用MADIDs分层分解所生成的联合树来降低推理的复杂性．在模型实例上的实验结果显示了MADIDs模型和EBK算法的有效性。[著者文摘]
关 键 词:
文章出处:
《计算机学报》-2008年31卷2期 -236-244页
栏目信息:
分 类 号:
Research on Multi-Agent Dynamic Influence Diagrams and Its Approximate Inference Algorithm
YAO Hong-Liang WANG Hao ZHANG You-Sheng WANG Rong-Gui （Department of Computer Science and Technology, Hefei University of Technology, Hefei 230009）
Abstract:
As MAIDs represent structural can not model dynamic environment and Multi-Agent MDPs are difficult to relations among Agents, Multi-Agent dynamic influences diagrams （MADIDs） are presented for modeling structural relations of Multi-Agent system in dynamic environment. For efficiently inferring in MADIDs, an extensional BK （EBK） algorithm is proposed, and the extensions are in three aspects. The marginalizing operation of utility nodes is added in BK algorithm the separators are added for decreasing the error of inference the junction tree that obtained by hierarchical decomposition of MADIDs is used for improving the efficiency of inference. Given model instances, the experiment results show the validity of MADIDs and EBK algorithm.[著者文摘]
Key words:
multi-Agent influence diagrams; multi-Agent dynamic influence diagrams; junction tree; BK algorithm
基金资助:
本课题得到国家自然科学基金（60575023）、教育部博士点基金（20050359012）和安徽省自然科学基金（0704120640）资助.
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