An Analysis Methodology for Belief Sharing in Large Groups

Robin Glinton, Robotics Institute, Carnegie Mellon University

Many applications require that a group of agents share a coherent distributed picture of the world given communication constraints. This paper describes an analysis and design methodology for coordination algorithms for extremely large groups of agents maintaining a distributed belief. This design methodology creates a probability distribution which relates global properties of the system to agent interaction dynamics using the tools of statistical mechanics. Using this probability distribution we show that this system undergoes a rapid phase transition between low divergence and high divergence in the distributed belief at a critical value of system temperature. We also show empirically that at the critical system temperature the number of messages passed and belief divergence between agents is optimal. Finally, we use this fact to develop an algorithm using system temperature as a local decision parameter for an agent.