Efficient approximate inference in distributed Bayesian networks for MAS-based sensor interpretation

Authors:
Norman Carver
Affiliations:
Southern Illinois University Carbondale, Carbondale, IL
Venue:
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Year:
2008

Citing 2
Cited 0

A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication

Artificial Intelligence
On the role of multiply sectioned Bayesian networks to cooperative multiagent systems

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

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Abstract

The multiply sectioned Bayesian network (MSBN) framework is the most studied approach for distributed Bayesian Network inference in an MAS setting. This paper describes a new framework that supports efficient approximate MAS-based sensor interpretation, more autonomy and asynchrony among the agents, and more focused, situation-specific communication patterns. Its use can lead to significant improvements in agent utilization and time-to-solution.