Die Mechanismen hinter dem KI-Bot, der ein Team aus Pokerpros vor knapp einem Jahr alt aussehen ließ, wurden nun in einem. 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. Das US-Verteidigungsministerium hat einen Zweijahresvertrag mit den Entwicklern der künstlichen Intelligenz (KI) „Libratus“ abgeschlossen.
Pentagon zahlt 10 Mio. US-Dollar für Poker spielende KIPokerstars chancenlos gegen "Libratus" Game over: Computer schlägt Mensch auch beim Pokern. Hauptinhalt. Stand: August , 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 Navigation menu VideoJason Les Discusses Playing AI Poker Bot Liberatus 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:
Right now Libratus is just the beginning. The AI still simplifies many different poker situations. For example it might not differentiate between a king-jack high flush-draw and a king-ten high flush-draw.
But Libratus is already close to having developed a perfect strategy — at least close enough to annihilate any human counterpart. Libratus beat humans in No-Limit Heads-Up.
Two years ago the University of Alberta introduced Cepheus to the world -- a bot which, for all intents and purposes, plays a perfect Limit Heads-Up strategy.
It's safe to say that those two variants are practically solved. As a matter of fact the guys from the University of Alberta managed to prove that their bot is at worst 0.
Nash equilibrium strategy. While The No-Limit bot Libratus might be much further away from this perfect strategy, it's only a matter of time before it'll be refined and get closer to it.
What about other poker variants? Poker with more than two players is orders of magnitudes more complex than heads-up.
The same holds true for more difficult variants like Omaha. But a bot like Libratus is still so complex it requires a direct connection to its enormous super computer while playing.
And it still plays remarkably slow. So there's no direct danger of it being used in your local casino or online game. The scary fact is: Bots don't even have to play a perfect strategy.
And they don't have to beat the best players. To make an impact they just have to beat the average player. And there's bad news on that front: We're there already.
For virtually any poker game there already is a bot that plays better than the average, decent human player.
So while poker in general might not yet be solved in a theoretical sense, it's solved enough for a decent bot to beat a decent player. The same phenomena was visible when computer chess was developed.
In fact the first time a computer reached an ELO rating comparable to a master rank was in -- 16 years before the AI eventually beat the world champion.
IEEE Spectrum. Retrieved Artificial Intelligence". Carnegie Mellon University. MIT Technology Review.
Interesting Engineering. Categories : Computer poker players Carnegie Mellon University. Hidden categories: Articles with short description Short description is different from Wikidata.
Latest commit. Git stats commits. Failed to load latest commit information. Jun 1, Jun 14, Oct 13, Major refactoring.
May 31, View code. Deep mind pokerbot for pokerstars and partypoker This pokerbot plays automatically on Pokerstars and Partypoker.
Releases No releases published. Packages 0 No packages published. Contributors 7. In contrast, limit poker forces players to bet in fixed increments and was solved in .
Nevertheless, it is quite costly and wasteful to construct a new betting strategy for a single-dollar difference in the bet. Libratus abstracts the game state by grouping the bets and other similar actions using an abstraction called a blueprint.
In a blueprint, similar bets are be treated as the same and so are similar card combinations e. Ace and 6 vs. Ace and 5. The blueprint is orders of magnitude smaller than the possible number of states in a game.
Libratus solves the blueprint using counterfactual regret minimization CFR , an iterative, linear time algorithm that solves for Nash equilibria in extensive form games.
Libratus uses a Monte Carlo-based variant that samples the game tree to get an approximate return for the subgame rather than enumerating every leaf node of the game tree.
It expands the game tree in real time and solves that subgame, going off the blueprint if the search finds a better action.
Solving the subgame is more difficult than it may appear at first since different subtrees in the game state are not independent in an imperfect information game, preventing the subgame from being solved in isolation.
This decouples the problem and allows one to compute a best strategy for the subgame independently. In short, this ensures that for any possible situation, the opponent is no better-off reaching the subgame after the new strategy is computed.
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.
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.
Next time you go to reload cash in your poker account think about What I Just Said. Especially so in the shark filled waters of sites like Poker Stars.
Get Poker Tracker 4 and start using it to win, then add on to it for your niche, like sit n goes, tournaments, cash games… Do it seriously.
As Libratus shows computer software analyzing play is the way to get a jump on your opponents like this computer did against the non software using human opponents.