Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Expert Systems
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Local computation with valuations from a commutative semigroup
Annals of Mathematics and Artificial Intelligence
A General Algorithm for Approximate Inference in Multiply Sectioned Bayesian Networks
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Optimal design in collaborative design network
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Loopy Belief Propagation: Convergence and Effects of Message Errors
The Journal of Machine Learning Research
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Inference in multiply sectioned Bayesian networks with extended Shafer-Shenoy and lazy propagation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Iterative join-graph propagation
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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, where agents are organized in a tree structure (called hypertree). In earlier work, agents need to follow an order of the depth-first traversal of the hypertree to update their belief. Hence, agents need some synchronization with each other and belief updating can only be done in a limited parallel. Especially, belief updating will fail if any communication channels have problems. In this paper, we present an iterative method where multiple agents asynchronously perform belief updating in a complete parallel. Compared to the previous work, the iterative method is simple, self-adaptive and robust.