Our goal was to replicate Libratus from a article published in Science titled Superhuman AI for heads-up no-limit poker: Libratus beats top professionals. Die Mechanismen hinter dem KI-Bot, der ein Team aus Pokerpros vor knapp einem Jahr alt aussehen ließ, wurden nun in einem. Pokerstars chancenlos gegen "Libratus" Game over: Computer schlägt Mensch auch beim Pokern. Hauptinhalt. Stand: August ,
Das sind die Geheimnisse hinter dem Erfolg von LibratusDie "Brains Vs. Artificial Intelligence: Upping the Ante" Challenge im Rivers Casino in Pittsburgh ist beendet. Poker-Bot Libratus hat sich nach. Die Mechanismen hinter dem KI-Bot, der ein Team aus Pokerpros vor knapp einem Jahr alt aussehen ließ, wurden nun in einem. Das US-Verteidigungsministerium hat einen Zweijahresvertrag mit den Entwicklern der künstlichen Intelligenz (KI) „Libratus“ abgeschlossen.
Libratus Poker Mehr zum Thema VideoAI Poker Bots Are Beating The World's Best Players (HBO) bspice(through)barnadesigns.com Libratus, an artificial intelligence developed by Carnegie Mellon University, made history by defeating four of the world’s best professional poker players in a marathon day poker competition, called “Brains Vs. Artificial Intelligence: Upping the Ante” at Rivers Casino in Pittsburgh. Libratus emerged as the clear victor after playing more than , hands in a heads-up no-limit Texas hold ’em poker tournament back in February. The machine crushed its meatbag opponents by big blinds per game, drawing in $1,, in prize money. Now, a paper published in Science reveals how Libratus was programmed. The approach taken by its creators Noam Brown, a PhD student, and Tuomas Sandholm, a professor of computer science, both at Carnegie Mellon University in the US. Pitting artificial intelligence (AI) against top human players demonstrates just how far AI has come. Brown and Sandholm built a poker-playing AI called Libratus that decisively beat four leading. Libratus is an artificial intelligence computer program designed to play poker, specifically heads up no-limit Texas hold 'em. Libratus' creators intend for it to be generalisable to other, non-Poker-specific applications. It was developed at Carnegie Mellon University, Pittsburgh. While the first program, Claudico, was summarily beaten by human poker players —“one broke-ass robot,” an observer called it — Libratus has triumphed in a series of one-on-one, or heads-up, matches against some of the best online players in the United States. Libratus relies on three main modules. Libratus: The Superhuman AI for No-Limit Poker (Demonstration) Noam Brown Computer Science Department Carnegie Mellon University [email protected] Tuomas Sandholm Computer Science Department Carnegie Mellon University Strategic Machine, Inc. [email protected] Abstract No-limit Texas Hold’em is the most popular vari-ant of poker in the world. 12/10/ · In a stunning victory completed tonight the Libratus Poker AI, created by Noam Brown et al. at Carnegie Mellon University, has beaten four human professional players at No-Limit Hold'em. For the first time in history, the poker-playing world is facing a future of . 2/2/ · Künstliche Intelligenz: Poker-KI Libratus kennt kein Deep Learning, ist aber ein Multitalent Tuomas Sandholm und seine Mitstreiter haben Details zu ihrer Poker-KI Libratus veröffentlicht, die Reviews:
Oct 13, Major refactoring. May 31, View code. Deep mind pokerbot for pokerstars and partypoker This pokerbot plays automatically on Pokerstars and Partypoker.
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But the developers of the AI used the past two years to improve the program immensely - and their improvements were extraordinary. A re-match was scheduled against four of the best heads-up poker players.
Kim is a highly successful online high-stakes player; Les was twice in striking range of a WSOP bracelet in when he finished second and third in WSOP events; Chou won the Asia Championship of Poker one year ago and McAulay has won several hundred thousand dollars playing online tournaments.
It's a derivative of the Claudico AI which lost its challenge against the humans two years ago. This challenge lasted for , hands — 30, per player - and ran from January This ensured that every hand was played with a stack size of big blinds -- reasonably deep stacks for heads-up poker which allowed plenty of room for strategic moves in each hand.
To reduce the luck factor, which might heavily skew the results, two special rules were put in place:. All hands were mirrored.
For example: when Player A got aces vs. Thus no party could just run hot over the course of the challenge. No hard all-ins.
When a hand was all-in before the river no more cards were dealt and each player received his equity in chips.
This also reduced the luck factor. This equates to a win rate of All four human players lost over their 30, hands against Libratus.
This is how they performed individually:. While the rules of the challenge were set to reduce the luck factor as much as possible, chance still plays a big role in the results of each hand — even with mirrored hands and even with the elimination of all-in luck.
So maybe, just maybe, the human players are actually better but the AI just got lucky. Let's look at some statistics regarding the results. The AI won with a win rate of Those are just rough estimates for the variance, but as we'll see they're good enough boundaries.
What's the probability of the humans actually playing better than the AI but losing at a rate of It turns out this probability is very low: Somewhere between 0.
Meaning: It's very, very unlikely the general result of this challenge — the AI plays better than four humans — is due to the AI just getting lucky.
Thus, it is guaranteed that the new strategy is no worse than the current strategy. This approach, if implemented naively, while indeed "safe", turns out to be too conservative and prevents the agent from finding better strategies.
The new method  is able to find better strategies and won the best paper award of NIPS In addition, while its human opponents are resting, Libratus looks for the most frequent off-blueprint actions and computes full solutions.
Thus, as the game goes on, it becomes harder to exploit Libratus for only solving an approximate version of the game.
While poker is still just a game, the accomplishments of Libratus cannot be understated. Bluffing, negotiation, and game theory used to be well out of reach for artificial agents, but we may soon find AI being used for many real-life scenarios like setting prices or negotiating wages.
Soon it may no longer be just humans at the bargaining table. Correction: A previous version of this article incorrectly stated that there is a unique Nash equilibrium for any zero sum game.
The statement has been corrected to say that any Nash equilibria will have the same value. Thanks to Noam Brown for bringing this to our attention. Citation For attribution in academic contexts or books, please cite this work as.
If you enjoyed this piece and want to hear more, subscribe to the Gradient and follow us on Twitter. Brown, Noam, and Tuomas Sandholm.
Mnih, Volodymyr, et al. Silver, David, et al. Bowling, Michael, et al. Libratus: the world's best poker player Dong Kim, one of the professionals that Libratus competed against.
Theory of Games The poker variant that Libratus can play, no-limit heads up Texas Hold'em poker, is an extensive-form imperfect-information zero-sum game.
A normal form game For our purposes, we will start with the normal form definition of a game. The Nash equilibrium Multi-agent systems are far more complex than single-agent games.
Who knows. Perhaps that all they could do out of frustration with the ai super computer beating them down continuously. Because these tournament poker players playing against Libratus were adaptive and winning online poker players and always used huds to win online themselves against other players.
They noticed a big hole in their abilities when they did not have a hud against Libratus to help guide them like they were used to using against other human players.
Yet Libratus is one giant poker player HUD in of itself. It analyzed its own play and found its own holes as well as collecting stats and information on the human Poker players it played against.
Therefore Poker Huds offer an unfair advantage to those that have and use them vs. If you play poker online you may have one already. Libratus was built with more than 15 million core hours of computation as compared to million for Claudico.
The computations were carried out on the new 'Bridges' supercomputer at the Pittsburgh Supercomputing Center.
According to one of Libratus' creators, Professor Tuomas Sandholm, Libratus does not have a fixed built-in strategy, but an algorithm that computes the strategy.
Their new method gets rid of the prior de facto standard in Poker programming, called "action mapping".
As Libratus plays only against one other human or computer player, the special 'heads up' rules for two-player Texas hold 'em are enforced.
To manage the extra volume, the duration of the tournament was increased from 13 to 20 days. The four players were grouped into two subteams of two players each.
One of the subteams was playing in the open, while the other subteam was located in a separate room nicknamed 'The Dungeon' where no mobile phones or other external communications were allowed.