The night before his newest poker competition was set to begin, Carnegie Mellon’s Tuomas Sandholm and his PhD student Noam Brown sat down to play a little No Limit Texas Hold’em against the main competition: the artificial intelligence program they designed called “Libratus.”
“I was totally wrecked,” Sandholm told The Washington Post. The machine destroyed him. But he is not a serious poker player, so that’s not such a big achievement.
But for the past 13 days, however, Libratus has been facing off against four world-champion poker players in a Pittsburgh casino. If it can beat them like it beat Sandholm, it would be an enormous breakthrough.
So far, after 67,000 hands, Libratus has won $701,242 worth of chips after starting from a balance of zero. That means, of course, that the champions have lost that same amount, $701,242. (They’re not playing with real money but rather for a lump-sum prize of $200,000 that will divide at the end of the tournament.)
There are 53,000 hands left to play and if this trend continues, it will be the first time that AI has beaten humans at poker.
That would be a huge achievement. Poker is not like other games, such as chess, where AI has emerged victorious thanks to advanced algorithms. Poker is much harder for AI. As the MIT Technology Review explained:
“Poker requires reasoning and intelligence that has proven difficult for machines to imitate. It is fundamentally different from checkers, chess, or Go, because an opponent’s hand remains hidden from view during play. In games of ‘imperfect information,’ it is enormously complicated to figure out the ideal strategy given every possible approach your opponent may be taking. And no-limit Texas Hold’em is especially challenging because an opponent could essentially bet any amount.”
“Libratus has had the lead since the outset,” Sandholm says.
Monday, on the tail end of Day 13, four poker players, Jimmy Chou, Dong Kim, Jason Les, and Daniel McAulay, sat in the dimly-lit blue light of computer screens in Pittsburgh’s Rivers Casino, playing a virtual hand of cards against a virtual opponent.
For Sandholm, a computer scientist with a 126-page C.V., this is the culmination of twelve years of research. Starting in 2004 at Carnegie Mellon University, Sandholm began studying abstract algorithms for sequential imperfect information games. A “perfect” information game is one like chess, for example, where both players see the board and are in a good position to anticipate the opponent’s next possible move. An “imperfect information” game is one in which on each players’ turn they don’t know all the information available in the game – such as the other person’s cards.
Poker is an “imperfect information” game because players hide their hands, limiting the capacity of the opponent to calculate what their next move should be, thus allowing players to bluff.
The uses of the exercise go far beyond poker. War and cyberwar are both areas in which this could be useful.
Sandholm settled on No Limit Texas Hold’em poker as a model that could be extrapolated to real-life “imperfect” situations like cybersecurity or military strategy. He wanted a general purpose algorithm that would excel in strategic reasoning.