The support-enumeration method (SEM) for computation of Nash equilibrium has been shown to achieve state-of-the-art empirical performance on normal-form games. Action-graph games (AGGs) are exponentially smaller than the normal form on many important classes of games. We show how SEM can be extended to the AGG representation, yielding an exponential improvement in worst-case runtime. Empirically, we demonstrate that our AGG-optimized SEM algorithm substantially outperforms the original SEM, and also outperforms state-of-the-art AGG-optimized algorithms on most problem distributions. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Thompson, D. R. M., Leung, S., & Leyton-Brown, K. (2011). Computing Nash equilibria of action-graph games via support enumeration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7090 LNCS, pp. 338–350). Springer Verlag. https://doi.org/10.1007/978-3-642-25510-6_29
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