Belief Propagation for Structured Decision Making

Qiang Liu, Alexander T. Ihler
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:521-530, 2012.

Abstract

Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and approximate inference. However, variational approaches have not been widely adoped for decision making in graphical models, often formulated through influence diagrams and including both centralized and decentralized (or multi-agent) decisions. In this work, we present a general variational framework for solving structured cooperative decision-making problems, use it to propose several belief propagation-like algorithms, and analyze them both theoretically and empirically.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-liu12b, title = {Belief Propagation for Structured Decision Making}, author = {Liu, Qiang and Ihler, Alexander T.}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {521--530}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/liu12b/liu12b.pdf}, url = {https://proceedings.mlr.press/r10/liu12b.html}, abstract = {Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and approximate inference. However, variational approaches have not been widely adoped for decision making in graphical models, often formulated through influence diagrams and including both centralized and decentralized (or multi-agent) decisions. In this work, we present a general variational framework for solving structured cooperative decision-making problems, use it to propose several belief propagation-like algorithms, and analyze them both theoretically and empirically.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Belief Propagation for Structured Decision Making %A Qiang Liu %A Alexander T. Ihler %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-liu12b %I PMLR %P 521--530 %U https://proceedings.mlr.press/r10/liu12b.html %V R10 %X Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and approximate inference. However, variational approaches have not been widely adoped for decision making in graphical models, often formulated through influence diagrams and including both centralized and decentralized (or multi-agent) decisions. In this work, we present a general variational framework for solving structured cooperative decision-making problems, use it to propose several belief propagation-like algorithms, and analyze them both theoretically and empirically. %Z Reissued by PMLR on 04 October 2026.
APA
Liu, Q. & Ihler, A.T.. (2012). Belief Propagation for Structured Decision Making. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:521-530 Available from https://proceedings.mlr.press/r10/liu12b.html. Reissued by PMLR on 04 October 2026.

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