Uncertainty in Artificial Intelligence
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Belief Propagation for Structured Decision Making
Qiang Liu, Alexander Ihler
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.
Pages: 523-532
PS Link:
PDF Link: /papers/12/p523-liu.pdf
AUTHOR = "Qiang Liu and Alexander Ihler",
TITLE = "Belief Propagation for Structured Decision Making",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "523--532"

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