A Prototypical System for Soft Evidential Update
Applied Intelligence
A symbolic method for structure verification in multiply sectioned Bayesian networks
Design and application of hybrid intelligent systems
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We consider multiple agents whose task is to determine the true state of an 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 communication? Multiply sectioned Bayesian networks (MSBNs) provide an effective 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 directly with arbitrarily another agent. Are there simpler frameworks with the same performance but with fewer 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 off.