Superhuman AI for Multiplayer Poker: Difference between revisions
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== Introduction == | == Introduction == | ||
For many years, most of the superhuman AI that were built can only beat human players in two-player zero-sum games. These games include checkers, chess, two-player limit poker, Go, and two-player no-limit poker. The most common strategy that the AI use to beat those games is to find the most optimal Nash equilibrium. A Nash equilibrium is the best possible choice that a player can take, regardless of what their opponent is going to choose. Nash equilibrium has been proven to always exists in all finite games, and the challenge is to find the equilibrium. To summarize, Nash equilibrium is the best possible strategy and is unbeatable in two-player zero-sum games, since it guarantees to not lose in expectation regardless what the opponent is doing. | |||
== Related Work == | == Related Work == | ||
Revision as of 12:49, 14 November 2020
Presented by
Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty
Introduction
For many years, most of the superhuman AI that were built can only beat human players in two-player zero-sum games. These games include checkers, chess, two-player limit poker, Go, and two-player no-limit poker. The most common strategy that the AI use to beat those games is to find the most optimal Nash equilibrium. A Nash equilibrium is the best possible choice that a player can take, regardless of what their opponent is going to choose. Nash equilibrium has been proven to always exists in all finite games, and the challenge is to find the equilibrium. To summarize, Nash equilibrium is the best possible strategy and is unbeatable in two-player zero-sum games, since it guarantees to not lose in expectation regardless what the opponent is doing.
Related Work
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Theoretical Analysis
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Experimental Results
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Discussion
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Conclusion
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Critiques
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References
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