{"id":3877,"date":"2019-07-12T21:02:17","date_gmt":"2019-07-12T12:02:17","guid":{"rendered":"http:\/\/163.180.4.222\/lab\/?p=3877"},"modified":"2019-07-12T21:02:17","modified_gmt":"2019-07-12T12:02:17","slug":"no-limit-ai-poker-bot-is-first-to-beat-professionals-at-multiplayer-game","status":"publish","type":"post","link":"https:\/\/biochemistry.khu.ac.kr\/lab\/?p=3877","title":{"rendered":"No limit: AI poker bot is first to beat professionals at multiplayer game"},"content":{"rendered":"<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h5>Triumph over five human opponents at Texas hold\u2019em brings bots closer to solving complicated real-world problems.<\/h5>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<div class=\"article__body serif cleared\">\n<figure class=\"figure\">\n<div class=\"embed intensity--high\">\n<div class=\"embed intensity--high\"><img decoding=\"async\" class=\"figure__image\" src=\"https:\/\/media.nature.com\/w800\/magazine-assets\/d41586-019-02156-9\/d41586-019-02156-9_16920180.jpg\" alt=\"Poker players around a table in Rio de Janeiro, Brazil\" data-src=\"\/\/media.nature.com\/w800\/magazine-assets\/d41586-019-02156-9\/d41586-019-02156-9_16920180.jpg\" \/><\/div>\n<\/div><figcaption>\n<p class=\"figure__caption sans-serif\"><span class=\"mr10\">Multiplayer poker has falled to the machines.<\/span>Credit: Alexandre Rotenberg\/Alamy<\/p>\n<\/figcaption><\/figure>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>Machines have raised the stakes once again. A superhuman poker-playing bot called Pluribus has beaten top human professionals at six-player no-limit Texas hold\u2019em poker, the most popular variant of the game. It is the first time that an artificial-intelligence (AI) program has beaten elite human players at a game with more than two players<sup><a href=\"https:\/\/www.nature.com\/articles\/d41586-019-02156-9?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+nature%2Frss%2Fcurrent+%28Nature+-+Issue%29#ref-CR1\" data-track=\"click\" data-action=\"anchor-link\" data-track-label=\"go to reference\" data-track-category=\"references\">1<\/a><\/sup>.<\/p>\n<p>\u201cWhile going from two to six players might seem incremental, it\u2019s actually a big deal,\u201d says Julian Togelius at New York University, who studies games and AI. \u201cThe multiplayer aspect is something that is not present at all in other games that are currently studied.\u201d<\/p>\n<p>The team behind Pluribus had already built an AI, called Libratus,\u00a0<a href=\"https:\/\/www.nature.com\/news\/how-rival-bots-battled-their-way-to-poker-supremacy-1.21580\" data-track=\"click\" data-label=\"https:\/\/www.nature.com\/news\/how-rival-bots-battled-their-way-to-poker-supremacy-1.21580\" data-track-category=\"body text link\">that had beaten professionals at two-player poker<\/a>. It built Pluribus by updating Libratus and created a bot that needs much less computing power to play matches. In a 12-day session with more than 10,000 hands, it beat 15 top human players. \u201cA lot of AI researchers didn\u2019t think it was possible to do this\u201d with our techniques, says Noam Brown at Carnegie Mellon University in Pittsburgh, Philadelphia, and Facebook AI Research in New York, who developed Pluribus with his Carnegie colleague Tuomas Sandholm.<\/p>\n<p>&nbsp;<\/p>\n<aside class=\"recommended pull pull--left sans-serif\" data-label=\"Related\"><a href=\"https:\/\/www.nature.com\/news\/reboot-for-the-ai-revolution-1.22826\" data-track=\"click\" data-track-label=\"recommended article\"><img decoding=\"async\" class=\"recommended__image\" src=\"https:\/\/media.nature.com\/w400\/magazine-assets\/d41586-019-02156-9\/d41586-019-02156-9_16408094.jpg\" \/><\/a><\/p>\n<p class=\"recommended__title serif\">Reboot for the AI revolution<\/p>\n<\/aside>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>Other AIs that have mastered human games \u2014 such as Libratus and DeepMind\u2019s Go-playing bots \u2014 have shown that they are unbeatable in two-player zero-sum matches. In these scenarios, there is always one winner and one loser, and game theory offers a well-defined best strategy.<\/p>\n<p>But game theory is less helpful for scenarios involving multiple parties with competing interests and no clear win\u2013lose conditions \u2014 which reflect most real-life challenges. By solving multiplayer poker, Pluribus lays the foundation for future AIs to tackle complex problems of this sort, says Brown. He thinks that their success is a step towards applications such as automated negotiations, better fraud detection and self-driving cars.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Extra complex<\/strong><\/p>\n<p>To tackle six-player poker, Brown and Sandholm radically overhauled Libratus\u2019s search algorithm. Most game-playing AIs search forwards through decision trees for the best move to make in a given situation. Libratus searched to the end of a game before choosing an action.<\/p>\n<p>But the complexity introduced by extra players makes this tactic impractical. Poker requires reasoning with hidden information \u2014 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 more players makes choosing an action at any given moment more difficult, because it involves assessing a larger number of possibilities.<\/p>\n<p>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.<\/p>\n<p>&nbsp;<\/p>\n<aside class=\"recommended pull pull--left sans-serif\" data-label=\"Related\"><a href=\"https:\/\/www.nature.com\/articles\/d41586-019-00505-2\" data-track=\"click\" data-track-label=\"recommended article\"><img decoding=\"async\" class=\"recommended__image\" src=\"https:\/\/media.nature.com\/w400\/magazine-assets\/d41586-019-02156-9\/d41586-019-02156-9_16689732.jpg\" \/><\/a><\/p>\n<p class=\"recommended__title serif\">AI pioneer: \u2018The dangers of abuse are very real\u2019<\/p>\n<\/aside>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>Pluribus teaches itself from scratch using a form of reinforcement learning similar to that used by DeepMind\u2019s Go AI, AlphaZero. It starts off playing poker randomly and improves as it works out which actions win more money. 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. If the alternatives lead to better outcomes, it will be more likely to choose theme in future.<\/p>\n<p>By playing trillions of hands of poker against itself, Pluribus created a basic strategy that it draws on in matches. 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. It then decides whether it can improve on it. And because it taught itself to play without human input, the AI settled on a few strategies that human players tend not to use.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>AI playpen<\/strong><\/p>\n<p>Pluribus\u2019s success is largely down to its efficiency. When playing, it runs on just two central processing units (CPUs). By contrast,\u00a0<a href=\"https:\/\/www.nature.com\/news\/google-ai-algorithm-masters-ancient-game-of-go-1.19234\" data-track=\"click\" data-label=\"https:\/\/www.nature.com\/news\/google-ai-algorithm-masters-ancient-game-of-go-1.19234\" data-track-category=\"body text link\">DeepMind\u2019s original Go bot used nearly 2,000 CPUs,<\/a>\u00a0and Libratus 100 CPUs, when they first beat top professionals. When playing against itself, Pluribus plays a hand in around 20 seconds \u2014 roughly twice as fast as professional humans.<\/p>\n<p>Games have proved a great way to measure progress in AI because bots can be scored against top humans \u2014 and objectively be hailed as superhuman if they triumph. But Brown thinks that AIs are outgrowing their playpen. \u201cThis was the last remaining challenge in poker,\u201d he says.<\/p>\n<p>But Togelius thinks there is mileage yet for AI researchers and games. \u201cThere\u2019s a lot of unexplored territory,\u201d he says. Few AIs have mastered more than one game, which requires general ability rather than a niche skill. And there\u2019s more than simply playing games, says Togelius. \u201cThere\u2019s also designing them. A great AI challenge if there ever was one.\u201d<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<\/div>\n<div class=\"emphasis\">doi: 10.1038\/d41586-019-02156-9<\/div>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>(\uc6d0\ubb38: <a href=\"https:\/\/www.nature.com\/articles\/d41586-019-02156-9?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+nature%2Frss%2Fcurrent+%28Nature+-+Issue%29\">\uc5ec\uae30<\/a>\ub97c \ud074\ub9ad\ud558\uc138\uc694~)<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; &nbsp; Triumph over five human opponents at Texas hold\u2019em brings bots closer to solving complicated real-world problems. &nbsp; &nbsp; Multiplayer poker has falled to<a href=\"https:\/\/biochemistry.khu.ac.kr\/lab\/?p=3877\" class=\"more-link\">(more&#8230;)<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[35,29,30],"tags":[],"class_list":["post-3877","post","type-post","status-publish","format-standard","hentry","category-lets-do-computer-science","category-lets-do-science","category-recent-science-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2 - aioseo.com -->\n\t<meta name=\"description\" content=\"Triumph over five human opponents at Texas hold\u2019em brings bots closer to solving complicated real-world problems. 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Machines have raised the stakes once again. A superhuman poker-playing bot called Pluribus has beaten top human professionals at six-player no-limit Texas hold\u2019em poker, the most popular variant of the game. It is","og:url":"https:\/\/biochemistry.khu.ac.kr\/lab\/?p=3877","og:image":"https:\/\/biochemistry.khu.ac.kr\/lab\/wp-content\/uploads\/2018\/06\/cropped-logoKHU-1-2.png","og:image:secure_url":"https:\/\/biochemistry.khu.ac.kr\/lab\/wp-content\/uploads\/2018\/06\/cropped-logoKHU-1-2.png","og:image:width":66,"og:image:height":46,"article:published_time":"2019-07-12T12:02:17+00:00","article:modified_time":"2019-07-12T12:02:17+00:00","twitter:card":"summary_large_image","twitter:title":"No limit: AI poker bot is first to beat professionals at multiplayer game \u2014 Synthesis-Based BioFusion Technology Lab","twitter:description":"Triumph over five human opponents at Texas hold\u2019em brings bots closer to solving complicated real-world problems. Machines have raised the stakes once again. A superhuman poker-playing bot called Pluribus has beaten top human professionals at six-player no-limit Texas hold\u2019em poker, the most popular variant of the game. 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