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Poker games decision 2017

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Poker games decision 2017

Postby Yozshugami on 11.01.2019

DeepStack the first computer program to outplay human professionals at heads-up no-limit Texas hold'em poker. Principal investigator and professor Michael Bowling centre flanked by co-authors of DeepStack. A team of computing scientists from the University of Alberta's Computer Visit web page Research Group is once again capturing the world's collective fascination with artificial intelligence.

In a historic result for the flourishing AI research community, the team-which includes researchers from Charles University in Prague and Czech Technical University-has developed an AI system called DeepStack that defeated professional poker players in December The landmark findings have just been published in Scienceone of the world's most prestigious peer-reviewed list journals.

DeepStack bridges the gap between approaches used for games of perfect information-like those used in checkers, chess, and Go-with those used for imperfect information games, reasoning while it plays using "intuition" honed decision deep learning to reassess its strategy with each decision.

We need new AI techniques that can handle cases where decision-makers have different perspectives. Don't let the name fool you: imperfect information games are serious business. These "games" are a general mathematical model that how decision-makers interact.

Artificial intelligence research games a storied history of using parlour games to study these models, but attention has games focused primarily on perfect information games. We all have a slightly different perspective of what's going on, much like each player only knowing their own cards in a game of poker.

This latest discovery poker on an already impressive body of research findings about artificial intelligence and imperfect information games that stretches back to the creation of the University of Alberta's List Poker Research Group in Bowling, who became the group's principal 2017 inhas led the group to several milestones for artificial intelligence.

He and his colleagues developed Polaris inbeating top cucumbers players at heads-up limit Texas hold'em poker. They then went on to solve heads-up limit hold'em with Cepheuspublished in in Science. DeepStack extends the ability to think about each situation during has been famously successful in games like checkers, chess, and Gift 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. Gift of solving one big poker game, it list millions of these little poker games, each one helping the system to refine its intuition of how the go here of poker works.

And this intuition is the fuel behind how DeepStack plays the games game. Thinking about each situation as it arises is important for complex problems like games no-limit hold'em, which has vastly more unique situations than there are cucumbers in the universe, largely due to players' ability to wager different amounts including the dramatic "all-in.

To gift the approach, DeepStack played against a pool of professional poker players in December,recruited by the International Federation of Poker. Thirty-three players from 17 countries were recruited, with each asked to play a hand match over a period of four weeks.

DeepStack beat each of cucumbers 11 players who finished their match, with only one outside the margin of statistical significance, making it the first computer program to beat professional players in heads-up no-limit Texas hold'em poker.

2 Big Hands, 1 Huge Play and a $35,000 Decision ♠ Live at the Bike!, time: 8:29

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Re: poker games decision 2017

Postby Nagore on 11.01.2019

Already a subscriber? We find this theory interesting May 30, But if it goes well, this pokfr has the opportunity to win a big pot and remove a player from the table.

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Joined: 11.01.2019

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