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Where Does AlphaGo Go: From Church-Turing Thesis to AlphaGo Thesis and Beyond
Fei-Yue Wang, Fellow, IEEE, Jun Jason Zhang, Senior Member, IEEE, Xinhu Zheng, Student Member, IEEE, Xiao Wang, Yong Yuan, Xiaoxiao Dai, Student Member, IEEE, Jie Zhang, Liuqing Yang, Fellow, IEEE
Institute of Automation, Chinese Academy of Sciences, The National University of Defense Technology, Qingdao Academy of Intelligent Industries, China, University of Denver, University of Minnesota, Colorado State University, USA
Abstract—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.
Index Terms—ACP, AlphaGo, AlphaGo Thesis, Church-Turing Thesis, deep learning, deep neural networks, deep rule-basedb networks, knowledge automation, parallel intelligence, parallel control, parallel management.
Citation: Fei-Yue Wang, Jun Jason Zhang, Xinhu hang, Xiao Wang, Yong Yuan, Xiaoxiao Dai, Jie Zhang, Liuqing Yang. Where does AlphaGo go: from Church-Turing Thesis to AlphaGo Thesis and beyond. IEEE/CAA Journal of Automatica Sinica, 2016, 3(2): 113-120
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Fig.1. Go: A game of complexity and a symbol for unity of contradiction
Fig.2. Historical milestones in AI: A retrospective from two academic families
Fig.3. Deep rule-based networks: Building a understandable decision language for deep networks
Fig.4. Parallel intelligence for decision-making: An architecture of parallel control and management for complex systems
Fig.5. The Virtual-Real Duality: The execution framework of the parallel systems
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