Exploring Localization in Bayesian Networks for Large Expert Systems
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.
Cite
@article{arxiv.1303.5438,
title = {Exploring Localization in Bayesian Networks for Large Expert Systems},
author = {Yang Xiang and David L. Poole and Michael P. Beddoes},
journal= {arXiv preprint arXiv:1303.5438},
year = {2013}
}
Comments
Appears in Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI1992)