Generating Dependence Structure of Multiply Sectioned Bayesian Networks

AAAI Conferences 

Multiply sectioned Bayesian networks (MSBNs) provide a general and exact framework for multi-agent distributed interpretation. To investigate algorithms for inference and other operations, experimental MSBNs are necessary. However, it is very time consuming and tedious to construct MSBNs manually. In this work, we investigate pseduo-random generation of MSBNs. Our focus is on the generation of MSBN structures. Pseduo-random generation of MSBN structures can be performed by a generate-and-test approach.

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