The current most popular variant of poker, played in casinos and seen on television, is no-limit Texas hold'em. This game and a smaller variant, limit Texas hold'em, have been used as a testbed for artificial intelligence research since 1997. Since 2006, the Annual Computer Poker Competition has allowed researchers, programmers, and poker players to play their poker programs against each other, allowing us to find out which artificial intelligence techniques work best in practice. The competition has resulted in significant advances in fields such as computational game theory, and resulted in algorithms that can find optimal strategies for games six orders of magnitude larger than was possible using earlier techniques.
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. 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. Last week, Google's AI subsidiary DeepMind announced it was opening its first international office in Edmonton, where Sutton -- alongside professors Michael Bowling and Patrick Pilarski -- will work part-time.
Over the past three weeks, an AI poker bot called Libratus has played thousands of games of heads-up, no-limit Texas hold'em against a cadre of top professional players at Rivers Casino in Pittsburgh. Poker requires reasoning and intelligence that has proven difficult for machines to imitate. Artificial intelligence has never beaten top players at a game so lacking in information as no-limit Texas hold'em. Still, given the progress machine learning is currently making, and the fact that other AI poker bots are also being developed, that seemingly impossible challenge may not remain impossible for long.
Participants in this year's edition of the poker extravaganza will see two changes: no firm "shot clock" and the return of the tradition of crowning the tournament's main event champion in July. Buy-ins for the 74-event tournament, which runs through July 22 at the Rio All-Suite Hotel and Casino, range from $333 to $111,111.
The University of Alberta's Computer Poker Research Group created DeepStack, an artificial intelligence program that defeated professional human poker players at heads-up, no-limit Texas hold'em. Apart from this win being the first of its kind, it bares significance in assisting to make better medical treatment recommendations to developing improved strategic defense planning, stated DeepStack: Expert-level artificial intelligence in heads-up no-limit poker, which was published in Science. In a similar case from May 11, 1997, Deep Blue, an IBM computer, outsmarted the world chess champion after six games –the computer had two wins, the champion won a single match, and there were three draws. The AI program was pitted against, "a pool of professional poker players recruited by the International Federation of Poker.
Doug Polk, one of the world's best poker players, shoveled egg whites into his mouth with a plastic fork and slurped unsweetened oatmeal from a paper cup, 13 days into the oddest tournament he has ever entered. His opponent, Claudico, did not struggle with fatigue, mental breakdown or hunger, despite... Doug Polk, one of the world's best poker players, shoveled egg whites into his mouth with a plastic fork and slurped unsweetened oatmeal from a paper cup, 13 days into the oddest tournament he has ever entered. The European Space Agency's Rosetta orbiter will commit operational suicide early Friday morning, but first it has just a little bit more science to do. The European Space Agency's Rosetta orbiter will commit operational suicide early Friday morning, but first it has just a little bit more science to do.
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. 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. Instead of solving one big poker game, it solves millions of these little poker games, each one helping the system to refine its intuition of how the game of poker works.
DeepStack's poker-playing success while running on fairly standard computer hardware could make it much more practical for AI to tackle many other "imperfect-information" situations involving business negotiations, medical diagnoses and treatments, or even guiding military robots on patrol. To train DeepStack's intuition, researchers turned to deep learning. A Carnegie Mellon University AI called Libratus achieved its statistically significant victory against four poker pros during a marathon tournament of 120,000 games total played in January 2017. But to achieve victory, Libratus still calculated its main poker-playing strategy ahead of time based on abstracted game solving--a computer- and time-intensive process that required 15 million processor-core hours on a new supercomputer called Bridges.
As Michael Bowling, co-author of the research and leader of the Computer Poker Research Group at Alberta, puts it: poker is the next big step for designing AI. In a game of Heads Up No Limit poker, DeepStack was able to win against professional poker players at a rate of 49 big blinds per 100. And that's what's powerful about deep learning, it can summarize its knowledge and make good choices looking ahead." They want to design the program to play with multiple players, which would expand the chance factor exponentially.
The 20-day poker tournament between four human pros and an artificial intelligence program concluded last night. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," said Libratus co-creator Tuomas Sandholm in a press release. "After play ended each day, a meta-algorithm analyzed what holes the pros had identified and exploited in Libratus' strategy," Sandholm said. Typically researchers develop algorithms that try to exploit the opponent's weaknesses.
Libratus, a powerful computer program, has crushed its human opponents at a heads-up no-limit Texas hold'em poker tournament held at Rivers Casino in Pittsburgh, Pennsylvania, winning $1,776,250 over 120,000 hands. The locked-away team are dealt the same cards at the open team but with places switched: the open team humans gets the locked-away AI's hole cards, the locked-away humans get the open AI's hole cards, and so on. The previous "Brains vs AI" poker match in 2015 saw Claudico, Libaratus' predecessor, lose to Dong Kim, Jason Les, Bjorn Li, and Doug Polk – the number one poker player at the time. Many researchers, including the team who recently published a paper on their own poker computer program DeepStack, use a technique called counterfactual regret minimization (CFR) to compute imperfect information games.