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.

Volume

1

fYear

2005

Firstpage

396

Lastpage

400

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Brain, 2005. ICNN&B '05. International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-9422-4

Type

conf

DOI

10.1109/ICNNB.2005.1614641

Filename

1614641