Poker Libratus
Tall and thin, with wire-frame glasses and neat brow hair framing a friendly face, Sandholm is behind the creation of three computer programs designed to test their mettle against human poker players: Claudico, Libratus, and most recently, Pluribus. (When we met, Libratus was still a toddler and Pluribus didn’t yet exist.). Poker playing robots – more realistically, poker programs that utilize artificial intelligence, or AI, to make their decision – have been under development for the last 20 or so years. To think of Libratus as just a poker-playing champ is to sorely underestimate it. Instead, Sandholm says, it's a more general set of algorithms meant to tackle any information-imperfect situation.
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Carnegie Mellon University’s Libratus, an artificial intelligence computer program designed to play poker, started the year by proving it could beat four human poker pros. Now, a pair of university researchers behind the program are ending the year by telling the world exactly how the AI program managed to do it.Libratus beat pros Jason Les, Dong Kim, Daniel McCauley and Jimmy Chou in a 20-day competition held in January at Rivers Casino in Pittsburgh, Pennsylvania. In fact, it beat each of the players at heads-up no-limit hold’em. Over 120,000 total hands, Libratus managed to end the sessions up more than $1.8 million in chips.
This week, Carnegie Mellon’s professor of computer science Tuomas Sandholm and Ph.D. student Noam Brown published an article in the research journal Science,detailing how it managed to do all that.
Libratus Poker Paper
According to the article, Libratus was programmed to use a three-pronged approach to the game of poker. Plus, it included more decision points than there are atoms in the universe.
- Brown and Sandholm built a poker-playing AI called Libratus that decisively beat four leading human professionals in the two-player variant of poker called heads-up no-limit Texas hold'em (HUNL).
- Libratus is an artificial intelligence computer program designed to play poker, specifically heads up no-limit Texas hold 'em. Libratus' creators intend for it to be generalisable to other, non-Poker-specific applications. It was developed at Carnegie Mellon University, Pittsburgh.
Libratus adjusted on the fly
Poker involves bluffing. So, the researchers said the program was designed to recognize and understand the tactic. It really went deeper than just taking a simple black and white approach to the game.
Sandholm and Brown claim Libratus was able to break poker down into computationally manageable parts. That way it could fix weaknesses in its strategy based on its opponents’ play. Essentially, Libratus did what every good poker player has done for decades: It adjusted to the strategies employed by its opponents on the fly.
Libratus’ three-pronged approach to the game included:
- Creating an abstract version of the game which was easier to solve
- Creating a more detailed plan-of-action based on how the game was playing out
- Improving on that plan in real time by detecting mistakes in its opponent’s strategy and exploiting them
Simply put, Libratus began with a basic strategy designed by looking at a simplified version of the game. That strategy became more complex as it learned how its opponents were playing. Finally, it adjusted the strategy even further to exploit weakness shown by its opponents.
If an opponent were to switch to a different strategy, Libratus also avoided opening itself up to exploitation by detecting potential holes in its own game.
Should bet sizing change, Libratus would add the missing decision branches and compute strategies for them. Then it would add those strategies to its plan going forward.
Libratus demoralizes opponents
After losing in January, Les described playing Libratus as a slightly demoralizing experience:
“Libratus turned out to be way better than we imagined. It’s slightly demoralizing. If you play a human and lose, you can stop, take a break. Here we have to show up to take a beating every day for 11 hours a day. It’s a real different emotional experience when you’re not used to losing that often.”
There may even be further reaching implications of Libratus’ success. Several bot rings employing AI have been discovered on online poker sites, including PokerStars. The success of Libratus could lead to an increase in the prevalence of bots online. However, this specific technology has yet to be tested in full-ring games.
Libratus Poker Algorithm
The future of AI
In the end, they built an artificial intelligence computer program that can beat the pros at poker. However, Sandholm and Brown say they are hoping the AI can ultimately do a lot more:
“The techniques that we developed are largely domain independent and can thus be applied to other strategic imperfect-information interactions, including non-recreational applications. Due to the ubiquity of hidden information in real-world strategic interactions, we believe the paradigm introduced in Libratus will be critical to the future growth and widespread application of AI.”
The technology behind Libratus has now been licensed to Sandholm’s company Strategic Machine. The company aims to apply strategic reasoning technologies to many different applications.
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.
| Name | Rank | Results (in chips) |
|---|---|---|
| Dong Kim | 1 | -$85,649 |
| Daniel MacAulay | 2 | -$277,657 |
| Jimmy Chou | 3 | -$522,857 |
| Jason Les | 4 | -$880,087 |
| Total: | -$1,766,250 |
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]
- ^Hsu, Jeremy (10 January 2017). 'Meet the New AI Challenging Human Poker Pros'. IEEE Spectrum. Retrieved 2017-01-15.
- ^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.
- ^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.
- ^Metz, Cade (24 January 2017). 'Artificial Intelligence Is About to Conquer Poker—But Not Without Human Help'. Wired. Retrieved 2017-01-24.
- ^'Libratus Poker AI Beats Humans for $1.76m; Is End Near?'. PokerListings. 30 January 2017. Retrieved 2018-03-16.
- ^Knight, Will (January 23, 2017). 'Why it's a big deal that AI knows how to bluff in poker'. MIT Technology Review.
- ^'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