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Iterative Multiagent Probabilistic Inference

Multiply sectioned Bayesian networks (MSBNs) support multiagent probabilistic inference in distributed large problem domains, where agents are organized in a tree structure (called hypertree). In earlier work, agents need to follow an order of the depth-first traversal of the hypertree to update their belief. Hence, agents need some synchronization with each other and belief updating can only be done in a limited parallel. Especially, belief updating will fail if any communication channels have problems. In this paper, we present an iterative method where multiple agents asynchronously perform belief updating in a complete parallel. Compared to the previous work, the iterative method is simple, self- adaptive and robust.