Facebook has achieved a major milestone in artificial intelligence (AI) thanks to one of its systems beating six professional poker players at no-limit Texas hold'em. The Pluribus AI defeated renowned players including Darren Elias, who holds the record for most World Poker Tour titles. Beating poker pros has been a major challenge for AI researchers, as the best players need to be good at bluffing and unpredictable. "Playing a six-player game rather than head-to-head requires fundamental changes in how the AI develops its playing strategy," said Noam Brown, a research scientist at Facebook AI. "We're elated with its performance and believe some of Pluribus's playing strategies might even change the way pros play the game." The breakthrough comes two years after an AI algorithm developed by Google-owned DeepMind helped a computer beat a human champion at the notoriously complicated board game Go for the first time.

While AI had some success at beating humans at other games such as chess and Go (games that follow predefined rules and aren't random), winning at poker proved to be more challenging because it requires strategy, intuition, and reasoning based on hidden information. Despite the challenges, artificial intelligence can now play--and win--poker. Artificial intelligence systems including DeepStack and Libratus paved the way for Pluribus, the AI that beat five other players in six-player Texas Hold'em, the most popular version of poker. This feat goes beyond games. This achievement means that artificial intelligence can now expand to help solve some of the world's most challenging issues.

While AI had some success at beating humans at other games such as chess and Go (games that follow predefined rules and aren't random), winning at poker proved to be more challenging because it requires strategy, intuition, and reasoning based on hidden information. Despite the challenges, artificial intelligence can now play--and win--poker. Artificial intelligence systems including DeepStack and Libratus paved the way for Pluribus, the AI that beat five other players in six-player Texas Hold'em, the most popular version of poker. This feat goes beyond games. This achievement means that artificial intelligence can now expand to help solve some of the world's most challenging issues.

While AI had some success at beating humans at other games such as chess and Go (games that follow predefined rules and aren't random), winning at poker proved to be more challenging because it requires strategy, intuition, and reasoning based on hidden information. Despite the challenges, artificial intelligence can now play--and win--poker. Artificial Intelligence Masters The Game of Poker – What Does That Mean For Humans? Artificial intelligence systems including DeepStack and Libratus paved the way for Pluribus, the AI that beat five other players in six-player Texas Hold'em, the most popular version of poker. This feat goes beyond games. This achievement means that artificial intelligence can now expand to help solve some of the world's most challenging issues.

Pluribus is the first AI bot capable of beating human experts in six-player no-limit Hold'em, the most widely played poker format in the world. This is the first time an AI bot has beaten top human players in a complex game with more than two players or two teams. We tested Pluribus against professional poker players, including two winners of the World Series of Poker Main Event. Pluribus succeeds because it can very efficiently handle the challenges of a game with both hidden information and more than two players. It uses self-play to teach itself how to win, with no examples or guidance on strategy. Pluribus uses far fewer computing resources than the bots that have defeated humans in other games. The bot's success will advance AI research, because many important AI challenges involve many players and hidden information. For decades, poker has been a difficult and important grand challenge problem for the field of AI. Because poker involves hidden information -- you don't know your opponents' cards -- success requires bluffing and other strategies that do not apply to chess, Go, and other games.

As the great Kenny Rogers once said, a good gambler has to know when to hold'em and know when to fold'em. At the Rivers Casino in Pittsburgh this week, a computer program called Libratus may finally prove that computers can do this better than any human card player. Libratus is playing thousands of games of heads-up, or two-player, no-limit Texas hold'em against several expert professional poker players. Now a little more than halfway through the 20-day contest, Libratus is up by almost $800,000 against its human opponents. So victory, while far from guaranteed, may well be in the cards. A win for Libratus would be a huge achievement in artificial intelligence.

There's a hall of champions at the University of Alberta that only computer science students know where to find -- more of a hallway, really, one office after the next, the achievements archived on hard drives and written in code. It's there you'll find the professors who solved the game of checkers, beat a top human player in the game of Go and used cutting-edge artificial intelligence to outsmart a handful of professional poker players for the very first time. But lately it's Richard Sutton who is catching people's attention on the Edmonton campus. He's a pioneer in a branch of artificial intelligence research known as reinforcement learning -- the computer science equivalent of treat-training a dog, except in this case the dog is an algorithm that's been incentivized to behave in a certain way. U of A computing science professors and artificial intelligence researchers (left to right) Richard Sutton, Michael Bowling and Patrick Pilarski are working with Google's DeepMind to open the AI company's first research lab outside the U.K., in Edmonton.

First they figured out how to play checkers and backgammon. Then they mastered chess, Go, "Jeopardy!" and even a few Atari video games. Now computers can challenge humans at the poker table -- and win. DeepStack, a software program developed at the University of Alberta's Computer Poker Research Group, took on 33 professional poker players in more than 44,000 hands of Texas hold'em. Overall, the program won by a significantly higher margin than if it had simply folded in each round, according to a new study in Science.

Computer researchers are betting they can take on the house after designing a new artificial intelligence program that has beat professional poker players. Researchers from University of Alberta, Czech Technical University and Charles University in Prague developed the "DeepStack" program as a way to build artificial intelligence capable of playing a complex kind of poker. Creating an AI program that can win against a human player in a no-limit poker game has long been a goal of researchers due to the complexity of the game. Michael Bowling, a professor in the Department of Computing Science in the University of Alberta, explained that computers have been able to win at "perfect" games such as chess or Go, in which all the information is available to both players, but that "imperfect" games like poker have been much harder to program for. "This game [poker] embodies situations where you find yourself not having all the information you need to make a decision," said Bowling.

A team of scientists has developed an artificial intelligence system called DeepStack that recently defeated professional poker players. The team of computing scientists from University of Alberta's Computer Poker Research Group, including researchers from Charles University in Prague and Czech Technical University, said DeepStack bridges the gap between approaches used for games of perfect information with those used for imperfect information games. "Poker has been a longstanding challenge in artificial intelligence," said Michael Bowling from the University of Alberta, Canada, in the paper published in the journal Science. It is the quintessential game of imperfect information in the sense that the players don't have the same information or share the same perspective while they are playing," Bowling added. Imperfect information games are a general mathematical model that describes how decision-makers interact. Artificial intelligence research has a storied history of using parlour games to study these models, but attention has been focused primarily on perfect information games. "We need new AI techniques that can handle cases where decision-makers have different perspectives," Bowling noted. DeepStack extends the ability to think about each situation during play -- which has been famously successful in games like checkers, chess, and Go -- to imperfect information games using a technique called continual re-solving. This allows DeepStack to determine the correct strategy for a particular poker situation without thinking about the entire game by using its "intuition" to evaluate how the game might play out in the near future.We train our system to learn the value of situations," Bowling said.