Uncertainty in Artificial Intelligence
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Exploiting Structure in Cooperative Bayesian Games
Frans Oliehoek, Shimon Whiteson, Matthijs Spaan
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.
Keywords:
Pages: 654-665
PS Link:
PDF Link: /papers/12/p654-oliehoek.pdf
BibTex:
@INPROCEEDINGS{Oliehoek12,
AUTHOR = "Frans Oliehoek and Shimon Whiteson and Matthijs Spaan",
TITLE = "Exploiting Structure in Cooperative Bayesian Games",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "654--665"
}


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