Games have been used as a measure of AI capabilities for quite some time. Despite the idea starting off in the eighteenth century with fraudulent chess-playing machines, computer algorithm approaches started to pop up in the 1940s. Twenty years ago, IBM supercomputer Big Blue made headlines for beating reigning world champion Garry Kasparov in a set of matches.
Texas Hold 'Em poker represents a different kind of challenge than chess, since each player has access to only a limited part of the game's total state. Despite this challenge, researchers published work in 2015 claiming that an algorithm could defeat human players one-on-one in the limited-bet form of Texas Hold 'Em. Recently, a group of scholars announced DeepStack, an algorithm that can win one-on-one no-limit Texas Hold'em whilst harnessing the power of an Nvidia GeForce GTX 1080 graphics card.
The researchers used a number of heuristics to reduce the computational complexity of the game to a manageable level. The approach reviews each round as an independent game, discarding knowledge of previous moves. The computer also restricts itself to a limited number of different bets in every round in order to reduce possible future outcomes by fourteen orders of magnitude. The Nvidia GeForce GTX 1080 graphics card requires about five seconds to evaluate the reduced level of information revealed in one round of a game.
The researchers tested the approach against 33 real players in a tournament. Eleven of those played a full 3,000 games against Deep Stack, and the AI beat 10 of tem by a statistically-significant margin. Arstechnica has a more detailed overview of the algorithm here, for those interested in reading more in a friendlier format than the original Science article.
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