Uncertainty in Artificial Intelligence
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Computing Nash Equilibria of Action-Graph Games
Navin Bhat, Kevin Leyton-Brown
Abstract:
Action-graph games (AGGs) are a fully expressive game representation which can compactly express both strict and context-specific independence between players' utility functions. Actions are represented as nodes in a graph G, and the payoff to an agent who chose the action s depends only on the numbers of other agents who chose actions connected to s. We present algorithms for computing both symmetric and arbitrary equilibria of AGGs using a continuation method. We analyze the worst-case cost of computing the Jacobian of the payoff function, the exponential-time bottleneck step, and in all cases achieve exponential speedup. When the indegree of G is bounded by a constant and the game is symmetric, the Jacobian can be computed in polynomial time.
Keywords: null
Pages: 35-42
PS Link:
PDF Link: /papers/04/p35-bhat.pdf
BibTex:
@INPROCEEDINGS{Bhat04,
AUTHOR = "Navin Bhat and Kevin Leyton-Brown",
TITLE = "Computing Nash Equilibria of Action-Graph Games",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "35--42"
}


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