@InProceedings{koller01a,
  author =	 {Daphne Koller and Brian Milch},
  title =	 {Multi-Agent Influence Diagrams for Representing and
                  Solving Games},
  googleid =	 {nwc5E1SOP1IJ:scholar.google.com/},
  booktitle =	 {Proceedings of the 17th International Joint
                  Conference on Artificial Intelligence},
  year =	 2001,
  abstract =	 { The traditional representations of games using the
                  extensive form or the strategic (normal) form
                  obscure much of the structure that is present in
                  real-world games. In this paper, we propose a new
                  representation language for general multi-player
                  games --- multi-agent influence diagrams
                  (MAIDs). This representation extends graphical
                  models for probability distributions to a
                  multi-agent decision-making context. MAIDs
                  explicitly encode structure involving the dependence
                  relationships among variables. As a consequence, we
                  can define a notion of strategic relevance of one
                  decision variable to another: D' is strategically
                  relevant to D if, to optimize the decision rule at
                  D, the decision maker needs to take into
                  consideration the decision rule at D'. We provide a
                  sound and complete graphical criterion for
                  determining strategic relevance. We then show how
                  strategic relevance can be used to detect structure
                  in games, allowing a large game to be broken up into
                  a set of interacting smaller games, which can be
                  solved in sequence. We show that this decomposition
                  can lead to substantial savings in the computational
                  cost of finding Nash equilibria in these games.},
  keywords =     {multiagent bayesian},
  url =		 {http://jmvidal.cse.sc.edu/library/koller01a.pdf},
  cluster = 	 {5926612126393763743},
}