But Polaris was not perfect – it occasionally lost. So in 2008, Las Vegas was a little shaken up when a computer program, Polaris, beat some of its best poker pros at Texas hold ’em. Not surprisingly, it’s these unknowns that make poker extremely challenging for computers to handle. A player doesn’t know what cards their opponent holds or what cards their opponent thinks they hold. But poker is an imperfect-information game. These games were solved by computers in 19 respectively. Examples are Connect Four and draughts – each player can see where all pieces lie on the board. “It will almost certainly leave human opponents in its wake,” says David Dowe, a machine learning and artificial intelligence expert at Monash University.Ĭomputer algorithms are very good at cracking those games where players know everything that has occurred before making their move – this is known in game theory as “perfect information”. It is the brainchild of Michael Bowling and colleagues at the University of Alberta who published the algorithm behind their winning result in Science.

But clever humans still had an edge when it came to poker – until now.Ī new program, Cepheus, plays the game so well you could play against it your whole life and, unless you were unbelievably lucky, still not finish on top. When it comes to games, machines have left us in the dust – we’ve been trounced at draughts, chess, even the TV quiz show Jeopardy.

Smith poker play in machine learning