Belief Propagation for Structured Decision Making
  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.
