Building a No Limit Texas Hold’em Poker Agent Based on Game Logs Using Supervised Learning

Teófilo, Luís Filipe; Reis, Luís Paulo

doi:10.1007/978-3-642-21538-4_8

Cited by 8 publications

(4 citation statements)

References 6 publications

(10 reference statements)

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“…A few other methods for opponent modeling were explored in addition to these three main approaches. Teofilo and Reis [2011] presented an opponent modeling method based on clustering. The method applies clustering algorithms to a poker game database to identify player types in No-Limit Texas Holdem based on their actions.…”

Section: Miscellaneous Techniquesmentioning

confidence: 99%

Opponent modeling and exploitation in poker using evolved recurrent neural networks

Miikkulainen

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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for their suggestions and encouragement. Without their help, this dissertation would not have been possible.In addition, I would like to extend my appreciation to my friends and colleagues in the Department of Computer Science at the University of Texas at Austin. It has been a great honor and pleasure working in this lively and inspiring community.Last but not least, I would like to thank my beloved family. In particular, I would like to thank my Mom, who encouraged me to embark on my journey towards a Ph.D. It would not be possible for this dream to come true without her love and dedication.

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Section: Miscellaneous Techniquesmentioning

confidence: 99%

Opponent modeling and exploitation in poker using evolved recurrent neural networks

Miikkulainen

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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“…Mesmo assim foi possível alcançar alguns sucessos como por exemplo em [4] onde um agente virtual conseguiu efetivamente acumular lucros em vários jogos. As técnicas de data mining também têm sido utilizadas de diversas formas como por exemplo para facilitar a identificação das estratégias dos oponentes [7], ou até mesmo para a construção de um agente virtual [8].…”

Section: Trabalho Relacionadounclassified

Poker learner: Players modeling through data-mining

Silva

Reis

2015

2015 10th Iberian Conference on Information Systems and Technologies (CISTI)

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Resumo -Ao longo dos últimos anos, o interesse dos investigadores da área de inteligência artificial no poker tem crescido significativamente. Ao contrário dos jogos de tabuleiro, o poker é um jogo de informação incompleta tornando-se assim um jogo bastante complexo para um agente virtual. Este trabalho tem como objetivo a criação de um modelo de dados que após a aplicação de técnicas de data mining permita obter um modelo de jogador de poker (fase de pré-flop). Será utilizada uma base dados de um jogador profissional onde os dados se encontram em ficheiros de texto. Ao longo do trabalho será utilizado o CRISP-DM, e serão efetuadas as suas diversas fases. Como ferramenta de ETL (Extract, Transform and Load) foi utilizado o Talend e para a execução de técnicas de data mining foi utilizado o software Weka. Como resultado final, foi conseguido um modelo de jogador com uma curva ROC de muito boa qualidade. Este resultado permite concluir que a abordagem está no caminho certo para permitir criar modelos completos de jogadores de poker. Palavras Chaveaprendizagem computacional, poker, jogos, mineração de dados, talend, Weka, CRISP-DM.Abstract -In recent years the game of poker has created a high interest on researchers from the artificial intelligence area. Unlike board games, poker is an incomplete information game becoming a very complex game for a virtual agent. The main objective of this work is to create a data model enabling to apply data mining techniques to obtain a poker player model (pre-flop stage). To do that we used a database from a professional poker player where the data is stored in text files. The work used CRISP-DM, performing its stages. As ETL (Extract, Transform and Load) tool Talend was used and for running the data mining techniques Weka was used. As a final result, a player model was achieved with a very good ROC curve. This result, enable us to conclude that the approach is adequate for creating complete poker player models.

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“…Other recent methodologies based on pattern matching [12] and cased based reasoning [13]. These approaches generate Poker agents based on past games played by human experts.…”

Section: One Great Breakthrough In the Domain Of Computer Poker And Other Extensive-form Games Research Was The Development Of The Countementioning

confidence: 99%

“…In [13], two information sets have a degree of similarity equal to the average similarity of the game features. In [12], instead of the average, the degree of similarity was measured through the Euclidean distance between the game features. The Monte Carlo Search Tree algorithm [14] and reinforcement learning approaches [15] are other techniques that were successfully applied to the domain of Computer Poker.…”

Section: One Great Breakthrough In the Domain Of Computer Poker And Other Extensive-form Games Research Was The Development Of The Countementioning

confidence: 99%

A Profitable Online No-Limit Poker Playing Agent

Teófilo

Reis

Cardoso

2014

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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The No-Limit Texas Hold'em variant of Poker is the game that is most frequently used to assess new developments in incomplete information problems, through the development of game playing agents. For this particular game, current state-ofthe-art techniques consist in the pre-computation of a set of strategies that are in a Nash-Equilibrium state. However, due to the game's decision tree size, current algorithms only work in an abstracted version of No-Limit Poker. Moreover, since these strategies are static, they ignore the opponents' playing style thus being unable to maximize profit against certain kinds of opponents. This makes these strategies unusable when playing in an online environment against human players. In this paper we present a rule-based strategy approach for a No-Limit Poker agent that was developed to play online, against human players and in online multiplayer matches. This strategy is based on a popular technique used by human playersshort stack playingwhich consists of playing in tables with up to 6 players and low initial resources. Using domain specific opponent modeling techniques and limiting the decisions to the first round of the game, the agent was able to make a good profit margin of 11.5% per game when playing against human players. The significance of our results resides in the fact that, for the first time in the Computer Poker literature, we present a game playing agent that can match human players in multiplayer games.

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Building a No Limit Texas Hold’em Poker Agent Based on Game Logs Using Supervised Learning

Cited by 8 publications

References 6 publications

Opponent modeling and exploitation in poker using evolved recurrent neural networks

Opponent modeling and exploitation in poker using evolved recurrent neural networks

Poker learner: Players modeling through data-mining

A Profitable Online No-Limit Poker Playing Agent

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