“Poker has been a benchmark in the field of AI for a long time,” says Noam Brown, a computer science PhD student at CMU who developed the code for Libratus. They have recently developed an Artificial Intelligence called the “Libratus”. Poker players are in a shock that this little masterpiece has managed to defeat four of the top Poker Pros in a 20-day Poker event called the “Brains Vs Artificial Intelligence: Upping the Ante”. This event was held at the Rivers Casino in Pittsburgh. “Poker is the main benchmark and challenge program for games of imperfect information,” Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh. The game, it turns out, has become the gold standard for developing artificial intelligence.

Libratus, the artificial intelligence (AI) engine designed by Professor Tuomas Sandholm at Carnegie Mellon University (CMU) and his graduate student Noam Brown has made an impression on Jason Les, one of the world’s top poker players. Poker News, the poker industry’s online news magazine, recently interviewed Les. A couple questions were telling when asked about which is a better name for his firstborn child and which is the more annoying opponent, Claudico or Libratus. For both questions, he responded with Libratus.[1]^,[2]

In January, Les and three others of the world’s top four poker champions—Dong Kim, Daniel McAulay, and Jimmy Chou—were challenged to 20 days of No-limit Heads-up Texas Hold ‘em poker at the Brains versus Artificial Intelligence tournament in Pittsburgh’s Rivers Casino. Libratus beat all four opponents, taking away more than $1.7 million in chips.

Les also considered that being selected to play against Libratus as his proudest poker accomplishment to date.

During the tournament, Dong Kim said about Libratus, “I didn’t realize how good it was until today. I felt like I was playing against someone who was cheating, like it could see my cards. I’m not accusing it of cheating. It was just that good.”[3] Kim also noted during the tournament that “he and his fellow humans have no real chance of winning.”[4]

Texas Hold ‘em, The Holy Grail

“In the area of game theory,” said Sandholm, “in which I’ve been working since 1989, No-Limit Heads-Up Texas Hold ‘em is the holy grail of imperfect-information games.” According to Sandholm, this version of poker was really the last game that had not been cracked by AI, in the sense of becoming better than humans. AI has already beaten top experts of other games, such as chess and go, but Texas Hold ‘em is different. In those games, the game play is open. All players know about where the pieces are at any time and the strategic possibilities presented, and thus can directly solve endgame strategies. In Texas Hold ‘em, players have a limited amount of information available about the opponents’ cards because some cards are played on the table and some are held privately in each player’s hand. The private cards represent hidden information that each player uses to devise their strategy. Thus, it is reasonable to assume that each player’s strategy is rational and designed to win the greatest amount of chips.

“Besides being a limited information play, the stakes space is huge. There are 10¹⁶⁰ different situations the player can face. You can’t solve all those possibilities, and you can’t solve a sub-game of the game tree with information from a sub game only. How you play a sub-game actually depends on how you play in totally different parts of the game. It takes totally different algorithms from a game like chess, checkers, or go.”

Learning and Reasoning in a Limited Information Environment

Libratus is an AI system designed to learn in a limited information environment. It consists of three modules:

1. A module that uses game-theoretic reasoning, just with the rules of the game as input, ahead of the actual game, to compute a blueprint strategy for playing the game. According to Sandholm, “It uses Nash equilibrium approximation, but with a new version of the Monte Carlo counterfactual regret minimization (MCCRM) algorithm that makes the MCCRM faster, and it also mitigates the issue of involving imperfect recall abstraction.”
2. An subgame solver that recalculates strategy on the fly so the software can refine its blueprint strategy with each move.
3. A post-play analyzer that reviews the opponent’s plays that exploited holes in Libratus’s strategies. “Then, Libratus re-computes a more refined strategy for those parts of the stage space, essentially finding and fixing the holes in its strategy to play even closer to Nash equilibrium in those spots,” stated Sandholm.

From Claudico to Libratus

Les and Kim also played against Sandholm’s previous AI poker player, Claudico, in 2015. And, while the two programs were authored by the same persons, Libratus is a different application. “Between Claudico and Libratus, we developed a better equilibrium-finding algorithm for module one, so we could do much finer-grained abstractions. The endgame solver of module two is a big improvement. In Claudico, we found during the 2015 tournament, that the application ran well with or without the endgame solver we had built. Libratus’ endgame solver improves play between 80 to 100 milli-big blinds per hand (mbb/hand),[5] increases safety, or non-exploitability, and it is a nested endgame solver, whereas Claudico was not. In module three, Claudico essentially continued overnight what it did in module one, while in Libratus, it actually fixes exploitable holes in its own strategy. That’s an entirely new aspect,” explained Sandholm.

Sandholm and his students have been working on poker optimization for 13 years. He typically had two PhD Students working with him, “but the actual code for Libratus was written from scratch,” stated Sandholm. He and his student, Noam Brown, spent a little over a year writing Libratus. “In 2015, we couldn’t beat the best players with Claudico, and many wondered how much we could accomplish in just a little over a year of work on Libratus,” added Sandholm. “In fact, the international betting sites were putting us at from four to five to one odds against.” It turned out not to be a safe bet.

Bridges, the Supercomputing Poker Machine

For the tournament, Libratus ran on the newest supercomputer, called Bridges, at the Pittsburgh Supercomputing Center (PSC) The system is a unique configuration built for traditional types of applications, like simulating astrophysical phenomena, and for machine learning workloads, like Libratus. Bridges was built by Hewlett Packard Enterprise (HPE), using Intel® Xeon® processors and Intel® Omni-Path Architecture (Intel® OPA), a new networking technology from Intel that is now being used in the world’s fastest supercomputers. Bridges comprises 846 nodes of computing power, with both “regular memory” nodes and “high-memory” nodes, the latter often used in. WIRED. Retrieved 2017-01-24.

[4] Ibid.

[5] Mbb/g is a normalizing measure of well a player plays.

# # #

Libratus Poker Air 2

This article was produced as part of Intel’s High Performance Computing (HPC) editorial program, with the goal of highlighting cutting-edge science, research and innovation driven by the HPC community through advanced technology. The publisher of the content has final editing rights and determines what articles are published.

Libratus is an artificial intelligence computer program designed to play poker, specifically heads up no-limitTexas hold 'em. Libratus' creators intend for it to be generalisable to other, non-Poker-specific applications. It was developed at Carnegie Mellon University, Pittsburgh.

Background[edit]

While Libratus was written from scratch, it is the nominal successor of Claudico. Like its predecessor, its name is a Latin expression and means 'balanced'.

Libratus was built with more than 15 million core hours of computation as compared to 2-3 million for Claudico. The computations were carried out on the new 'Bridges' supercomputer at the Pittsburgh Supercomputing Center. According to one of Libratus' creators, Professor Tuomas Sandholm, Libratus does not have a fixed built-in strategy, but an algorithm that computes the strategy. The technique involved is a new variant of counterfactual regret minimization,^[1] namely the CFR+ method introduced in 2014 by Oskari Tammelin.^[2] On top of CFR+, Libratus used a new technique that Sandholm and his PhD student, Noam Brown, developed for the problem of endgame solving. Their new method gets rid of the prior de facto standard in Poker programming, called 'action mapping'.

As Libratus plays only against one other human or computer player, the special 'heads up' rules for two-player Texas hold 'em are enforced.

2017 humans versus AI match[edit]

From January 11 to 31, 2017, Libratus was pitted in a tournament against four top-class human poker players,^[3] namely Jason Les, Dong Kim, Daniel McAulay and Jimmy Chou. In order to gain results of more statistical significance, 120,000 hands were to be played, a 50% increase compared to the previous tournament that Claudico played in 2015. To manage the extra volume, the duration of the tournament was increased from 13 to 20 days.

The four players were grouped into two subteams of two players each. One of the subteams was playing in the open, while the other subteam was located in a separate room nicknamed 'The Dungeon' where no mobile phones or other external communications were allowed. The Dungeon subteam got the same sequence of cards as was being dealt in the open, except that the sides were switched: The Dungeon humans got the cards that the AI got in the open and vice versa. This setup was intended to nullify the effect of card luck.

The prize money of $200,000 was shared exclusively between the human players. Each player received a minimum of $20,000, with the rest distributed in relation to their success playing against the AI. As written in the tournament rules in advance, the AI itself did not receive prize money even though it won the tournament against the human team.

During the tournament, Libratus was competing against the players during the days. Overnight it was perfecting its strategy on its own by analysing the prior gameplay and results of the day, particularly its losses. Therefore, it was able to continuously straighten out the imperfections that the human team had discovered in their extensive analysis, resulting in a permanent arms race between the humans and Libratus. It used another 4 million core hours on the Bridges supercomputer for the competition's purposes.

Strength of the AI[edit]

Libratus had been leading against the human players from day one of the tournament. The player Dong Kim was quoted on the AI's strength as follows: 'I didn’t realize how good it was until today. I felt like I was playing against someone who was cheating, like it could see my cards. I’m not accusing it of cheating. It was just that good.'^[4]

At the 16th day of the competition, Libratus broke through the $1,000,000 barrier for the first time. At the end of that day, it was ahead $1,194,402 in chips against the human team. At the end of the competition, Libratus was ahead $1,766,250 in chips and thus won resoundingly. As the big blind in the matches was set to $100, Libratus winrate is equivalent to 14.7 big blinds per 100 hands. This is considered an exceptionally high winrate in poker and is highly statistically significant.^[5]

Of the human players, Dong Kim came first, MacAulay second, Jimmy Chou third, and Jason Les fourth.

Other possible applications[edit]

While Libratus' first application was to play poker, its designers have a much broader mission in mind for the AI.^[6] The investigators designed the AI to be able to learn any game or situation in which incomplete information is available and 'opponents' may be hiding information or even engaging in deception. Because of this Sandholm and his colleagues are proposing to apply the system to other, real-world problems as well, including cybersecurity, business negotiations, or medical planning.^[7]

See also[edit]

References[edit]

1. ^Hsu, Jeremy (10 January 2017). 'Meet the New AI Challenging Human Poker Pros'. IEEE Spectrum. Retrieved 2017-01-15.
2. ^Brown, Noam; Sandholm, Tuomas (2017). 'Safe and Nested Endgame Solving for Imperfect-Information Games'(PDF). Proceedings of the AAAI workshop on Computer Poker and Imperfect Information Games.
3. ^Spice, Byron; Allen, Garrett (January 4, 2017). 'Upping the Ante: Top Poker Pros Face Off vs. Artificial Intelligence'. Carnegie Mellon University. Retrieved 2017-01-12.
4. ^Metz, Cade (24 January 2017). 'Artificial Intelligence Is About to Conquer Poker—But Not Without Human Help'. Wired. Retrieved 2017-01-24.
5. ^'Libratus Poker AI Beats Humans for $1.76m; Is End Near?'. PokerListings. 30 January 2017. Retrieved 2018-03-16.
6. ^Knight, Will (January 23, 2017). 'Why it's a big deal that AI knows how to bluff in poker'. MIT Technology Review.
7. ^'Artificial Intelligence Wins $800,000 Against 4 Poker Masters'. Interesting Engineering. 27 January 2017.

External links[edit]

• Brains versus Artificial Intelligence official website at the Rivers Casino

Libratus Poker Air Purifier