Opponent Modeling in Texas Hold'em
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Many of the current approaches to opponent modeling research in the domain of poker focus on building an explicit model that captures the opponent’s behavior. Unfortunately, all of these approaches face the same problems for which no solution has yet been found. In this paper, the properties of explicit opponent models and the diﬃculties that they introduce will be discussed and compared to the properties of implicit opponent models. Recently, Bard et al. proposed an implicit approach that seems promising: the agent that is described in their paper is shown to have won the 2011 Annual Computer Poker Competition and recently they entered an agent based on this implicit modeling framework in the 2013 Annual Computer Poker Competition that won (shared) second place. Maybe the time has come to favor implicit models over explicit models for opponent modeling. To be able to make a fair judgment on this we will also discuss the possible problems that are introduced by the implicit modeling framework.