Where does AlphaGo go: From church-turing thesis to AlphaGo thesis and beyond

被引：217
作者：
Wang F.-Y. [1 ,2 ]
Zhang J.J. [3 ]
Zheng X. [4 ]
Wang X. [1 ,5 ]
Yuan Y. [1 ,5 ]
Dai X. [3 ]
Zhang J. [3 ]
Yang L. [6 ]
机构：
[1] State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences (SKL-MCCS, CASIA), Beijing
[2] Research Center of Computational Experiments and Parallel Systems, National University of Defense Technology, Changsha
[3] Department of Electrical and Computer Engineering, Ritchie School of Engineering and Computer Science, University of Denver, Denver, 80210, CO
[4] Department of Computer Science and Engineering, University of Minnesota, Minneapolis, 55414, MN
[5] Qingdao Academy of Intelligent Industries (QAII), Qingdao, Shandong
[6] Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, 80523, CO
来源：
IEEE/CAA Journal of Automatica Sinica | 2016年 / 3卷 / 02期
基金：
中国国家自然科学基金;
关键词：
ACP; AlphaGo; AlphaGo Thesis; Church-Turing Thesis; deep learning; deep neural networks; deep rule-based networks; knowledge automation; parallel intelligence; parallel management; parallel ontrol;
D O I：
10.1109/JAS.2016.7471613
中图分类号：
学科分类号：
摘要：
An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation. © 2014 Chinese Association of Automation.
引用
收藏
页码：113 / 120
页数：7
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