{"id":173018,"date":"2015-01-09T02:58:07","date_gmt":"2015-01-09T07:58:07","guid":{"rendered":"http:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/uncategorized\/self-taught-computer-program-finds-super-strategy-for-poker.php"},"modified":"2015-01-09T02:58:07","modified_gmt":"2015-01-09T07:58:07","slug":"self-taught-computer-program-finds-super-strategy-for-poker","status":"publish","type":"post","link":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/super-computer\/self-taught-computer-program-finds-super-strategy-for-poker.php","title":{"rendered":"Self-taught computer program finds super strategy for poker"},"content":{"rendered":"<p><p>    NEW YORK -- A computer program that taught itself to play poker    has created nearly the best possible strategy for one version    of the game, showing the value of techniques that may prove    useful to help decision-making in medicine and other areas.  <\/p>\n<p>    The program considered 24 trillion simulated poker hands per    second for two months, probably playing more poker than all    humanity has ever experienced, says Michael Bowling, who led    the project.  <\/p>\n<p>    The resulting strategy still won't win every game because of    bad luck in the cards. But over the long run -- thousands of    games -- it won't lose money. \"We can go against the best    (players) in the world and the humans are going to be the ones    that lose money,\" said Bowling, of the University of Alberta in    Edmonton, Canada.  <\/p>\n<p>    The strategy applies specifically to a game called heads-up    limit Texas Hold 'em.  <\/p>\n<p>    While scientists have created poker-playing programs for years,    Bowling's result stands out because it comes so close to    \"solving\" its version of the game, which essentially means    creating the optimal strategy.  <\/p>\n<p>    Poker is hard to solve because it involves imperfect    information, where a player doesn't know everything that has    happened in the game he is playing -- specifically, what cards    the opponent has been dealt.  <\/p>\n<p>    Many real-world challenges like negotiations and auctions also    include imperfect information, which is one reason why poker    has long been a proving ground for the mathematical approach to    decision-making called game theory.  <\/p>\n<p>    Tuomas Sandholm of Carnegie Mellon University in Pittsburgh,    who didn't participate in the new work, called Bowling's    results a landmark. He said it's the first time that an    imperfect-information game that is competitively played by    people has been essentially solved.  <\/p>\n<p>    Bowling's paper, released Thursday by the journal Science,    introduces some techniques that could become useful for    applying game theory in real-world situations. Bowling is    investigating the possibility of helping doctors determine    proper insulin doses for diabetic patients, for example.  <\/p>\n<p>    Game theory has also been used to schedule security patrols,    and it has implications for other areas such as developing    strategies for cybersecurity, designing drugs and fighting    disease pandemics.  <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>Continue reading here: <\/p>\n<p><a target=\"_blank\" href=\"http:\/\/www.twincities.com\/nation\/ci_27286116\/self-taught-computer-program-finds-super-strategy-poker?source=rss\/RK=0\/RS=bCmdjJq2X07oTs1.w.yCL8rspoE-\" title=\"Self-taught computer program finds super strategy for poker\">Self-taught computer program finds super strategy for poker<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> NEW YORK -- A computer program that taught itself to play poker has created nearly the best possible strategy for one version of the game, showing the value of techniques that may prove useful to help decision-making in medicine and other areas. The program considered 24 trillion simulated poker hands per second for two months, probably playing more poker than all humanity has ever experienced, says Michael Bowling, who led the project.  <a href=\"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/super-computer\/self-taught-computer-program-finds-super-strategy-for-poker.php\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"limit_modified_date":"","last_modified_date":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[41],"tags":[],"class_list":["post-173018","post","type-post","status-publish","format-standard","hentry","category-super-computer"],"modified_by":null,"_links":{"self":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/173018"}],"collection":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/comments?post=173018"}],"version-history":[{"count":0,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/173018\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/media?parent=173018"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/categories?post=173018"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/tags?post=173018"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}