By MICHAEL KAPLAN
Published: September 5, 2013
Stroll among the games at the Cosmopolitan, the newest casino on the Las Vegas Strip, and you might be overwhelmed by the latest whooping and flashing gambling machines. All the high-resolution monitors and video effects, devoted to themes ranging from deep-sea-fishing expeditions to Spider-Man to the unsubtlest visions of cash washing over lucky winners, are only the most obvious signs of technology’s move onto the casino floor. Behind the scenes, server-based gaming now enables managers to rapidly alter payouts, raise or reduce betting minimums, even change games themselves. (In just minutes, a bank of slot machines styled for dance clubbers can be rethemed to appeal to church ladies on a Sunday afternoon.) But a few deceptively prim-looking machines represent an even greater technological leap, the biggest advance in automated gambling since Charles Fey introduced the one-armed bandit in 1895. They owe the way they play to artificial intelligence.
The machines, called Texas Hold ‘Em Heads Up Poker, play the limit version of the popular game so well that they can be counted on to beat poker-playing customers of most any skill level. Gamblers might win a given hand out of sheer luck, but over an extended period, as the impact of luck evens out, they must overcome carefully trained neural nets that self-learned to play aggressively and unpredictably with the expertise of a skilled professional. Later this month, a new souped-up version of the game, endorsed by Phil Hellmuth, who has won more World Series of Poker tournaments than anyone, will have its debut at the Global Gaming Expo in Las Vegas. The machines will then be rolled out into casinos around the world.
They will be placed alongside the pure numbers-crunchers, indifferent to the gambler. But poker is a game of skill and intuition, of bluffs and traps. The familiar adage is that in poker, you play the player, not the cards. This machine does that, responding to opponents’ moves and pursuing optimal strategies. But to compete at the highest levels and beat the best human players, the approach must be impeccable. Gregg Giuffria, whose company, G2 Game Design, developed Texas Hold ‘Em Heads Up Poker, was testing a prototype of the program in his Las Vegas office when he thought he detected a flaw. When he played passively until a hand’s very last card was dealt and then suddenly made a bet, the program folded rather than match his bet and risk losing more money. “I called in all my employees and told them that there’s a problem,†he says. The software seemed to play in an easily exploitable pattern. “Then I played 200 more hands, and he never did anything like that again. That was the point when we nicknamed him Little Bastard.â€
Illustration by Tim Enthoven
The pokerbot, which takes on one challenger at a time, can trace its roots to the Norwegian Defense Research Establishment in Kjeller, Norway. Until 2002, that’s where an engineer named Fredrik Dahl worked on artificial intelligence for secret government projects on combat simulations. The job involved using neural networks. Functioning much like an extremely focused, one-dimensional version of the human brain, these complex computer algorithms develop strategies that emerge through so many repetitive mathematical calculations that few humans could reproduce, much less endure them. Dahl’s work on two-sided, zero-sum games, where there is no mutual interest, proved to be useful in developing strategies to win not only wars but also poker games.
He started with backgammon, though. While a student at the University of Oslo, where he concentrated on computer science, he developed a penchant for the game. He once made it to the finals of the Norwegian National Backgammon Championships. “One thing I learned from backgammon is how to handle losses, no matter how well I play,†he says. “It is not a good game for sore losers.â€
Rather than be sore, he used computers to improve his play. Dahl created a neural net that predicted the probability of winning a backgammon match from each position on the board, at every possible stage in a game. Any individual situation is easy enough to solve, Dahl says; the challenge was determining all possible situations, giving value to the importance of each one and choosing a play. “The program needed to self-train and discover these strategies itself,†Dahl says.
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