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Efficient Approximate Inference in Distributed Bayesian Networks for MAS-based Sensor Interpretation (Short Paper)
Norman Carver
- 01 Jan 2008
TL;DR: A new framework is described that supports efficient approximate MASbased sensor interpretation, more autonomy and asynchrony among the agents, and more focused, situation-specific communication patterns.
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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 MASbased 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.
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References
On the role of multiply sectioned Bayesian networks to cooperative multiagent systems
Yang Xiang,Victor Lesser +1 more
- 01 Jul 2003
TL;DR: A small set of high level choices are identified which logically imply the key representational choices leading to MSBNs and facilitate comparisons with related frameworks and provides guidance to potential extensions of the framework.
Inference in belief networks: A procedural guide
Cecil Huang,Adnan Darwiche +1 more
TL;DR: This document provides a self-contained, procedural guide to understanding and implementing probability propagation in trees of clusters (PPTC), and synthesizes various optimizations to PPTC that are scattered throughout the literature.
Multiply sectioned bayesian networks and junction forests for large knowledge-based systems
Yang Xiang,David Poole,Michael P. Beddoes +2 more
- 01 May 1993
TL;DR: In this article, the authors derive reasonable constraints that enable a natural partition of a domain and its representation by separate Bayesian subnets, such that evidential reasoning takes place at only one of them at a time; and marginal probabilities obtained are identical to those that would be obtained from the homogeneous network.
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