Title :
An Extensional Junction Tree Approximate Inference Algorithm for Dynamic Influence Diagrams
Author :
Yao, Hongliang ; Wang, Hao ; Zhang, Yousheng ; Li, Junzhao
Author_Institution :
Dept. of Comput. Sci. & Technol., Hefei Univ. of Technol.
Abstract :
We introduce a dynamic influence diagram (DID) to model a multi-agent system in dynamic environment, where the DID is an extension of a static influence diagram over time. Based on splitting junction tree and Boyen-Koller algorithm, an extensional junction tree approximate inference algorithm of DID is proposed in this paper, where clusters of junction tree are split by strategic relevance. Finally, in the setting of Robocup, we use DID to emulate three agents´ pass-catch problem, and analyze the complexity and the error of extensional junction tree approximate inference algorithm
Keywords :
inference mechanisms; multi-agent systems; trees (mathematics); uncertainty handling; dynamic environment; dynamic influence diagrams; extensional junction tree approximate inference algorithm; multi-agent system; splitting junction tree; Bayesian methods; Clustering algorithms; Computer science; Electronic mail; Heuristic algorithms; Inference algorithms; Multiagent systems; Probability distribution; Stochastic processes; Tree graphs;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614641
