Computing Nash equilibria of action-graph games

  • Authors:
  • Navin A. R. Bhat;Kevin Leyton-Brown

  • Affiliations:
  • University of Toronto, Toronto, ON Canada;University of British Columbia, Vancouver, BC Canada

  • Venue:
  • UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
  • Year:
  • 2004

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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 in-degree of G is bounded by a constant and the game is symmetric, the Jacobian can be computed in polynomial time.