An Extensional Junction Tree Approximate Inference Algorithm for Dynamic Influence Diagrams
Hongliang Yao, Hao Wang, Yousheng Zhang, Junzhao Li · 2006
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.