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Recent developments show that Multiply Sectioned Bayesian Networks (MSBNs)can be used for diagnosis of natural systems as well as for model-baseddiagnosis of artificial systems. They can be applied to single-agent orientedreasoning systems as well as multi-agent distributed probabilistic reasoningsystems. Belief propagation between a pair of subnets plays a central role inmaintenance of global consistency in a MSBN. This paper studies the operationUpdateBelief, presented originally with MSBNs, for inter-subnet propagation. Weanalyze how the operation achieves its intended functionality, which provideshints as for how its efficiency can be improved. We then define two newversions of UpdateBelief that reduce the computation time for inter-subnetpropagation. One of them is optimal in the sense that the minimum amount ofcomputation for coordinating multi-linkage belief propagation is required. Theoptimization problem is solved through the solution of a graph-theoreticproblem: the minimum weight open tour in a tree.

Optimization of Inter-Subnet Belief Updating in Multiply Sectioned Bayesian Networks