Abstract
As MMDPs are difficult to represent structural relations among Agents and MAIDs can not model dynamic environment, we present Multi-Agent dynamic influences (MADIDs). MADIDs have stronger knowledge representation ability and MADIDs may efficiently model dynamic environment and structural relations among Agents. Based on the hierarchical decomposition of MADIDs, a junction tree factored particle filter (JFP) algorithm is presented by combing the advantages of the junction trees and particle filter. JFP algorithm converts the distribution of MADIDs into the local factorial form, and the inference is performed by factor particle of propagation on the junction tree. Finally, and the results of algorithm comparison show that the error of JFP algorithm is obviously less than BK algorithm and PF algorithm without the loss of time performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Howard, R.A., Matheson, J.E.: Influence diagrams. Readings on the Principles and Applications of Decision Analysis 2, 719–792 (1981)
Koller, D., Milch, B.: Multi-agent influence diagrams for representing and solving games. In: IJCAI, pp. 1024–1034 (2001)
Boutilier, C.: Sequential optimality and coordination in multi-agent systems. In: IJCAI 1999, pp. 478–485 (1999)
Barto, A.G., Mahadevan: Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems. Theory and Applications 13(1/2), 41–77 (2003)
Wang, H.W., Li, C., Liu, H.: Entropic measurements of complexity for Markov decision processes. Control and Decision 19(9), 983–987 (2004)
Boyen, X., Kollen, D.: Tractable inference for complex stochastic processes. In: Proc. Of UAI 1998, pp. 33–42 (1998)
Paskin, M.A.: Thin junction tree filters for simultaneous localization and mapping. In: Proc. of IJCAI 2003, pp. 157–1164 (2003)
Frick, M., Groile, M.: Deciding first-order properties of locally tree-decomposable graphs. Journal of the ACM (48), 1184–1206 (2001)
Doucet, A., de Freitas, N., Murphy, K.: Rao-blackwellised particle filtering for dynamic Bayesian networks. In: Proceedings of the UAI-16th, pp. 253–259 (2000)
Burkhard, H.D., Duhaut, D., Fujita, M., Lima, P.: The road to RoboCup 2050. IEEE Robotics & Automation Magazine 9(2), 31–38 (2002)
Kjaerulff, U.: Reduction of computational complexity in Bayesian networks through removal of weak dependences. In: UAI 1994, pp. 374–382 (1994)
Murphy, K.: The bayes net toolbox for matlab. Computing Science and Statistics 33 (2001)
Yao, H.-L., Hao, W., Zhang, Y.-S.: Research on Multi-Agent Dynamic Influence Diagrams and Its Approximate Inference Algorithm. Chinese Journal of Computers 2(31), 236–244 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yao, H., Chang, J., Jiang, C., Wang, H. (2009). Junction Tree Factored Particle Inference Algorithm for Multi-Agent Dynamic Influence Diagrams. In: Deng, X., Hopcroft, J.E., Xue, J. (eds) Frontiers in Algorithmics. FAW 2009. Lecture Notes in Computer Science, vol 5598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02270-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-02270-8_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02269-2
Online ISBN: 978-3-642-02270-8
eBook Packages: Computer ScienceComputer Science (R0)