Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Robust probabilistic inference in distributed systems
UAI '04 Proceedings of the 20th conference on Uncertainty in 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
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Efficient design and inference in distributed bayesian networks: an overview
TbiLLC'09 Proceedings of the 8th international tbilisi conference on Logic, language, and computation
| Hi-index | 0.00 |
This paper is focusing on exact Bayesian reasoning in systems of agents, which represent weakly coupled processing modules supporting collaborative inference through message passing. By using the theory on factor graphs and cluster graphs we (i) analyze the suitability of the existing approaches to modular inference with respect to a relevant class of domains and (ii) derive methods for construction of modular systems, which support globally coherent Bayesian inference without compilation of secondary probabilistic structures spanning multiple modules. In the proposed approach dependencies between inference modules are reduced through targeted instantiation of variables, which is based on the analysis of cluster graphs.