Sunday Read How A.I. Conquered Poker?

Sunday Read How A.I. Conquered Poker

How did AI conquer poker?

The Sunday Read: ‘How A.I. Conquered Poker’ More episodes of The Daily Feb.6, 2022

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Written by Keith Romer Produced by and Aaron Esposito Original music by Aaron Esposito Engineered by Elisheba Ittoop

If you didn’t think poker and artificial intelligence could be bedfellows, think again. Keith Romer delves into the history of man’s pursuit of the perfect game of poker, and explains how the use of A.I. is altering how it is played: individuals using an algorithmic “solver program” to analyze potential weaknesses about themselves and their opponents, thus gaining an advantage.

  • While it feels futuristic, this desire to optimize poker isn’t new.
  • Romer explains how this pursuit harks back at least as far as the 1944 publication of “Theory of Games and Economic Behavior,” by the mathematician John von Neumann and the economist Oskar Morgenstern, who wanted to correct what they saw as a fundamental imprecision in the field of economics.

He explains their thesis, and profiles the current poker players for whom adopting A.I. has become imperative if they are to keep up with the competition. With the dominance of A.I. in poker, older players bemoan what they see as a fundamental change in the game’s fabric.

As Romer shows, there are downsides to this optimization, despite the evident advantages: The emotional swings that come from winning or losing giant sums and the fatigue of 12-hour sessions remain the same challenges as always, but now even the top players have to put in significant work away from the tables to succeed.

Are these new generations of A.I. tools merely a continuation of a longer pattern of technological innovation in poker, or does it mark an irreversible structural shift? One thing’s for certain: The stakes are high. There are a lot of ways to listen to ‘The Daily.’ We want to hear from you.

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  5. Additional production for The Sunday Read was contributed by Emma Kehlbeck, Parin Behrooz, Anna Diamond, Sarah Diamond, Jack D’Isidoro, Elena Hecht, Desiree Ibekwe, Tanya Pérez, Marion Lozano, Naomi Noury, Krish Seenivasan, Corey Schreppel, Margaret Willison, Kate Winslett and Tiana Young.

Special thanks to Mike Benoist, Sam Dolnick, Laura Kim, Julia Simon, Lisa Tobin, Blake Wilson and Ryan Wegner. : The Sunday Read: ‘How A.I. Conquered Poker’
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Can AI win poker?

How Researchers Developed Poker-Playing AI – In an article for in 2015, researchers at the Department of Computing Science at the University of Alberta reported that they had developed a computer program that could calculate the best course of action for a variant of poker called Limit Texas Hold’em.

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The same year, Polish computer programmer and online poker player Piotrek Lopusiewicz started selling the first version of his program PioSOLVER for $249. The program calculates solutions for no-limit poker, which is more complicated than Limit Texas Hold’em. PioSOLVER can help players remake their approach to the game by learning what works best in different situations.

The program even figures out the best and worst hands to bluff with.

After the development of PioSolver, scientists at Carnegie Mellon University partnered with Facebook to develop Pluribus, a poker-playing bot that can beat some of the best Texas Hold’em players in the world.Pluribus played 10,000 hands of poker against five other people who have earned at least a million dollars in poker, and the bot won an average of $480 from its human competitors for every 100 hands – which is similar to the earnings that professional poker players shoot for.One of the reasons Pluribus is groundbreaking is because it plays against more than one person, and doesn’t just rely on game strategy to win.With its solutions to complicated problems, Pluribus can be the gateway to solving some of the biggest problems in the world, like self-driving cars, fraud detection, drug development, and cybersecurity.

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Is there an algorithm for poker?

Algorithm – The algorithm is a numerical approach to quantify the strength of a poker hand where its result expresses the strength of a particular hand in percentile (i.e. ranging from 0 to 1), compared to all other possible hands. The underlying assumption is that an Effective Hand Strength (EHS) is composed of the current Hand Strength (HS) and its potential to improve or deteriorate (PPOT and NPOT): E H S = H S × ( 1 − N P O T ) + ( 1 − H S ) × P P O T where:

E H S is the Effective Hand Strength H S is the current Hand Strength (i.e. not taking into account potential to improve or deteriorate, depending on upcoming table cards N P O T is the Negative POTential (i.e. the probability that our current hand, if the strongest, deteriorates and becomes a losing hand) P P O T is the Positive POTential (i.e. the probability that our current hand, if losing, improves and becomes the winning hand)

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Can poker AI beat humans?

While AI had some success at beating humans at other games such as chess and Go (games that follow predefined rules and aren’t random), winning at poker proved to be more challenging because it requires strategy, intuition, and reasoning based on hidden information.

  1. Despite the challenges, artificial intelligence can now play—and win—poker.
  2. Artificial Intelligence Masters The Game of Poker – What Does That Mean For Humans? Adobe Stock Artificial intelligence systems including DeepStack and Libratus paved the way for Pluribus, the AI that beat five other players in six-player Texas Hold ’em, the most popular version of poker.

This feat goes beyond games. This achievement means that artificial intelligence can now expand to help solve some of the world’s most challenging issues. Tuomas Sandholm, a CMU professor who helped create Pluribus, stated in a press release : “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.” DeepStack: Scalable Approach to Win at Poker The DeepStack team, from the University of Alberta in Edmonton, Canada, combined deep machine learning and algorithms to create AI capable of winning at two-player, “no-limit” Texas Hold ’em, a game more complex for AI to master than others because of its random nature, hidden cards and players’ bluffs.

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DeepStack’s neural networks were trained by solving more than 10 million poker game situations. The AI relies on its neural networks to determine the best moves. DeepStack played two-player Texas Hold ’em against professional poker players from the International Federation of Poker. After playing 44,852 games, DeepStack’s results were ten times what a professional poker player considers a sizable margin.

Libratus: Masters Two-Player Texas Hold ‘Em Libratus is an AI, built by Noam Brown and Tuomas Sandholm of Carnegie Mellon University in 2017, that was ultimately unbeatable at two-person poker. This system required 100 central processing units (CPUs) to run.

Libratus played 120,000 hands in a 20-day poker competition against four top-ranked Texas Hold ’em players. It won by a staggering amount and walked away with $1.8 million in chips. Pluribus: Superhuman Poker-Playing Bot A very significant milestone was achieved by Pluribus, a robot that was able to beat some of the best poker players in the world in a game of six-player Texas Hold ’em.

The scientists at Carnegie Mellon University along with Facebook AI collaborated on the project—the first where AI competed in a game against more than one person and where it couldn’t just rely on game strategy to win. Now that artificial intelligence can beat multiple players in such a complicated game, it’s the gateway to solve some of the world’s most vexing issues such as automated negotiations, drug development, security and cyber-security, self-driving cars and better fraud detection.

Pluribus’ results were impressive. It played 10,000 hands of poker against five others from a pool of million-dollar earners in poker. On average, Pluribus won $480 from its human competitors for every 100 hands-on par with what professional poker players aim to achieve. The research team created Pluribus by building on what it learned from Libratus.

It radically overhauled the search algorithm. Typically, part of the success formula for AI when playing strategic games against an opponent is to process through decision trees until the end of the game before making a move. However, in a multi-player game, this process wasn’t feasible because there was too much-hidden information and the possibilities to process are much greater.

The solution for Pluribus was that it only looks ahead a few moves to determine the action it would take, rather than evaluate all moves until the end of the game. The AI teaches itself through reinforcement learning, continually looking back at plays and evaluating the success based on the circumstances.

If it determines the outcome would have been better with a different move, it learns to apply that to future play. Before competing against humans, Pluribus played trillions of hands of poker against itself. Then it faced off against one professional poker player; when it made a mistake, the player alerted the team.
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Do AI cheat in games?

How AI cheating is sometimes used to make a computer game AI appear intelligent – AI cheating is a crucial gaming tactic to keep players engaged for a long time and make the game more intriguing and challenging. The game will not be particularly exciting or complex if it ends as soon as it begins or if a human player can defeat the AI agent.

Since human players cannot detect AI cheating, it should be used. Cheating in the context of artificial intelligence in video games refers to the programmer giving agents access to knowledge and actions that the player would not have in the same circumstance. It is a well-known fact that many AIs “cheat” (or, at the very least, “fudge”) to stay up with human players, according to Computer Gaming World in 1994.

In general, if it weren’t for this advantage, humans might easily defeat AI after a minimum of trial and error in games where strategic creativity is crucial. The use of cheating in AI demonstrates the limitations of artificially created “intelligence.” Cheating is used to increase performance daily; in many cases, as long as the player is unaware of the outcome, it may be considered appropriate.

A player may view an AI’s inherent advantages as cheating if they cause the agent to behave differently from a human player, even though the fantastic speed and accuracy that come naturally to computers are not considered cheating. “Halo: Combat Evolved,” released in 2001, had some AI cheating to make the gameplay more exciting and the player more engaged with the gaming.a first-person shooter in which the participant takes on the character of the Master Chief and engages in combat with diverse alien enemies on foot and in vehicles.

Enemies deploy suppressing fire and grenades and use cover quite intelligently. Certain adversaries run when their leader dies because the squad condition impacts the individuals. There is a lot of focus on the small details, with opponents firing back grenades and team members retaliating when you upset them. Similarly, ” STALKER” is a first-person shooter survival horror game in which participants must battle military personnel, artificial experimentation, and mercenaries known as Stalkers. Suppose the difficulty level is set to its utmost. In that case, the many enemies the player will face will adopt various battle tactics, including healing injured teammates, giving orders, outwitting the opponent, and employing weapons precisely where needed. Numerous instances of AI cheating were disclosed in the Civilization game series. Turn-based strategy video games like Civilization have been around since 1991. The formal titles of these games, like Sid Meier’s Civilization, typically include Sid Meier’s name. He created the first game in the series and contributed creatively to most others.
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