Justifying Multiply Sectioned Bayesian Networks

We consider multiple agents who s task is to determine the true state of a uncertain domain so they can act properly If each agent only has partial knowledge about the domain and local observation how can agents accomplish the task with the least amount of commu nication Multiply sectioned Bayesian networks MSBNs provide an e ective and exact framework for such a task but also impose a set of constraints The most notable is the hypertree agent organization which prevents an agent from communicating with arbitrarily another agent Are there simpler frameworks with the same performance but with less restrictions We identify a small set of high level choices which logically imply the key representational choices made in MSBNs The result addresses concerns regarding the necessity of restrictions of the framework It facilitates comparison with related frameworks and provides guidance to extension of the framework as what can or cannot be traded o