Exploiting Structure in Cooperative Bayesian Games

Frans A. Oliehoek, Shimon Whiteson, Matthijs T. J. Spaan
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:653-663, 2012.

Abstract

Cooperative Bayesian games (BGs) can model decision-making problems for teams of agents under imperfect information, but require space and computation time that is exponential in the number of agents. While agent independence has been used to mitigate these problems in perfect information settings, we propose a novel approach for BGs based on the observation that BGs additionally possess a different types of structure, which we call type independence. We propose a factor graph representation that captures both forms of independence and present a theoretical analysis showing that non-serial dynamic programming cannot effectively exploit type independence, while Max-Sum can. Experimental results demonstrate that our approach can tackle cooperative Bayesian games of unprecedented size.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-oliehoek12a, title = {Exploiting Structure in Cooperative {B}ayesian Games}, author = {Oliehoek, Frans A. and Whiteson, Shimon and Spaan, Matthijs T. J.}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {653--663}, 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/oliehoek12a/oliehoek12a.pdf}, url = {https://proceedings.mlr.press/r10/oliehoek12a.html}, abstract = {Cooperative Bayesian games (BGs) can model decision-making problems for teams of agents under imperfect information, but require space and computation time that is exponential in the number of agents. While agent independence has been used to mitigate these problems in perfect information settings, we propose a novel approach for BGs based on the observation that BGs additionally possess a different types of structure, which we call type independence. We propose a factor graph representation that captures both forms of independence and present a theoretical analysis showing that non-serial dynamic programming cannot effectively exploit type independence, while Max-Sum can. Experimental results demonstrate that our approach can tackle cooperative Bayesian games of unprecedented size.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Exploiting Structure in Cooperative Bayesian Games %A Frans A. Oliehoek %A Shimon Whiteson %A Matthijs T. J. Spaan %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-oliehoek12a %I PMLR %P 653--663 %U https://proceedings.mlr.press/r10/oliehoek12a.html %V R10 %X Cooperative Bayesian games (BGs) can model decision-making problems for teams of agents under imperfect information, but require space and computation time that is exponential in the number of agents. While agent independence has been used to mitigate these problems in perfect information settings, we propose a novel approach for BGs based on the observation that BGs additionally possess a different types of structure, which we call type independence. We propose a factor graph representation that captures both forms of independence and present a theoretical analysis showing that non-serial dynamic programming cannot effectively exploit type independence, while Max-Sum can. Experimental results demonstrate that our approach can tackle cooperative Bayesian games of unprecedented size. %Z Reissued by PMLR on 04 October 2026.
APA
Oliehoek, F.A., Whiteson, S. & Spaan, M.T.J.. (2012). Exploiting Structure in Cooperative Bayesian Games. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:653-663 Available from https://proceedings.mlr.press/r10/oliehoek12a.html. Reissued by PMLR on 04 October 2026.

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