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An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday. Sign up for Nature Briefing. Search Article search Search.{/INSERTKEYS}{/PARAGRAPH} He thinks that their success is a step towards applications such as automated negotiations, better fraud detection and self-driving cars. References 1. When playing, it runs on just two central processing units CPUs. Sign me up to receive the daily Nature Briefing email. {PARAGRAPH}{INSERTKEYS}Multiplayer poker has fallen to the machines. It built Pluribus by updating Libratus and created a bot that needs much less computing power to play matches. Libratus searched to the end of a game before choosing an action. Article Google Scholar Download references. It starts off playing poker randomly and improves as it works out which actions win more money. Nature menu. Nature Research menu. But Brown thinks that AIs are outgrowing their playpen. The team behind Pluribus had already built an AI, called Libratus, that had beaten professionals at two-player poker. Search for this author in: Pub Med Nature. Games have proved a great way to measure progress in AI because bots can be scored against top humans β€” and objectively be hailed as superhuman if they triumph. Close banner Close. Machines have raised the stakes once again. Most game-playing AIs search forwards through decision trees for the best move to make in a given situation. In these scenarios, there is always one winner and one loser, and game theory offers a well-defined best strategy. It then decides whether it can improve on it. The key breakthrough was developing a method that allowed Pluribus to make good choices after looking ahead only a few moves rather than to the end of the game. By solving multiplayer poker, Pluribus lays the foundation for future AIs to tackle complex problems of this sort, says Brown. Few AIs have mastered more than one game, which requires general ability rather than a niche skill. But the complexity introduced by extra players makes this tactic impractical. A great AI challenge if there ever was one. Get the most important science stories of the day, free in your inbox. In a day session with more than 10, hands, it beat 15 top human players. If the alternatives lead to better outcomes, it will be more likely to choose theme in future. Sign up. Brown, N. Poker requires reasoning with hidden information β€” players must work out a strategy by considering what cards their opponents might have and what opponents might guess about their hand based on previous betting. But game theory is less helpful for scenarios involving multiple parties with competing interests and no clear winβ€”lose conditions β€” which reflect most real-life challenges. Advanced search. It is the first time that an artificial-intelligence AI program has beaten elite human players at a game with more than two players 1. Download references. After each hand, it looks back at how it played and checks whether it would have made more money with different actions, such as raising rather than sticking to a bet. By playing trillions of hands of poker against itself, Pluribus created a basic strategy that it draws on in matches. When playing against itself, Pluribus plays a hand in around 20 seconds β€” roughly twice as fast as professional humans. And because it taught itself to play without human input, the AI settled on a few strategies that human players tend not to use. At each decision point, it compares the state of the game with its blueprint and searches a few moves ahead to see how the action played out. But more players makes choosing an action at any given moment more difficult, because it involves assessing a larger number of possibilities. PDF version. Enter your email address. Nature Briefing An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday. Douglas Heaven Douglas Heaven is a science writer based in London. But Togelius thinks there is mileage yet for AI researchers and games.