Uncertainty in Artificial Intelligence
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Exploring Localization in Bayesian Networks for Large Expert Systems
Yang Xiang, David Poole, Michael Beddoes
Abstract:
Current Bayesian net representations do not consider structure in the domain and include all variables in a homogeneous network. At any time, a human reasoner in a large domain may direct his attention to only one of a number of natural subdomains, i.e., there is ‘localization' of queries and evidence. In such a case, propagating evidence through a homogeneous network is inefficient since the entire network has to be updated each time. This paper presents multiply sectioned Bayesian networks that enable a (localization preserving) representation of natural subdomains by separate Bayesian subnets. The subnets are transformed into a set of permanent junction trees such that evidential reasoning takes place at only one of them at a time. Probabilities obtained are identical to those that would be obtained from the homogeneous network. We discuss attention shift to a different junction tree and propagation of previously acquired evidence. Although the overall system can be large, computational requirements are governed by the size of only one junction tree.
Keywords:
Pages: 344-351
PS Link:
PDF Link: /papers/92/p344-xiang.pdf
BibTex:
@INPROCEEDINGS{Xiang92,
AUTHOR = "Yang Xiang and David Poole and Michael Beddoes",
TITLE = "Exploring Localization in Bayesian Networks for Large Expert Systems",
BOOKTITLE = "Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-92)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1992",
PAGES = "344--351"
}


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