Poker


On Strategy Stitching in Large Extensive Form Multiplayer Games

Neural Information Processing Systems

Computing a good strategy in a large extensive form game often demands an extraordinary amount of computer memory, necessitating the use of abstraction to reduce the game size. Typically, strategies from abstract games perform better in the real game as the granularity of abstraction is increased. This paper investigates two techniques for stitching a base strategy in a coarse abstraction of the full game tree, to expert strategies in fine abstractions of smaller subtrees. We provide a general framework for creating static experts, an approach that generalizes some previous strategy stitching efforts. In addition, we show that static experts can create strong agents for both 2-player and 3-player Leduc and Limit Texas Hold'em poker, and that a specific class of static experts can be preferred among a number of alternatives.


PokerBot: Create your poker AI bot in Python - Data Blogger

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In this tutorial, you will learn step-by-step how to implement a poker bot in Python. First, we need an engine in which we can simulate our poker bot. It also has a GUI available which can graphically display a game. Both the engine and the GUI have excellent tutorials on their GitHub pages in how to use them. The choice for the engine (and/or the GUI) is arbitrary and can be replaced by any engine (and/or GUI) you like.


What Online Poker Players Can Teach Us About AI

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Poker is considered a good challenge for AI, as it is seen as combination of mathematical/strategic play, and human intuition, especially about the strategies of others. I would consider the game a cross between the two extremes of technical vs. human skill: chess and rock-paper-scissors. In the game of chess, the technically superior player will almost always win, an amateur would lose literally 100% of their games to the top chess playing AI. In rock-paper-scissors, if the top AI plays the perfect strategy, of each option 1/3rd of the time, it will be unbeatable, but also by definition be incapable of beating anyone. To see why let's analyse how it plays against the Bart Simpson strategy: If your opponent always plays rock, you will play rock 1/3rd of the time, paper 1/3rd and scissors 1/3rd, meaning you will tie 1/3rd, win 1/3rd, and lose 1/3rd.


AI beats professionals in six-player poker

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The AI, called Pluribus, defeated poker professional Darren Elias, who holds the record for most World Poker Tour titles, and Chris "Jesus" Ferguson, winner of six World Series of Poker events. Each pro separately played 5,000 hands of poker against five copies of Pluribus. In another experiment involving 13 pros, all of whom have won more than $1 million playing poker, Pluribus played five pros at a time for a total of 10,000 hands and again emerged victorious. "Pluribus achieved superhuman performance at multi-player poker, which is a recognized milestone in artificial intelligence and in game theory that has been open for decades," said Tuomas Sandholm, Angel Jordan Professor of Computer Science, who developed Pluribus with Noam Brown, who is finishing his Ph.D. in Carnegie Mellon's Computer Science Department as a research scientist at Facebook AI. "Thus far, superhuman AI milestones in strategic reasoning have been limited to two-party competition. The ability to beat five other players in such a complicated game opens up new opportunities to use AI to solve a wide variety of real-world problems."


r/MachineLearning - AMA: We are Noam Brown and Tuomas Sandholm, creators of the Carnegie Mellon / Facebook multiplayer poker bot Pluribus. We're also joined by a few of the pros Pluribus played against. Ask us anything!

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You are right that the algorithms in Pluribus are totally different than reinforcement learning or MCTS. At a high level, that is because our settings are 1) games, that is, there is more than one player, and 2) of imperfect information, that is, when a player has to choose an action, the player does not know the entire state of the world. There is no good textbook on solving imperfect-information games. So, to read up on this literature, you will need to read research papers. Below in this post are selected papers from my research group that would be good to read given that you want to learn about this field.


Facebook and CMU's 'superhuman' poker AI beats human pros

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AI has definitively beaten humans at another of our favorite games. A poker bot, designed by researchers from Facebook's AI lab and Carnegie Mellon University, has bested some of the world's top players in a series of games of six-person no-limit Texas Hold'em poker. Over 12 days and 10,000 hands, the AI system named Pluribus faced off against 12 pros in two different settings. In one, the AI played alongside five human players; in the other, five versions of the AI played with one human player (the computer programs were unable to collaborate in this scenario). Pluribus won an average of $5 per hand with hourly winnings of around $1,000 -- a "decisive margin of victory," according to the researchers.


Facebook's new poker-playing AI could wreck the online poker industry--so it's not being released

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Poker requires a skill that has always seemed uniquely human: the ability to be devious. To win, players must analyze how their opponents are playing and then trick them into handing over their chips. Such cunning, of course, comes pretty naturally to people. Now an AI program has, for the first time, shown itself capable of outwitting a whole table of poker pros using similar skills.


Hold 'Em or Fold 'Em? This A.I. Bluffs With the Best

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As Mr. Elias realized, Pluribus knew when to bluff, when to call someone else's bluff and when to vary its behavior so that other players couldn't pinpoint its strategy. "It does all the things the best players in the world do," said Mr. Elias, 32, who has won a record four titles on the World Poker Tour. "And it does a few things humans have a hard time doing." Experts believe the techniques that drive this and similar systems could be used in Wall Street trading, auctions, political negotiations and cybersecurity, activities that, like poker, involve hidden information. "You don't always know the state of the real world," said Noam Brown, the Facebook researcher who oversaw the Pluribus project.


AI beats professionals at six-player Texas Hold 'Em poker

New Scientist

Artificial intelligence has finally cracked the biggest challenge in poker: beating top professionals in six-player no-limit Texas Hold'Em, the most popular variant of the game. Over 20,000 hands of online poker, the AI beat fifteen of the world's top poker players, each of whom has won more than $1 million USD playing the game professionally. The AI, called Pluribus, was tested in 10,000 games against five human players, as well as in 10,000 rounds where five copies of Pluribus played against one professional – and did better than the pros in both. Pluribus was developed by Noam Brown of Facebook AI Research and Tuomas Sandholm at Carnegie Mellon University in the US. It is an improvement on their previous poker-playing AI, called Libratus, which in 2017 outplayed professionals at Heads-Up Texas Hold'Em, a variant of the game that pits two players head to head.


Bet On The Bot: AI Beats The Professionals At 6-Player Texas Hold 'Em

NPR Technology

During one experiment, the poker bot Pluribus played against five professional players. During one experiment, the poker bot Pluribus played against five professional players. In artificial intelligence, it's a milestone when a computer program can beat top players at a game like chess. But a game like poker, specifically six-player Texas Hold'em, has been too tough for a machine to master -- until now. Researchers say they have designed a bot called Pluribus capable of taking on poker professionals in the most popular form of poker and winning.