Regret Minimization in Games with Incomplete Information

Abstract

Extensive games are a powerful model of multiagent decision-making scenarios<lb>with incomplete information. Finding a Nash equilibrium for very large instances<lb>of these games has received a great deal of recent attention. In this paper, we<lb>describe a new technique for solving large games based on regret minimization.<lb>In particular, we introduce… (More)

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