Sometimes, poker is all about the bluff. Make the table believe you have a full house when you really have a low pair, and it can pay off big time. Read your opponents — a grimace here, a smirk there — and bet accordingly.
It’s not a skill you’d think computers would be particularly good at. But new research published in Science today shows that A.I. can learn to respond to fibs without needing to even see anyone else at the table, and outwit the best human poker players. It’s a development that may have implications far beyond the casino.
A poker-playing bot called Pluribus recently crushed a dozen top poker professionals at six-player, no-limit Texas Hold ’em over a 12-day marathon of 10,000 poker hands. Pluribus was created by Noam Brown, an A.I. researcher who now works at Facebook, and Tuomas Sandholm, a computer science professor at Carnegie Mellon University in Pittsburgh. (The two co-authored the paper in Science.)
If each chip in the experiment were worth a dollar, Pluribus would have made $1,000 an hour against the pros, according to Facebook, which published its own blog post on the research. (That haul greatly exceeds what experienced pros could expect, even playing at a table that included some amateurs.) Brown conducted most of his poker research while earning his master’s and PhD at Carnegie Mellon from 2012 to 2019, but he’s worked at Facebook for the last nine months and joined the company full-time in June — part of a wave of A.I. academics being hoovered up by tech companies.
“I think this is really going to be essential for developing A.I.s that are deployed in the real world.”
Cleaning up at the poker table isn’t the ultimate goal of Brown and Sandholm’s research, though. The game is really a simulator for how an algorithm could master a situation with multiple deceptive adversaries that hide information and are each trying to pressure the other to quit. A.I. can already calculate probability far better and…