Exploring Localization In Bayesian Networks For Large Expert Systems [chapter]

Yang Xiang, David Poole, Michael P. Beddoes
1992 Uncertainty in Artificial Intelligence  
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
more » ... t 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.
doi:10.1016/b978-1-4832-8287-9.50052-9 fatcat:jk7quua6k5fb3jtxvicecmmzfu