| 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] |
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| 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 2933 全文点击 3720 投稿时间：2016-07-19 修订日期：2017-01-03 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 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 |
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| 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. |