2.765

2022影响因子

(CJCR)

• 中文核心
• EI
• 中国科技核心
• Scopus
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• 英国科学文摘

## 留言板

 引用本文: 田渊栋. 阿法狗围棋系统的简要分析. 自动化学报, 2016, 42(5): 671-675.
TIAN Yuan-Dong. A Simple Analysis of AlphaGo. ACTA AUTOMATICA SINICA, 2016, 42(5): 671-675. doi: 10.16383/j.aas.2016.y000001
 Citation: TIAN Yuan-Dong. A Simple Analysis of AlphaGo. ACTA AUTOMATICA SINICA, 2016, 42(5): 671-675.

## A Simple Analysis of AlphaGo

###### Author Bio: Research scientist in Facebook AI Research, working on deep learning and computer vision. Prior to that, he was a researcher/software engineer in Google Self-driving Car Team in 2013»2014. He received Ph. D. in Robotics Institute, Carnegie Mellon University in 2013, Bachelor and Master degrees in computer science in Shanghai Jiao Tong University. He is the recipient of 2013 ICCV Marr Prize Honorable Mentions.
• 摘要: 谷歌的围棋系统阿法狗(AlphaGo)在三月的比赛中以4:1的成绩击败了围棋世界冠军李世石, 大大超过了许多人对计算机围棋程序何时能赶上人类职业高手的预期(约10～30年).本文在技术层面分析了阿法狗系统的组成部分, 并基于它过去的公开对局预测了它可能的弱点.
• 图  1  AlphaGo的分析1

Fig.  1  Analysis 1 of AlphaGo

图  2  AlphaGo的分析2

Fig.  2  Analysis 2 of AlphaGo

•  [1] Silver D, Huang A, Maddison C J, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D. Mastering the game of go with deep neural networks and tree search. Nature, 2016, 529(7587): 484-489 [2] Tian Y D, Zhu Y. Better computer go player with neural network and long-term prediction. In: International Conference on Learning Representation (ICLR). San Juan, Puerto Rico, 2016.

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##### 出版历程
• 收稿日期:  2016-04-14
• 录用日期:  2016-05-10
• 刊出日期:  2016-05-01

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