Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Multiply sectioned Bayesian belief networks for large knowledge-based systems: an application to neuromuscular diagnosis
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Optimal design in collaborative design network
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Agent-based distributed intrusion alert system
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
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Multiply sectioned Bayesian networks (MSBNs) support multiagent probabilistic inference in distributed large problem domains. Inference in MSBNs can be performed effectively using their compiled representations. The compilation involves cooperative moralization and triangulation of the set of local graphical structures that collectively defines the dependencies among domain variables. Privacy of agents prevents us from compiling MSBNs by first assembling graphical subnets at a central location and then compiling their union. In earlier work, agents perform compilation in a limited parallel via a depth-first traversal of the local structures organized in a tree structure (called hypertree). Agents need some synchronization with each other. In this paper, we present an iterative method, by which multiple agents compile MSBNs asynchronously. Compared to the traversal method, the iterative one is self-adaptive and robust.