Uncertainty in Artificial Intelligence
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Optimization of Inter-Subnet Belief Updating in Multiply Sectioned Bayesian Networks
Yang Xiang
Abstract:
Recent developments show that Multiply Sectioned Bayesian Networks (MSBNs) can be used for diagnosis of natural systems as well as for model-based diagnosis of artificial systems. They can be applied to single-agent oriented reasoning systems as well as multi-agent distributed probabilistic reasoning systems. Belief propagation between a pair of subnets plays a central role in maintenance of global consistency in a MSBN. This paper studies the operation UpdateBelief, presented originally with MSBNs, for inter-subnet propagation. We analyze how the operation achieves its intended functionality, which provides hints as for how its efficiency can be improved. We then define two new versions of UpdateBelief that reduce the computation time for inter-subnet propagation. One of them is optimal in the sense that the minimum amount of computation for coordinating multi-linkage belief propagation is required. The optimization problem is solved through the solution of a graph-theoretic problem: the minimum weight open tour in a tree.
Keywords: Bayesian networks, belief propagation, probabilistic reasoning, multi-agent reasonin
Pages: 565-573
PS Link:
PDF Link: /papers/95/p565-xiang.pdf
BibTex:
@INPROCEEDINGS{Xiang95,
AUTHOR = "Yang Xiang ",
TITLE = "Optimization of Inter-Subnet Belief Updating in Multiply Sectioned Bayesian Networks",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "565--573"
}


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