Simple search methods for finding a Nash equilibrium

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AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents (2003, background): “However, in practice Nash equilibria can be found for reasonably large games, using a variety of algorithms (Lemke & Howson, 1964; Porter, Nudelman, & Shoham, 2004; Sandholm, Gilpin, & Conitzer, 2005).5”
Robust game theory (2006, background): “Finally, Porter, Nudelman, and Shoham [43] offer a potential shortcut, which exploits the fact that, for”
Methods for empirical game-theoretic analysis (extended abstract) (2006, background): “The recent surge in interest among AI researchers in game theory has led to numerous advances in game modeling (Gal & Pfeffer 2004; Kearns, Littman, & Singh 2001; Koller & Milch 2003; Leyton-Brown & Tennenholtz 2003) and solution techniques (Gilpin & Sandholm 2006; Porter, Nudelman, & Shoham 2004 )”
Fairness in Multi-Agent Sequential Decision-Making (2014, background): “Prior work [11] demonstrated that it is beneficial to exploit these results in finding a NE, especially in 2-player games.”
Computing the optimal strategy to commit to (2006, background): “A significant amount of research has recently been devoted to the computation of solutions according to various solution concepts for settings in which the agents choose their strategies simultaneously, such as dominance [7, 11, 3] and (especially) Nash equilibrium [8, 21, 16, 15, 2, 22, 23, 4].”