The independent chip model (ICM) forms a cornerstone of all modern poker tournament strategy. However, despite its prominence, the ICM's performance in the real world has not been sufficiently scrutinized, especially at a large scale. In this paper, we introduce our new dataset of poker tournaments, consisting of results of over ten thousand events. Then, using this dataset, we perform two experiments as part of a large-scale empirical validation of the ICM. First, we verify that the ICM performs more accurately than a baseline we propose. Second, we obtain empirical evidence of the ICM underestimating the performances of players with larger stacks while overestimating those who are short-stacked. Our contributions may be useful to future researchers developing new algorithms for estimating a player's value in poker tournaments.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. A Florida man drew ire over the weekend when he entered and won a women's poker tournament at the Seminole Hard Rock Hotel & Casino in the Sunshine State. Dave Hughes, 70, entered the $250 no-limit Texas Hold'em event with a prize pool of up to $17,450. Of the 83 competitors to enter the tournament, 82 of them were women, and the last one was Hughes.

Parallel Algorithm for Approximating Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Strategic Planning Sam Ganzfried 1, Conner Laughlin 2, Charles Morefield 2 1 Ganzfried Research 2 Arctan, Inc. Abstract Many real-world domains contain multiple agents behaving strategically with probabilistic transitions and uncertain (potentially infinite) duration. Such settings can be modeled as stochastic games. While algorithms have been developed for solving (i.e., computing a game-theoretic solution concept such as Nash equilibrium) two-player zero-sum stochastic games, research on algorithms for nonzero-sum and multi-player stochastic games is very limited. We present a new algorithm for these settings, which constitutes the first parallel algorithm for multiplayer stochastic games. We present experimental results on a 4-player stochastic game motivated by a naval strategic planning scenario, showing that our algorithm is able to quickly compute strategies constituting Nash equilibrium up to a very small degree of approximation. Introduction Nash equilibrium has emerged as the most compelling solution concept in multiagent strategic interactions. For two-player zero-sum (adversarial) games, a Nash equilibrium can be computed in polynomial time (e.g., by linear programming). This result holds both for simultaneous-move games (often represented as a matrix), and for sequential games of both perfect and imperfect information (often represented as an extensive-form game tree).

NEW YORK Artificial intelligence has made history by beating humans in poker for the first time, the last remaining game in which humans had managed to maintain the upper hand. Libratus, an AI built by Carnegie Mellon University racked up over $1.7 million worth of chips against four of the top professional poker players in the world in a 20-day marathon poker tournament that ended on Tuesday in Philadelphia. While machines have beaten humans over the last two decade in chess, checkers, and most recently in the ancient game of Go, Libratus' victory is significant because poker is an imperfect information game -- similar to the real world where not all problems are laid out and the difficulty in figuring out human behavior is one of the main reasons why it was considered immune to machines. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," said Tuomas Sandholm, professor of computer science at CMU who created Libratus with a Ph.D student Noam Brown said on Wednesday. The victory prompted inquiries from companies all over the world seeking to use Libratus' algorithm for problem solving.

Carnegie Mellon's No-Limit Texas Hold'em software made short work of four of the world's best professional poker players in Pittsburgh at the grueling "Brains vs. Artificial Intelligence" poker tournament. Poker now joins chess, Jeopardy, go, and many other games at which programs outplay people. But poker is different from all the others in one big way: players have to guess based on partial, or "imperfect" information. "Chess and Go are games of perfect information," explains Libratus co-creator Noam Brown, a Ph.D. candidate at Carnegie Mellon. "All the information in the game is available for both sides to see. Poker is a game of imperfect information, since neither player can see their opponent's cards," he writes in an email to The Christian Science Monitor.

The world's best artificial intelligence poker player seems to know exactly when to hold'em and when to fold'em. An artificial-intelligence program known as Libratus has beat the world's absolute best human poker players in a 20-day No-Limit Texas Hold'em tournament, defeating four opponents by about $1.77 million in poker chips, according to Pittsburgh's Rivers Casino, where the "Brains vs. Artificial Intelligence" poker tournament was held. At the end of each day, at least one of the human players was beating the AI program. But in the end, it was not enough. "We appreciate their hard work, but unfortunately, the computer won," said Craig Clark, general manager of Rivers Casino.

Imagine your smartphone being able to negotiate the best price of a new car for you -- that's one of the potential implications of artificial intelligence beating humans in poker for the first time, experts say. Libratus, an AI built by Carnegie Mellon University (CMU), racked up over $US1.7 million ($2.2 million) worth of chips against four of the top professional poker players in the world in a 20-day marathon poker tournament that ended in Philadelphia on Tuesday.. While machines have beaten humans over the last two decade in chess, checkers, and most recently in the ancient game of Go, Libratus' victory is significant because poker is an imperfect information game -- similar to the real world where not all problems are laid out. The difficulty in figuring out human behaviour is one of the main reasons why poker was considered immune to machines. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," said Tuomas Sandholm, professor of computer science at CMU who created Libratus with a PhD student Noam Brown.

Artificial intelligence has enjoyed some remarkable online-gaming victories in recent years, though usually in slower games with clear--if incredibly multi-threaded--rule systems. On Monday, the robots pushed ahead with a slightly more remarkable online-gaming victory over their puny human masters when an AI program won big at Texas Hold'Em poker. A lengthy tournament of Hold'Em--specifically, the heads-up, no-limit variety--ended with victory for Libratus, an AI program developed by a professor and PhD candidate at Carnegie Mellon University. Libratus emerged victorious after 120,000 combined hands of poker played against four human online-poker pros. Libratus' $1.7 million margin of victory, combined with so many hands, clears the "Brains Vs.

An AI just claimed another gaming victory over humans by winning a 20-day poker tournament. The AI, called Libratus, took on four of the world's best Heads-Up No-Limit Texas Hold'Em poker players at a Pennsylvania casino. After 120,000 hands, Libratus won with a lead of over $1.7 million in chips. "I'm feeling great," says Tuomas Sandholm, a computer scientist at Carnegie Mellon University who was part of the team that created the AI. "This is a David versus Goliath story, and Libratus was able to throw a pebble."

Computers are now defeating us in casino table games. Libratus, an Artificial Intelligence system developed by researchers from Carnegie Mellon University has been squaring off against four professional poker players since Jan 11. Libratus is up nearly $800,000 through 80,000 hands of heads-up no limit Texas Hold'em against the four pros. "It's not about the money," said Jason Les, a professional from Costa Mesa, CA, in an interview with The Verge. With 40,000 hands left to play, the $800,000 deficit is nearly impossible for the humans to make up.