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Computing an approximate jam/fold equilibrium for 3-player no-limit Texas Hold'em tournaments
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International Conference on Autonomous Agents archive
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 table of contents
Estoril, Portugal
SESSION: Economic paradigms table of contents
Pages 919-925  
Year of Publication: 2008
ISBN:978-0-9817381-1-6
Authors
Sam Ganzfried  Carnegie Mellon University, Pittsburgh, PA
Tuomas Sandholm  Carnegie Mellon University, Pittsburgh, PA
Sponsors
AAAI : Association for the Advancement of Artifical Intelligence
ACM: Association for Computing Machinery
Publisher
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Downloads (6 Weeks): 8,   Downloads (12 Months): 83,   Citation Count: 2
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ABSTRACT

A recent paper computes near-optimal strategies for two-player no-limit Texas hold'em tournaments; however, the techniques used are unable to compute equilibrium strategies for tournaments with more than two players. Motivated by the widespread popularity of multiplayer tournaments and the observation that jam/fold strategies are nearoptimal in the two player case, we develop an algorithm that computes approximate jam/fold equilibrium strategies in tournaments with three --- and potentially even more --- players. Our algorithm combines an extension of fictitious play to imperfect information games, an algorithm similar to value iteration for solving stochastic games, and a heuristic from the poker community known as the Independent Chip Model which we use as an initialization. Several ways of exploiting suit symmetries and the use of custom indexing schemes made the approach computationally feasible. Aside from the initialization and the restriction to jam/fold strategies, our high level algorithm makes no poker-specific assumptions and thus also applies to other multiplayer stochastic games of imperfect information.


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.

 
1
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2
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D. Fudenberg and D. Levine. The Theory of Learning in Games. MIT Press, 1998.
 
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S. Ganzfried and T. Sandholm. Algorithms for multiplayer stochastic games of imperfect information with application to three-player no-limit Texas hold'em tournaments. Draft.
 
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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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Collaborative Colleagues:
Sam Ganzfried: colleagues
Tuomas Sandholm: colleagues