Exploring Localization in Bayesian Networks for Large Expert Systems

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, com...

• Type: Conference
• Year: 1992
• Where: UAI
• Authors: Yang Xiang, David Poole, Michael P. Beddoes
• Keywords: Homogeneous Network, Junction Tree, Natural Subdomains, UAI 1992, UAI 2008