the News desk.
|Poker Bots take on the Professional Poker Players for
| Rematch over
120,000 hand of Heads-up No-limit Texas
Artificially intelligent, poker-playing software developed at
Carnegie Mellon University will challenge some of the game's best human players
to a rematch.
Four professional poker players will play 120,000 hand of
Heads-up No-limit Texas Hold'em poker against Libratus, a computer program
developed by Tuomas Sandholm, professor of computer science at CMU, and Ph.D.
student Noam Brown at Rivers Casino on Pittsburgh's North Shore starting Jan.
11, 2017 for $200,000.
CMU's computer software lost to four professional
players during the inaugural Brains Vs. Artificial Intelligence poker
tournament in 2015. The 80,000 hands played against a computer program named
Claudico weren't enough, however, to establish human or computer superiority
with statistical significance.
captured the worlds attention last year when it defeated humanitys
champion at the game of Go. It was a landmark event for AI, much like the
moment in 1997 when IBMs Deep Blue defeated Garry Kasparov at chess.
Starting next week an artificial intelligence system named Libratus, developed
by a team at Carnegie Mellon University, will try to establish a new milestone:
beating some of the best human players at Heads-Up No-Limit Texas Holdem
While Libratus may one day be listed in the history books
alongside Deep Blue and Alpha Go, its actually attempting to solve a very
different kind of problem. Go and chess are perfect-information games: each
player knows exactly what moves have been made and what space is left on the
board to consider. Poker is an imperfect-information game, which makes it far
more challenging for artificial intelligence to master.
complete information game you can solve a subtree of the game tree, says
Professor Tuomas Sandholm, who built the Libratus system with PhD student Noam
Brown. AI trying to win a game of chess or Go can work through how a sequence
of moves will play out. With incomplete-information games, its not
like that at all. You cant know what cards the other player has been
dealt, he explains. That means you dont know exactly what
subgame youre in. Also, you dont know which cards chance will
produce next from the deck.
Incomplete information games have thus
far proved much harder to solve. CMUs AI focuses on information sets, a
grouping of possible states that take into account the known and unknown
variables. Its a massive mathematical undertaking. The game has 10
to the power of 160 information sets, and 10 to the power of 165 nodes in the
game tree, says Sandholm. That means there are more possible permutations
in a hand of poker than atoms in our universe. And even if you had
another whole universe for each atom in our universe and counted all the atoms
in those universes, it would be more than that.
merely strategize many moves in advance, as AI might do when playing chess or
Go, the system built by CMU is looking to achieve the perfect balance of risk
and reward, a state of play defined by the Nash Equilibrium. You might be
familiar with this seminal piece of mathematics from the film A Beautiful Mind,
which chronicled the life of John Nash; he introduced the concept back in 1950.
It has since become a cornerstone of game theory, earning Nash a Nobel Prize in
For the humans matched against the machine, this approach produces
a relentless opponent. I always tell people the one word I can use to
describe the experience: a grind. The first few days we ended up playing til
midnight, and when we were done we went back to the hotel and studied for a few
hours before going to sleep. Then we would wake up at 9AM and do it all over
again, says Jason Les, a poker pro who played against CMUs prior AI
system during its first tournament, and who will be returning this
Sandholm point out that playing it safe is not the same as playing
conventionally. This poker program, and the Claudico program a year and
half ago, they come up with new moves. They play moves that established poker
literature considers really bad. For example in the first move in a hand
of poker, limping means you just call the opponent, you put in the minimum
amount of money to continue the hand. All the poker books say that is a
terrible move, but CMUs poker bots limp somewhere between 716
percent of the time.
That really contradicts the folk wisdom on
how to play this game, says Sandholm. The algorithms figure it out
just from the rules of the game, we dont give them any historical data
about how humans play. They play like Martians, they figure out their own
strategy. The AI also flouted convention by donk betting a lot, taking
the initiative from the player who placed the final wager in the previous
I think they show human players that they can make these
unconventional strategies work, says Les. However, in practice they
are too difficult to emulate without the help of a computer. Dong Kyu
Kim, who played against CMUs prior system in 2015, has adopted some of
its strange techniques. I have learned a lot from Claudico to use in my
own game, says Kim, who believes that following its lead can offer an
edge over many human opponents.
A team from the University of Alberta
built an AI system that was better than the best humans at limit
back in 2008, and achieved near perfect play at that variant of the game in
2015. No-Limit, where the size of bets are not constrained, is much more
complex, but all the poker pros involved in this tournament felt it was only a
matter of time before the machines would prevail.
I do not believe
that poker is different enough from chess and Go, and ultimately think that
computers will dominate the game, said Jimmy Chou. Humans may have
the upper-hand occasionally due to our unpredictable nature, but long-term I
will put my money on the effectiveness of machines due to math and
science. Kim agrees. I hate to admit it as a professional poker
player, but I do believe machines will be able to beat humans in all forms of
poker. It is just a matter of time.
While the triumphs of Deep
Blue and AlphaGo captured the public imagination, systems that solve perfect
information games have a limited application. Most real world
interactions include multiple parties and incomplete information, says
Sandholm. Crafting a system that can outperform humans at these types of tasks
will be, much more important from an AI perspective, and for making the
world better in general. AlphaGos creator has his eye on no-limit
poker and Starcraft II, both imperfect-information games.
will begin on January 11th at the Rivers Casino in Pittsburgh, Pennsylvania.
Four of the worlds top poker pros Jason Les, Dong Kim, Daniel
McAulay, and Jimmy Chou will collectively play 120,000 hands over the
course of the 20-day tournament, vying for a cut of the $200,000 prize purse.
If you want to tune in, live streams of the matches between Libratus and its
human opponents will be made available on Twitch as the tournament
You can follow the match live at the
Pittsburgh Casino live update page.