Opponent Modeling in Poker (original) (raw)
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Opponent Classification in Poker
2010
Modeling games has a long history in the Artificial Intelligence community. Most of the games that have been considered solved in AI are perfect information games. Imperfect information games like Poker and Bridge represent a domain where there is a great deal of uncertainty involved and additional challenges with respect to modeling the behavior of the opponent etc.
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A Simulation System to Support Computer Poker Research
2011
Abstract. The development of Poker agents is a meaningful domain for AI research because it addresses issues such as opponent modeling, risk management and decision-making under uncertain information. The competitiveness of Poker agents is typically measured through simulation systems that run a series of games. However, current systems do not provide an adequate toolset for assessing the agents' capabilities since they were built to play and not specifically for the creation or validation of new agents.
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Using counterfactual regret minimization to create competitive multiplayer poker agents
2010
Games are used to evaluate and advance Multiagent and Artificial Intelligence techniques. Most of these games are deterministic with perfect information (e.g. Chess and Checkers). A deterministic game has no chance element and in a perfect information game, all information is visible to all players. However, many real-world scenarios with competing agents are stochastic (non-deterministic) with imperfect information. For two-player zero-sum perfect recall games, a recent technique called Counterfactual Regret Minimization (CFR) computes strategies that are provably convergent to an e-Nash equilibrium. A Nash equilibrium strategy is useful in two-player games since it maximizes its utility against a worst-case opponent. However, for multiplayer (three or more player) games, we lose all theoretical guarantees for CFR. However, we believe that CFR-generated agents may perform well in multiplayer games. To test this hypothesis, we used this technique to create several 3-player limit Tex...
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HoldemML: A framework to generate No Limit Hold'em Poker agents from human player strategies
6th Iberian Conference on Information Systems and Technologies (CISTI 2011), 2011
Developing computer programs that play Poker at human level is considered to be challenge to the A.I. research community, due to its incomplete information and stochastic nature. Due to these characteristics of the game, a competitive agent must manage luck and use opponent modeling to be successful at short term and therefore be profitable. In this paper we propose the creation of No Limit Hold'em Poker agents by copying strategies of the best human players, by analyzing past games between them. To accomplish this goal, first we determine the best players on a set of game logs by determining which ones have higher winning expectation. Next, we define a classification problem to represent the player strategy, by associating a game state with the performed action. To validate and test the defined player model, the HoldemML framework was created. This framework generates agents by classifying the data present on the game logs with the goal to copy the best human player tactics. Th...
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