@InProceedings{vickrey02a,
  author =	 {David Vickrey and Daphne Koller},
  title =	 {Multi-Agent Algorithms for Solving Graphical Games},
  googleid =	 {FY8qu4e2I-IJ:scholar.google.com/},
  booktitle =	 {Proceedings of the Eighteenth National Conference on
                  Artificial Intelligence},
  pages =	 {345--351},
  year =	 2002,
  abstract =	 {Consider the problem of a group of agents trying to
                  find a stable strategy profile for a joint
                  interaction. A standard approach is to describe the
                  situation as a single multiplayer game and find an
                  equilibrium strategy profile of that game. However,
                  most algorithms for finding equilibria are
                  computationally expensive; they are also
                  centralized, requiring that all relevant payoff
                  information be available to a single agent (or
                  computer) who must determine the entire equilibrium
                  profile. In this paper, we exploit two ideas to
                  address these problems. We consider structured game
                  representations, where the interaction between the
                  agents is sparse, an assumption that holds in many
                  real-world situations. We also consider the slightly
                  relaxed task of finding an approximate
                  equilibrium. We present two algorithms for finding
                  approximate equilibria in these games, one based on
                  a hill-climbing approach and one on constraint
                  satisfaction. We show that these algorithms exploit
                  the game structure to achieve faster
                  computation. They are also inherently local,
                  requiring only limited communication between
                  directly interacting agents. They can thus be scaled
                  to games involving large numbers of agents, provided
                  the interaction between the agents is not too
                  dense.},
  keywords =     {multiagent game-theory},
  url =		 {http://jmvidal.cse.sc.edu/library/vickrey02a.ps},
  cluster = 	 {16295068570833555221}
}