Exploring localization in Bayesian networks for large expert systems

inference computation. How can this aim be realized when the domain is large? Current Bayesian net representations do not consider structure in the domain and include all variables in a homogeneous network. At Real World any time, a human reasoner in a large do-Domain main 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