Anytime algorithms for multiagent decision making using coordination graphs

Nikos Vlassis, Reinoud Elhorst, and Jelle R. Kok. Anytime algorithms for multiagent decision making using coordination graphs. In Proceedings of the International Conference on Systems, Man, and Cybernetics (SMC), The Hague, The Netherlands, October 2004.

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Abstract

Coordination graphs provide a tractable framework for cooperative multiagent decision making, by decomposing the global payoff function into a sum of local terms. In this paper we review some distributed algorithms for action selection in a coordination graph, and discuss their pros and cons. For real-time decision making, we emphasize the need for anytime algorithms for action selection: these are algorithms that improve the quality of the solution over time. We describe variable elimination, coordinate ascent, and the max-plus algorithm in Bayesian networks. We discuss some interesting open problems related to the use of max-plus for real-time multiagent decision making.

BibTeX Entry

@InProceedings{Vlassis04smc,
  author =       {Nikos Vlassis and Reinoud Elhorst and Jelle R. Kok},
  title =        {Anytime algorithms for multiagent decision making
                  using coordination graphs},
  address =      {The Hague, The Netherlands},
  booktitle =    {Proceedings of the International Conference on
                  Systems, Man, and Cybernetics (SMC)},
  year =         2004,
  month =        oct,
  abstract =     { Coordination graphs provide a tractable framework
                  for cooperative multiagent decision making, by
                  decomposing the global payoff function into a sum of
                  local terms. In this paper we review some
                  distributed algorithms for action selection in a
                  coordination graph, and discuss their pros and
                  cons. For real-time decision making, we emphasize
                  the need for anytime algorithms for action
                  selection: these are algorithms that improve the
                  quality of the solution over time. We describe
                  variable elimination, coordinate ascent, and the
                  max-plus algorithm in Bayesian networks. We discuss
                  some interesting open problems related to the use of
                  max-plus for real-time multiagent decision making. }
}

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