Tuomas Sandholm and Noam Brown spent the past year building an AI that plays Texas Hold ‘Em. The two Carnegie Melon researchers call their creation Libratus, and they believe it can top the world’s best players at no-limit Hold ‘Em, a version of the classic poker game that allows any bet at any time. No machine has ever reached such heights with this unusually complex game of cards. Although AI systems have topped the best players at checkers, chess, Othello, and even Go, no-limit Hold ‘Em creates a different obstacle. In contrast to those other games of intellect, a poker player can know only part of what’s happening during each hand. Poker is an imperfect information game. So many of the cards are hidden—and so much luck is involved.
To prove the powers of this new AI, the two researchers recently arranged for Libratus to challenge four of the world’s best players at a casino in Pittsburgh, not far from Carnegie Mellon, where Sandholm is a professor and Brown is a PhD student. Sandholm did much the same thing last year with another AI, and though his earlier attempt failed, as the machine’s opponents exploited particularly telling quirks in the way it played, he felt that his latest creation, drawing on more than a decade of research, had reached a new level of smarts that could finally eclipse human competition. Then, last week, just days before match, Sandholm was hit with some competition of a different kind. A rival team of researchers based at the University of Alberta published a paper claiming that their new AI, DeepStack, had already beaten some top human poker players.
As usual in the world of high-stakes AI research, it’s not just AI versus human. It’s AI versus AI. And it’s human versus human. Carnegie Mellon and Alberta have competed in poker AI for more than a decade, and now, they’re finally reaching the finish line.
The AlphaGo Analogy
At the moment, the end result of this multi-faceted competition is still in doubt. Led by University of Alberta professor Michael Bowling—a notable figure the recent AI revolution who did his PhD work at Carnegie Mellon—the Alberta team isn’t discussing its paper because, as one of Bowling’s students told us, it hasn’t yet been peer-reviewed. And as their rival Sandholm says, the paper doesn’t settle the matter because DeepStack merely played against good pokers layers, not great ones. But we’re certainly approaching a point where no-limit Texas Hold ‘Em—and similar imperfect information games—are finally cracked by artificial intelligence. Libratus started its match against four of the very best poker players on Wednesday—winning the both the first and second days—and this competition will play out by the end of the month.
What may be even more interesting, however, is that its rival, DeepStack, is successfully using deep neural networks to mimic the very human intuition that poker players rely on, echoing the design of AlphaGo, the AI that recently cracked the ancient game of Go, the most complex of the perfect information games. “It’s analogous to AlphaGo,” says University of Michigan professor Michael Wellman, who specializes in game theory and closely follows the world of AI poker. “They found a way to integrate…