Marginal Calibration in Multi-agent Probabilistic Systems
Karen H. Jin, Dan Wu · 2008
The multiply sectioned Bayesian network (MSBN) model successfully extends the traditional Bayesian network (BN) model for the support of probabilistic inference in distributed multi-agent systems. However, existing MSBN inference methods do not allow agents to reason about their own problem sub-domains right after the initialization process. Extensive amount of inter-agent message passings are needed to calibrate each agent's local subnet into a correct prior marginal distribution. In this paper, we introduce the concept of prior marginal factors to facilitate this process. Based on the analysis of the prior marginal factors, minimum message passing is required during calibration. Furthermore, we have removed the requirement of maintaining a consistent junction tree (JT) during message calculation. Therefore, our marginal calibration algorithm guarantees that a prior marginal in each MSBN subnet is formed with greatly reduced communication and computational cost. Our preliminary experiments have confirmed the improved time efficiency of the proposed algorithm.