A Probabilistic Framework for Multi-agent Distributed Interpretation and Optimization of Communicati

Multiply sectioned Bayesian networks for single-agent systems are extended into a framework for multi-agent distributed interpretation systems. Each agent is represented as a Bayesian subnet. Unlike in single-agent systems where evidence is entered one subnet at a time, multiple agents may acquire evidence asynchronously in parallel. New communication operations are thus proposed to maintain global consistency. Inter-agent 'distance' prevents constant maintenance of global consistency. We show that, if new operations are followed, between two successive communications, answers to queries from an agent are consistent with all local evidence, and are consistent with all global evidence gathered up to the rst communication. During communication, each agent is not available to process evidence for a period of time (called o -line time). Two criteria for minimization of o -line time, which may commonly be used, are considered. We derive, under each criterion, the optimal schedules when communication is initiated from an arbitrarily selected agent.