Two Carnegie Mellon researchers, Tuomas Sandholm, a professor of computer science at CMU, and PhD student Noam Brown, have developed an AI called Libratus, which has recently beaten the world’s top players outside of Hold ‘Em, a poker version that allows for any bet on any time. No machine has ever been able to beat people in this unusual card game. AI programs have beaten the best players in checkers, chess, Othello, and the Japanese game strategy game, but there is no limit to Hold ‘Em with a different obstacle: Poker is a game of’ incomplete knowledge ‘. With so many cards hidden, without skill, some luck is inevitably involved.
This is not the only AI that attempts to deal with poker, and moreover, real-world problems involving incomplete details. A team of rival investigators at the University of Alberta have published a paper claiming that their AI, DeepStack, has already beaten the best poker players, using a completely different strategy. (This paper will be reviewed by peers from February 2017).
DeepStack uses deep neural networks to mimic human emotions, in a design like AlphaGo, Google’s Go-play AI. Although Go may be complicated, it still has, like chess, complete details.
Texas Hold ’em, by contrast, is a card game with incomplete details. The player is dealt with two “hole” cards that can only be seen by the player, before the three community cards face up at the table, followed by the fourth and fifth cards. Players can place bets after each clause of the contract, and unlimited Texas Hold ‘Em, can bet as much as they want in any category.
The strategy in pot limit omaha poker is to win as much money as possible, not with each hand. As the game progresses, it becomes a competition where players try to guess their opponents’ cards based on not only the latest bets, but on all bets made during the game by all players. There is also complete bluffing.
That’s why it’s so hard for AI to play poker. Libratus, which uses its statistics on a large computer at the Pittsburgh Supercomputing Center, has one advantage over humans: in seconds, it can ‘play’ tens of thousands of different game genres, and use this to find the best play.
DeepStack uses a different approach. He does not have to look all the way to the bottom. Instead, it uses a neural network to predict the outcome of each game. The Alberta team ‘trained’ this DeepStack net using thousands of poker scenarios, not just looking for cards but betting. In this way, the neural network ‘learns’ to judge which batteries will work best. It does not count all the possible effects of all hands, but it does make a quick estimate. DeepStack has beaten a lot of good players, but it has some way of beating up top players. Meanwhile, Libratus has captured the attention of the poker world.
Libratus has won more than $ US1.7 million compared to the four professional poker players in the world in a 20-day tournament that ended on 31 Jan 2017!
“AI’s excellent ability to generate strategic thinking with incomplete information has now been transferred to those excellent people,” said Professor Sandholm.
One of the main improvements compared to previous AI, Claudico, also from CMU, was able to lie Libratus.
Dong Kim, one of the four poker players who took part in the tournament also took part in the same tournament as Claudico in 2015.
Regarding the challenge from Libratus, Kim said, “It was almost part of overcoming the challenge. I knew we would never come back. There have been a few bugs in the algorithm. We just ran over to Claudico, touching him everywhere, but this time I felt like it was the other way around. ”
Against Claudico, human runners received $ US700,000 from more than 80,000 hands, beating them almost every day of the tournament. However, against Libratus, they won only five of the 20 days.
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