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多Agent动态影响图及其概率分布的近似方法*

, PP. 525-532

Keywords: 多Agent动态影响图(MADIDs),KL差分,联合树,扩展BK(EBK)算法

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Abstract:

将多Agent影响图(MAIDs)在时间上进行扩展,提出一种决策模型:多Agent动态影响图(MADIDs),用于表示动态环境中多Agent协作的结构关系.为了有效计算MADIDs的概率分布,以Agents之间的策略偏序关系为指导,给出概率分布的一种分解近似方法,进而讨论概率分布在推理中的近似.对MADIDs概率分布计算的复杂性、误差以及误差在时间上的传播进行分析,进而基于KL差分,给出一个可对近似分布的精度和复杂性进行均衡的函数.最后,针对一个表示协作关系的MADID模型,进行实验和算法比较,实验结果显示该概率分布近似方法的有效性.

References

[1]  Howard R A, Matheson J E. Influence Diagrams. Readings on the Principles and Applications of Decision Analysis, 1984, 11(2): 719762
[2]  Koller D, Milch B. MultiAgent Influence Diagrams for Representing and Solving Games. Games and Economic Behavior, 2003, 45(1): 181221
[3]  Gal Y, Pfeffer A. A Language for Modeling Agents Decision Making Processes in Games // Proc of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems. Melbourne, Australia, 2003: 265272
[4]  Boyen X, Kollen D. Tractable Inference for Complex Stochastic Processes // Proc of the 14th Annual Conference on Uncertainty in Artificial Intelligence. Madison, USA, 1998: 3342
[5]  Frick M, Groiie M. Deciding FirstOrder Properties of Locally TreeDecomposable Graphs. Journal of the ACM, 2001, 48(6): 11841206
[6]  Draper D. Clustering without (Thinking about) Triangulation // Proc of the 11th Annual Conference on Uncertainty in Artificial Intelligence. Montreal, Canada, 1995: 125133
[7]  Rached Z, Alajaji F, Campbell L L. The KullbackLeibler Divergence Rate between Markov Sources Information Theory. IEEE Trans on Information Theory, 2004, 50(5): 917921
[8]  Kjaerulff U. Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependences // Proc of the 10th Annual Conference on Uncertainty in Artificial Intelligence. Seattle, USA, 1994: 374382
[9]  Paskin M A. Thin Junction Tree Filters Frontier for Simultaneous Localization and Mapping // Proc of the 18th International Joint Conference on Artificial Intelligence. Acapulco, Mexico, 2003: 11571164
[10]  Burkhard H D, Duhaut D, Fujita M, et al. The Road to RoboCup 2050. Robotics & Automation Magazinge, 2002, 9(2): 3138
[11]  Tian Fengzhan, Zhang Hongwei, Lu Yuchang, et al. Simplification of Inferences in Multiply Sectioned Bayesian Networks. Journal of Computer Research and Development, 2003, 40(8): 12301237 (in Chinese) (田凤占,张宏伟,陆玉昌,等.多模块贝叶斯网络中推理的简化.计算机研究与发展, 2003, 40(8): 12301237)
[12]  Murphy K. The Bayes Net Toolbox for Matlab. Computing Science Statics, 2001, 33(2): 331351
[13]  Oliver N M, Rosario B, Pentland A P. A Bayesian Computer Vision System for Modeling Human Interactions. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 831843
[14]  Wang Hongwei, Li Shen, Liu Huixin. Entropic Measurements of Complexity for Markov Decision Processes. Control and Decision, 2004, 19(9): 983987,993 (in Chinese) (王红卫,李 琛,刘会新.马尔可夫决策过程复杂性的熵测度.控制与决策, 2004, 19(9): 983987,993)
[15]  Boutilier C, Poole D. Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations // Proc of the 13th National Conference on Artificial Intelligence. Portland, USA, 1996: 11681175
[16]  Barto A G, Mahadevan S. Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems, 2003, 13(1/2): 4177
[17]  Dagum P, Luby M. Approximating Probabilistic Inference Using Bayesian Networks Is NPHard. Artificial Intelligence, 1993, 60(1): 141153

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