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VWN Printer Friendly News: Poker-playing AI program first to beat pros at no-limit Texas hold 'em

 Poker-playing AI program first to beat pros at no-limit Texas hold 'em
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Date posted: 02/03/2017
Posted by: Site Administration
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Artificial Intelligence

For the first time ever, an artificial intelligence program has beaten human poker professionals at heads-up, no-limit Texas hold ‘em.

It’s a historic result in artificial intelligence that has implications far beyond the poker table, from helping make more robust medical treatment recommendations to developing better strategic defence planning.

DeepStack, created by the University of Alberta's Computer Poker Research Group, bridges the gap between approaches used for games of perfect information—like those used in checkers, chess, and Go where both players can see everything on the board—with those used for imperfect information games by reasoning while it plays, using “intuition” honed through deep learning to reassess its strategy with each decision.

“Poker has been a long-standing challenge problem in artificial intelligence,” said computing scientist Michael Bowling, professor in the University of Alberta’s Faculty of Science and principal investigator on the study. “It is the quintessential game of imperfect information in the sense that the players don’t have the same information or share the same perspective while they’re playing.”

“We need new AI techniques that can handle cases where decision-makers have different perspectives,” said Bowling. “Think of any real-world problem. We all have a slightly different perspective of what’s going on, much like each player only knowing their own cards in a game of poker.”

DeepStack extends the ability to think about each situation during play to imperfect information games using a technique called continual re-solving. This allows DeepStack to determine the correct strategy for a particular poker situation by using its “intuition” to evaluate how the game might play out in the near future without thinking about the entire game.

“We train our system to learn the value of situations,” said Bowling. “Each situation itself is a mini poker game. Instead of solving one big poker game, it solves millions of these little poker games, each one helping the system to refine its intuition of how the game of poker works. And this intuition is the fuel behind how DeepStack plays the full game.”

Thinking about each situation as it arises is important for complex problems like heads-up no-limit hold’em, which has vastly more unique situations than there are atoms in the universe, largely due to players’ ability to wager different amounts including the dramatic “all-in.”

Despite the game’s complexity, DeepStack takes action at human speed—with an average of only three seconds of “thinking” time—and runs on a simple gaming laptop.

See the full Story via external site: www.ualberta.ca