引用本文:刘知青,吴修竹.解读AlphaGo背后的人工智能技术[J].控制理论与应用,2016,33(12):1685~1687.[点击复制]
LIU Zhi-qing,WU Xiu-zhu.Interpretation of the arti?cial intelligence technology behind Alphago[J].Control Theory and Technology,2016,33(12):1685~1687.[点击复制]
解读AlphaGo背后的人工智能技术
Interpretation of the arti?cial intelligence technology behind Alphago
摘要点击 3271  全文点击 3864  投稿时间:2016-07-19  修订日期:2017-01-03
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DOI编号  10.7641/CTA.2016.60526
  2016,33(12):1685-1687
中文关键词  AlphaGo  深度学习  价值网络  策略网络
英文关键词  AlphaGo  deep learning  value network  policy network
基金项目  
作者单位E-mail
刘知青* 北京邮电大学软件学院 linxiaomo1992@163.com 
吴修竹 北京邮电大学软件学院  
中文摘要
      随着人工智能在各个领域的应用,越来越多的问题通过人工智能得到更优的解决, 但是围棋因其本身的复 杂度一直是人工智能领域的难解之题. AlphaGo团队利用了人工智能中的一个重要分支—深度学习训练了一款围 棋人工智能程序, 并在2016年3月与职业九段选手李世石的对弈中以4:1的比分获胜, 受到了大众的广泛关注. 本文 介绍了AlphaGo这一程序背后的复杂的网络构造以及不同网络的优缺点.
英文摘要
      With the application of arti?cial intelligence in various ?elds, more and more problems have been solved. But computer Go has been a dif?cult problem in the ?eld of arti?cial intelligence, because of the complexity of the game. AlphaGo team has trained a Go AI program which took advantage of an important branch of arti?cial intelligence – deep learning. In March 2016 AlphaGo won 4–1 the game with professional Go player Lee se-dol (9P), received extensive attention of the public.