Simulation of Graphical Models for Multiagent Probabilistic Inference

Abstract

Multiply-sectioned Bayesian networks (MSBNs) extend Bayesian networks to graphical models for multiagent probabilistic reasoning. The empirical study of algorithms for manipulations of MSBNs (e.g., verification, compilation, and inference) requires experimental MSBNs. As engineering MSBNs in large problem domains requires significant knowledge and engineering effort, the authors explore automatic simulation of MSBNs. Due to the large domain over which an MSBN is defined and a set of constraints to be satisfied, a generate-and-test approach toward simulation has a high rate of failure. The authors present an alternative approach that treats the simulation process as a sequence of decisions. They constrain the space of each decision so that backtracking is minimized and the outcome is always a legal MSBN. A suite of algorithms that implements this approach is presented, and experimental results are shown.