Dan Wu and Sonia Bhatti, University of Windsor, Canada

Multiply Sectioned Bayesian Network (MSBN) provides a model for probabilistic reasoning in multi-agent systems. The exact inference is costly and difficult to be applied in the context of MSBNs as the size of problem domain becomes larger and complex. So the approximate techniques are used as an alternative in such cases. Recently, for reasoning in MSBNs, LJFbased Local Adaptive Importance Sampler (LLAIS) has been developed for approximate reasoning in MSBNs. However, the prototype of LLAIS is tested only on Alarm Network (37 nodes). But further testing on larger networks has not been reported yet, so the scalability and reliability of algorithm remains questionable. Hence, we tested LLAIS on three large networks (treated as local JTs) namely Hailfinder (56 nodes), Win95pts (76 nodes) and PathFinder(109 nodes). From the experiments done, it is seen that LLAIS without parameters tuned shows good convergence for Hailfinder and Win95pts but not for Pathfinder network. Further when these parameters are tuned the algorithm shows considerable improvement in its accuracy and convergence for all the three networks tested.

MSBN, LJF, Adaptive Importance sampling, Tunable parameters