%0 Journal Article
%T 解读AlphaGo背后的人工智能技术<br>Interpretation of the arti?cial intelligence technology behind Alphago
%A 刘知青
%A 吴修竹
%J 控制理论与应用
%D 2016
%R 10.7641/CTA.2016.60526
%X 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.<br>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.
%K AlphaGo 深度学习 价值网络 策略网络&lt
%K br&gt
%K AlphaGo deep learning value network policy network
%U http://jcta.alljournals.ac.cn/cta_cn/ch/reader/view_abstract.aspx?file_no=CCTA160526&flag=1