Optimal design in collaborative design network
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Agent interface enhancement: making multiagent graphical models accessible
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Efficient approximate inference in distributed Bayesian networks for MAS-based sensor interpretation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Efficient Distributed Bayesian Reasoning via Targeted Instantiation of Variables
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
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We consider a common task in multiagent systems where agents need to estimate the state of an uncertain domain so that they can act accordingly. If each agent only has partial knowledge about the domain and local observations, how can the agents accomplish the task with a limited 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. Are there simpler frameworks with the same performance but with less constraints? We identify a small set of high level choices which logically imply the key representational choices leading to MSBNs. The result addresses the necessity of constraints of the framework. It facilitates comparisons with related frameworks and provides guidance to potential extensions of the framework.