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 Welcome to
the News desk.
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| Poker Bot Libratus crushes Professional Poker
Players |
01/02/17 |
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 Editor |
• Astounding win
rate of 14.7 big blinds per 100 hands
Artificially intelligent, poker-playing software developed at
Carnegie Mellon University, challenged some of the game's best human players to
a rematch over a 20 day tournament that involved 120,000 hands. Libratus, a
computer program developed by Tuomas Sandholm, professor of computer science at
CMU, and Ph.D. student Noam Brown, took on 4 poker pros at Rivers Casino on
Pittsburgh's North Shore, Jan. 11-30, 2017 for $200,000.
The
competition, called Brains vs. Artificial Intelligence: Upping the Ante, took
place between the new AI poker bot Libratus (latin: 'balanced') and Jason Les,
Dong Kim, Daniel McAulay and Jimmy Chou, considered to be 4 top professionals.
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.
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For this rematch the
four players were split into two pairs, one pair playing in the open and the
other in a separate room with no external communication. The hands that
Libratus received in the open match were played by the humans in the hidden
match to reduce variation based on the quality of the cards dealt.
The
humans teamed up at the start of the tournament with a collective plan of each
trying different ranges of bet sizes to probe for weaknesses in the Libratus
AI’s strategy that they could exploit. During each night of the
tournament, they gathered together back in their hotel rooms to analyze the
day’s worth of plays and talk strategy.
The human strategy of
playing weird bet sizes had its greatest success in the first week, even if the
AI never lost its lead from the beginning. Libratus held a growing lead of
$193,000 in chips by the third day, but the poker pros narrowed the AI’s
lead by clawing back $42,201 in chips on the fourth day. After losing an
additional $8,189 in chips to Libratus on the fifth day, the humans scored a
sizable victory of $108,775 in chips on the sixth day and cut the AI’s
lead to just $50,513.
But Libratus struck back by winning $180,816 in
chips on the seventh day. After that, the “wheels were coming off the
wagon” for the human poker pros, Sandholm says. They noticed that Libratus
seemed to become especially unbeatable toward the last of the four betting
rounds in each game, and so they tried betting big up front to force a result
before the fourth round. They speculated on how much Libratus could change its
strategy within each game. But victory only seemed to slip further
away.
Sadly for the humans the machine kept on learing and getting
better. Late each day, after the poker play ended, Mr. Brown connected Libratus
to the Pittsburgh Supercomputer Center’s Bridges computer to run
algorithms to improve its strategy overnight. In the morning he would spend two
hours getting the newly enhanced bot back up and running.
In the end
Libratus was ahead $1,766,250 and with big blinds of $100 that translates into
14.7 big blinds per 100 hands, a crushing result in any analysis. |
| |
| Player |
Position |
Result |
| Dong Kim |
1 |
-$85,649 |
| Daniel MacAulay |
2 |
-$277,657 |
| Jimmy Chou |
3 |
-$522,857 |
| Jason Les |
4 |
-$880,087 |
| |
|
-$1,766,250 |
|
| |
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 Libratus.
Despite the historic victory over humans, AI
still has a ways to go before it can claim to have perfectly solved heads-up,
no-limit Texas Hold’em. That’s because the computational power
required to solve the game is still far beyond even the most powerful
supercomputers. The game has 10160 possible
plays at different stages, more than the number of atoms in the observable
universe, which is around 1082.
However as the tournament came to an end
many a viewer on the Twitch live-stream expressed their concern with comments
such as “Dude poker is dead!!!!!!!!!!!” before adding “RIP
poker.” Brown tried to reassure the Twitch chat that invincible
poker-playing bots probably would not be flooding online poker play anytime
soon.
“People are worried that my work here has killed poker: I
hope it has done the exact opposite,” Sandholm said. “I think of
Poker and no limit [Texas hold’em] as a recreational intellectual endeavor
in much the same way as composing a symphony or performing ballet or playing
chess.”
But for the Carnegie Mellon University staff this was not
just about poker. The algorithms that power Libratus aren’t specific to
poker, which means the system could have a variety of applications outside of
recreational games, from negotiating business deals to setting military or
cybersecurity strategy and planning medical treatment – anywhere where
humans are required to do strategic reasoning with imperfect
information.
“Poker is the least of our concerns here,” said
Roman V Yampolskiy, a professor of computer science at the University of
Louisville. “You have a machine that can kick your ass in business and
military applications. I’m worried about how humanity as a whole will deal
with that.”
For Brown, Libratus challenges preconceptions about
machine intelligence versus human intelligence.
“People have this
idea that poker is a very human game and that bots can’t bluff, for
example. That’s totally wrong. It’s not about reading your opponent
and trying to tell if they are lying, it’s about the cards and
probabilities,” he said.
Most online poker players have nothing to
fear from Libratus right now. The system only works in Heads Up poker, where
only two players are involved. A game with three players or more would be too
computationally intensive, and require a totally different strategy and
algorithmic approach.
Note : Throughout the competition, CMU said
Libratus recruited the raw power of approximately 600 of Bridges' 846 compute
nodes. Bridges' total speed is 1.35 petaflops -- approximately 7,250 times as
fast as a high-end laptop -- with its memory coming in at 274 terabytes, about
17,500 as much as you’d get in that laptop. |
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