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GS3 and Tartanian: game theory-based heads-up limit and no-limit Texas Hold'em poker-playing programs
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International Conference on Autonomous Agents archive
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers table of contents
Estoril, Portugal
SESSION: Academic software table of contents
Pages 1647-1648  
Year of Publication: 2008
Authors
Andrew Gilpin  Mellon University, Pittsburgh, PA
Tuomas Sandholm  Mellon University, Pittsburgh, PA
Troels Bjerre Sørensen  University of Aarhus, Århus, Denmark
Sponsors
AAAI : Association for the Advancement of Artifical Intelligence
ACM: Association for Computing Machinery
Publisher
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Downloads (6 Weeks): 9,   Downloads (12 Months): 66,   Citation Count: 0
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ABSTRACT

We demonstrate two game theory-based programs for heads-up limit and no-limit Texas Hold'em poker. The first player, GS3, is designed for playing limit Texas Hold'em, in which all bets are a fixed amount. The second player, Tartanian, is designed for the no-limit variant of the game, in which the amount bet can be any amount up to the number of chips the player has. Both GS3 and Tartanian are based on our potential-aware automated abstraction algorithm for identifying strategically similar situations in order to decrease the size of the game tree. Tartanian, in order to deal with the virtually infinite strategy space of no-limit poker, in addition uses a discretized betting model designed to capture the most important strategic choices in the game. The strategies for both players are computed using our improved version of Nesterov's excessive gap technique specialized for poker.

In this demonstration, participants will be invited to play against both of the players, and to experience first-hand the sophisticated strategies employed by our players.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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A. Gilpin, S. Hoda, J. Peña, and T. Sandholm. Gradient-based algorithms for finding Nash equilibria in extensive form games. In 3rd International Workshop on Internet and Network Economics (WINE), San Diego, CA, 2007.
 
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A. Gilpin and T. Sandholm. Optimal Rhode Island Hold'em poker. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 1684--1685, 2005. Intelligent Systems Demonstration.
 
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A. Gilpin and T. Sandholm. A competitive Texas Hold'em poker player via automated abstraction and real-time equilibrium computation. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 2006.
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A. Gilpin, T. Sandholm, and T. B. Sørensen. Potential-aware automated abstraction of sequential games, and holistic equilibrium analysis of Texas Hold'em poker. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 2007.
 
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S. Hoda, A. Gilpin, and J. Peña. A gradient-based approach for computing Nash equilibria of large sequential games. Available at http://www.optimization-online.org/, 2007.

Collaborative Colleagues:
Andrew Gilpin: colleagues
Tuomas Sandholm: colleagues
Troels Bjerre Sørensen: colleagues