Controlling Information Exchange in Distributed Bayesian Networks

We propose an algorithm that given a problem formulated as a Distributed Bayesian Network, finds a coordination strategy which minimizes the communication costs while achieving the desired confidence level of the global solution. We developed a system based on this algorithm which models the communication decision process for any given problem structure as a Markov Decision Process and use dynamic programming to produce the optimal communication strategy. To reduce the computational cost of the MDP approach, we further propose an algorithm based on the concept of Mutual Information to approximate the optimal solution. Experimental results for both systems are given to illustrate the effectiveness of the algorithms.