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

  • Luís Filipe Teófilo
  • Luís Paulo Reis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6752)

Abstract

The development of competitive artificial Poker players is a challenge toArtificial Intelligence (AI) because the agent must deal with unreliable information and deception which make it essential to model the opponents to achieve good results. In this paper we propose the creation of an artificial Poker player through the analysis of past games between human players, with money involved. To accomplish this goal, we defined a classification problem that associates a given game state with the action that was performed by the player. To validate and test the defined player model, an agent that follows the learned tactic was created. The agent approximately follows the tactics from the human players, thus validating this model. However, this approach alone is insufficient to create a competitive agent, as generated strategies are static, meaning that they can’t adapt to different situations. To solve this problem, we created an agent that uses a strategy that combines several tactics from different players.By using the combined strategy, the agentgreatly improved its performance against adversaries capable of modeling opponents.

Keywords

Poker Machine Learning Supervised Learning Opponent Modeling Artificial Intelligence 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luís Filipe Teófilo
    • 1
    • 2
  • Luís Paulo Reis
    • 1
    • 2
  1. 1.Departamento de Engenharia InformáticaFaculdade de Engenharia da Universidade do PortoPortugal
  2. 2.Laboratório de Inteligência Artificial e Ciência de ComputadoresUniversidade do PortoPortugal

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