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Computing Card Probabilities in Texas Hold'em

A Simulation System to Support Computer Poker Research

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 as-sessing the agents’ capabilities sinc...

HoldemML: A framework to generate No Limit Hold'em Poker agents from human play...

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 ...

Building a No Limit Texas Hold’em Poker Agent Based on Game Logs Using Su...