Superhuman AI for Multiplayer Poker: Difference between revisions
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In the past two decades, most of the superhuman AI that were built can only beat human players in two-player zero-sum games. More specifically, in the game of poker we only have AI models that can beat them in two-player settings. Poker is a great challenge in AI and game theory because it captures the challenges in hidden information so elegantly. This means that developing a superhuman AI in multiplayer poker is the remaining great milestone in this field. In this paper, the AI whom we call Pluribus, is capable of defeating human professional poker players in Texas hold'em poker which is a six-player poker game and is the most commonly played format in the world. | In the past two decades, most of the superhuman AI that were built can only beat human players in two-player zero-sum games. More specifically, in the game of poker we only have AI models that can beat them in two-player settings. Poker is a great challenge in AI and game theory because it captures the challenges in hidden information so elegantly. This means that developing a superhuman AI in multiplayer poker is the remaining great milestone in this field. In this paper, the AI whom we call Pluribus, is capable of defeating human professional poker players in Texas hold'em poker which is a six-player poker game and is the most commonly played format in the world. | ||
== | == Challenges of Multiplayer Games == | ||
Many AI have reached superhuman performance in games like 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. | |||
The problem with current AI systems is that they only try to achieve Nash equilibriums instead of trying to actively detect and exploit weaknesses in opponents. For example, let's consider the game of Rock-Paper-Scissors, the Nash equilibrium is to randomly pick any option with equal probability. However, we can see that this means the best strategy that the opponent can have will result in a tie. Therefore, in this example our player cannot win in expectation. | |||
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Revision as of 12:16, 14 November 2020
Presented by
Hansa Halim, Sanjana Rajendra Naik, Samka Marfua, Shawrupa Proshasty
Introduction
In the past two decades, most of the superhuman AI that were built can only beat human players in two-player zero-sum games. More specifically, in the game of poker we only have AI models that can beat them in two-player settings. Poker is a great challenge in AI and game theory because it captures the challenges in hidden information so elegantly. This means that developing a superhuman AI in multiplayer poker is the remaining great milestone in this field. In this paper, the AI whom we call Pluribus, is capable of defeating human professional poker players in Texas hold'em poker which is a six-player poker game and is the most commonly played format in the world.
Challenges of Multiplayer Games
Many AI have reached superhuman performance in games like 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.
The problem with current AI systems is that they only try to achieve Nash equilibriums instead of trying to actively detect and exploit weaknesses in opponents. For example, let's consider the game of Rock-Paper-Scissors, the Nash equilibrium is to randomly pick any option with equal probability. However, we can see that this means the best strategy that the opponent can have will result in a tie. Therefore, in this example our player cannot win in expectation.
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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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