多Agent动态影响图及其概率分布的近似方法<sup>*</sup>

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (527 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract MultiAgent dynamic influence diagrams (MADIDs) are presented by extending MultiAgent 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 KLdivergence, 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.

Key words： MultiAgent Dynamic Influence Diagrams (MADIDs) Kullbak Leibler (KL)Divergence Junction Tree Extensive Boyen Kollen (EBK) Algorithm

Received: 10 January 2006

ZTFLH:

TP181

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	YAO HongLiang
	WANG Hao
	ZHANG YouSheng
	YU Kui

Cite this article:

YAO HongLiang,WANG Hao,ZHANG YouSheng等. MultiAgent Dynamic Influence Diagrams and Its Approximation of Probability Distribution[J]. , 2007, 20(4): 525-532.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2007/V20/I4/525

[1] 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
[2] 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)
[3] 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
[4] Barto A G, Mahadevan S. Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems, 2003, 13(1/2): 4177
[5] Dagum P, Luby M. Approximating Probabilistic Inference Using Bayesian Networks Is NPHard. Artificial Intelligence, 1993, 60(1): 141153
[6] Howard R A, Matheson J E. Influence Diagrams. Readings on the Principles and Applications of Decision Analysis, 1984, 11(2): 719762
[7] Koller D, Milch B. MultiAgent Influence Diagrams for Representing and Solving Games. Games and Economic Behavior, 2003, 45(1): 181221
[8] 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
[9] 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
[10] Frick M, Groiie M. Deciding FirstOrder Properties of Locally TreeDecomposable Graphs. Journal of the ACM, 2001, 48(6): 11841206
[11] Draper D. Clustering without (Thinking about) Triangulation // Proc of the 11th Annual Conference on Uncertainty in Artificial Intelligence. Montreal, Canada, 1995: 125133
[12] 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
[13] 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
[14] 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
[15] Burkhard H D, Duhaut D, Fujita M, et al. The Road to RoboCup 2050. Robotics & Automation Magazinge, 2002, 9(2): 3138
[16] 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)
[17] Murphy K. The Bayes Net Toolbox for Matlab. Computing Science Statics, 2001, 33(2): 331351