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An Analysis Methodology for Belief Sharing in Large Groups

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Robin Glinton, Robotics Institute, Carnegie Mellon University
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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.