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One of the most important strategies in poker is the art of bluffing, in which a player makes or raises a bet without having the best hand, in order to fool an opponent into folding. "People often think about bluffing as being a psychological phenomenon," Sandholm said. But beyond psychology, "bluffing still emerges as a strategic phenomenon," he said.

Sandholm and his colleagues didn't pre-program Claudico's poker strategy. They wrote algorithms that automatically compute a strategy by trying to find the Nash equilibrium. This concept from game theory was developed by American mathematician John Nash, who was portrayed in the film "A Beautiful Mind." In a noncooperative game, players are said to be in Nash equilibrium if they are making the best decision possible, taking into account the decisions of the other players.

31/05/2023

"I am guessing [a poker-playing AI] will be stronger than the best humans in the world in one to five years," said Tuomas Sandholm, a computer scientist at�Carnegie Mellon University in Pittsburgh, who led the team that created Claudico. But even as AI ability gets closer and closer to that of the best human players, it will take many more poker hands in order to truly declare the program victorious, Sandholm said.

One of AI's best-known achievements occurred when IBM's chess-playing Deep Blue computer beat reigning world champion Garry Kasparov. But unlike chess, poker is a game of incomplete information; players do not have knowledge about the payoffs and strategies of the other players. "Poker is much more relevant for the real world than chess," Sandholm told Live Science.

31/05/2023

Caltech researcher Ralph Adolphs explained that several different parts of the brain might be activated during a particular type of decision-making. And the maps show which parts of the frontal lobe are the most critical areas that, if damaged

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didn't develop the AI just to excel at a card game, they said. The same abilities that make Claudico good at poker can be applied to everything from auctions to cybersecurity. For example, Sandholm and his team developed a similar AI program to figure out which frequencies and energies the military should use for wireless communication in order to avoid signal jamming by an enemy.

22/05/2023

phenomenon," he said.

Sandholm and his colleagues didn't pre-program Claudico's poker strategy. They wrote algorithms that automatically compute a strategy by trying to find the Nash equilibrium. This concept from game theory was developed by American mathematician John Nash, who was portrayed in the film "A Beautiful Mind." In a noncooperative game, players are said to be in Nash equilibrium if they are making the best decision possible, taking into account the decisions of the other players.

Claudico's poker ability may

20/05/2023

In the past, that's been a stumbling block. Libratus was involved in a poker tournament in 2015 and couldn't beat the humans, with the match ending in a statistical tie. However, the souped-up version of Libratus used in the recent tournament has a better end-game solving strategy, Sandholm said.

The tournament
For the "Brains vs. Artificial Intelligence" tournament, four of the world's best poker players faced off one-on-one against Libratus in 120,000 hands of poker. At stake was a $200,000 pot, which the human players received even if they lost.

"They are professionals, so they were fighting to the bitter end, really hard," Sandholm said. "They were studying really hard every night on their computers, trying to find holes in the AI."

In the end, it was no contest: The AI prevailed.

20/05/2023

So Sandholm and his colleagues relied on a different concept to program Libratus. Known as Nash equilibrium, it is a mathematical way of determining the best game strategy to maximize your own payoffs while minimizing those of your opponent. In any one hand of poker, random chance dictates that the Nash equilibrium play may lose, but over the course of many hands, Nash equilibrium translates to the "unbeatable play" strategy, Sandholm said.

However, "the game has 10 to the power of 160 different situations," meaning it has many, many more computational possibilities than Go. As a result, the program can't calculate the perfect Nash equilibrium solution, but must instead approximate.