[1] Zheng Nan-Ning. On challenges in artificial intelligence. Acta Automatica Sinica, 2016, 42(5):641-642(郑南宁. 人工智能面临的挑战. 自动化学报, 2016, 42(5):641-642) http://cn.bing.com/academic/profile?id=7552814bad8265e3a165d5411c7423e9&encoded=0&v=paper_preview&mkt=zh-cn
[2] 王飞跃. 关于复杂系统研究的计算理论与方法. 中国基础科学, 2004, 6(5):3-10 http://www.cnki.com.cn/Article/CJFDTOTAL-ZGJB200405000.htm

Wang Fei-Yue. Computational theory and method on complex system. China Basic Science, 2004, 6(5):3-10 http://www.cnki.com.cn/Article/CJFDTOTAL-ZGJB200405000.htm
[3] 王飞跃. 人工社会、计算实验、平行系统——关于复杂社会经济系统计算研究的讨论. 复杂系统与复杂性科学, 2004, 1(4):25-35 http://www.cnki.com.cn/Article/CJFDTOTAL-FZXT200404001.htm

Wang Fei-Yue. Artificial societies, computational experiments, and parallel systems:a discussion on computational theory of complex social-economic systems. Complex Systems and Complexity Science, 2004, 1(4):25-35 http://www.cnki.com.cn/Article/CJFDTOTAL-FZXT200404001.htm
[4] 王飞跃. 平行系统方法与复杂系统的管理和控制. 控制与决策, 2004, 19(5):485-489 http://www.cnki.com.cn/Article/CJFDTOTAL-KZYC200405001.htm

Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5):485-489 http://www.cnki.com.cn/Article/CJFDTOTAL-KZYC200405001.htm
[5] 王飞跃,史帝夫·兰森.从人工生命到人工社会——复杂社会系统研究的现状与展望.复杂系统与复杂科学, 2004, 1(1):33-41 http://www.cnki.com.cn/Article/CJFDTOTAL-FZXT200401008.htm

Wang F Y, Lansing J S. From artificial life to artificial societies——new methods for studies of complex social systems. Complex Systems and Complexity Science, 2004, 1(1):33-41 http://www.cnki.com.cn/Article/CJFDTOTAL-FZXT200401008.htm
[6] Zhang N, Wang F Y, Zhu F H, Zhao D B, Tang S M. DynaCAS:computational experiments and decision support for ITS. IEEE Intelligent Systems, 2008, 23(6):19-23 doi: 10.1109/MIS.2008.101
[7] Wang F Y. Parallel control and management for intelligent transportation systems:concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3):630-638 doi: 10.1109/TITS.2010.2060218
[8] Li L, Wen D. Parallel systems for traffic control:a rethinking. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4):1179-1182 doi: 10.1109/TITS.2015.2494625
[9] Wang F Y, Wang X, Li L X, Li L. Steps toward parallel intelligence. IEEE/CAA Journal of Automatica Sinica, 2016, 3(4):345-348 doi: 10.1109/JAS.2016.7510067
[10] Jain A K, Duin R P W, Mao J C. Statistical pattern recognition:a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(1):4-37 doi: 10.1109/34.824819
[11] Bishop C M. Pattern Recognition and Machine Learning. New York:Springer, 2006.
[12] Shalev-Shwartz S. Online learning and online convex optimization. Foundations and Trends in Machine Learning, 2011, 4(2):107-194 doi: 10.1561/2200000018
[13] Rakhlin A, Sridharan K. Statistical learning and sequential prediction[Online], available:http://www-stat.wharton.upenn.edu/rakhlin/courses/stat928/stat928_notes.pdf, January 1, 2017
[14] Pérez-Sánchez B, Fontenla-Romero O, Guijarro-Berdiñas B. A review of adaptive online learning for artificial neural networks. Artificial Intelligence Review, 2016, DOI: 10.1007/s10462-016-9526-2
[15] Wiering M, van Otterlo M. Reinforcement Learning:State-of-the-Art. Berlin Heidelberg:Springer, 2012.
[16] Sutton R S, Barto A G. Reinforcement Learning:An Introduction (Second edition). Cambridge, MA:The MIT Press, 2017.
[17] Settles B. Active Learning. San Rafael, CA:Morgan&Claypool Publishers, 2012.
[18] Wang F Y. A big-data perspective on AI:Newton, Merton, and analytics intelligence. IEEE Intelligent Systems, 2012, 27(5):2-4 doi: 10.1109/MIS.2012.91
[19] Zeng D, Lusch R. Big data analytics:perspective shifting from transactions to ecosystems. IEEE Intelligent Systems, 2013, 28(2):2-5 doi: 10.1109/MIS.2013.40
[20] Zhou Z H, Chawla N V, Jin Y C, Williams G J. Big data opportunities and challenges:discussions from data analytics perspectives. IEEE Computational Intelligence Magazine, 2014, 9(4):62-74 doi: 10.1109/MCI.2014.2350953
[21] Richard Feynman[Online], available:https://en.wikiquote.org/wiki/Richard_Feynman, January 1, 2017
[22] Bainbridge W S. The scientific research potential of virtual worlds. Science, 2007, 317(5837):472-476 doi: 10.1126/science.1146930
[23] 王坤峰, 苟超, 王飞跃. 平行视觉:基于ACP的智能视觉计算方法. 自动化学报, 2016, 42(10):1490-1500 http://www.aas.net.cn/CN/abstract/abstract18936.shtml

Wang Kun-Feng, Gou Chao, Wang Fei-Yue. Parallel vision:an ACP-based approach to intelligent vision computing. Acta Automatica Sinica, 2016, 42(10):1490-1500 http://www.aas.net.cn/CN/abstract/abstract18936.shtml
[24] Gaidon A, Wang Q, Cabon Y, Vig E. Virtual worlds as proxy for multi-object tracking analysis. In:Proceedings of the 2016 Computer Vision and Pattern Recognition. Las Vegas, NV:CVPR, 2016. 4340-4349
[25] Wang K, Gou C, Wang F Y, Rehg J M. Parallel vision for perception and understanding of complex scenes:methods, framework, and perspectives. Pattern Recognition, to be published
[26] 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 http://www.aas.net.cn/CN/abstract/abstract18856.shtml

Tian Yuan-Dong. A simple analysis of AlphaGo. Acta Automatica Sinica, 2016, 42(5):671-675 http://www.aas.net.cn/CN/abstract/abstract18856.shtml
[27] Wang F Y, Zhang J J, Zheng X H, Wang X, Yuan Y, Dai X X, Zhang J, Yang L Q. Where does AlphaGo go:from church-turing thesis to AlphaGo thesis and beyond. IEEE/CAA Journal of Automatica Sinica, 2016, 3(2):113-120 doi: 10.1109/JAS.2016.7471613
[28] 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 doi: 10.1038/nature16961
[29] Hinton G, Sejnowski T J. Unsupervised Learning:Foundations of Neural Computation. Cambridge:MIT Press, 1999.
[30] Duda R O, Hart P E, Stork D G. Unsupervised learning and clustering. Pattern Classification (Second edition). New York:Wiley, 2001. 517-600
[31] LeCun Y. Predictive Learning. Speech at NIPS, 2016. https://drive.google.com/file/d/0BxKBnD5y2M8NREZod0tVdW5FLTQ/view
[32] Piaget J. The Origins of Intelligence in Children (Second edition). New York:International Universities Press Inc., 1974.
[33] Drescher G L. Made-Up Minds:A Constructivist Approach to Artificial Intelligence. Cambridge:MIT Press, 1991.
[34] Hawkins J, Blakeslee S. On Intelligence:How a New Understanding of the Brain will Lead to the Creation of Truly Intelligent Machines. New York:Times Books, 2004.
[35] Zhu X J, Goldberg A B. Introduction to Semi-Supervised Learning. San Francisco, CA:Morgan&Claypool Publishers, 2009.
[36] Schwenker F, Trentin E. Pattern classification and clustering:a review of partially supervised learning approaches. Pattern Recognition Letters, 2014, 37:4-14 doi: 10.1016/j.patrec.2013.10.017
[37] Minsky M. The Society of Mind. New York:Simon&Schuster, 1988.
[38] Shoham Y, Leyton-Brown K. Multiagent Systems:Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge:Cambridge University Press, 2008.
[39] Zhou Z H. Ensemble Methods:Foundations and Algorithms. Boca Raton, FL, USA:CRC Press, 2012.
[40] Li L, Chen X Q, Zhang L. Multimodel ensemble for freeway traffic state estimations. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(3):1323-1336 doi: 10.1109/TITS.2014.2299542
[41] Wang F Y, Zhang J, Wei Q, Li L. PDP:parallel dynamic programming. IEEE/CAA Journal of Automatica Sinica, 2017, 4(1):1-5 doi: 10.1109/JAS.2017.7510310
[42] Merton R K. The unanticipated consequences of purposive social action. American Sociological Review, 1936, 1(6):894-904 doi: 10.2307/2084615
[43] 王飞跃. 软件定义的系统与知识自动化:从牛顿到默顿的平行升华. 自动化学报, 2015, 41(1):1-8 http://www.aas.net.cn/CN/abstract/abstract18578.shtml

Wang Fei-Yue. Software-defined systems and knowledge automation:a parallel paradigm shift from Newton to Merton. Acta Automatica Sinica, 2015, 41(1):1-8 http://www.aas.net.cn/CN/abstract/abstract18578.shtml
[44] Lowd D, Meek C. Adversarial learning. In:Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. Chicago, Illinois:ACM, 2005.
[45] Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In:Proceedings of Advances in Neural Information Processing Systems 27:28th Annual Conference on Neural Information Processing Systems 2014. Montreal, Canada:NIPS, 2014. 2672-2680
[46] Goodfellow I J, Shlens J, Szegedy C. Explaining and harnessing adversarial examples[Online], available:https://arxiv.org/abs/1412.6572, January 1, 2017
[47] Xia Y C, He D, Qin T, Wang L W, Yu N H, Liu T Y, Ma W Y. Dual learning for machine translation[Online], available:https://arxiv.org/abs/1611.00179, January 1, 2017
[48] Ullrich C. Descriptive and prescriptive learning theories. Pedagogically Founded Courseware Generation for Web-Based Learning:Lecture Notes in Computer Science. Berlin Heidelberg:Springer, 2008, 5260:37-42
[49] Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. InfoGAN:Interpretable representation learning by information maximizing generative adversarial nets[Online], available:https://arxiv.org/abs/1606.03657, January 1, 2017
[50] Li Y C, Liu Y H, Zhang C, Zhao D C, Zheng N N. The "floor-wall" traffic scenes construction for unmanned vehicle simulation evaluation. In:Proceedings of the 17th IEEE Conference on Intelligent Transportation Systems. Qingdao, China:IEEE, 2014. 1726-1731
[51] Li L, Huang W L, Liu Y H, Zheng N N, Wang F Y. Intelligence testing for autonomous vehicles:a new approach. IEEE Transactions on Intelligent Vehicles, 2016, 1(2):158-166 doi: 10.1109/TIV.2016.2608003
[52] 王飞跃. 社会信号处理与分析的基本框架:从社会传感网络到计算辩证解析方法. 中国科学:信息科学, 2013, 43(12):1598-1611 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201312008.htm

Wang Fei-Yue. A framework for social signal processing and analysis:from social sensing networks to computational dialectical analytics. Scientia Sinica Informationis, 2013, 43(12):1598-1611 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201312008.htm
[53] 王飞跃. 平行控制:数据驱动的计算控制方法. 自动化学报, 2013, 39(4):293-302 http://www.aas.net.cn/CN/abstract/abstract17915.shtml

Wang Fei-Yue. Parallel control:a method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4):293-302 http://www.aas.net.cn/CN/abstract/abstract17915.shtml
[54] 王飞跃. 天命唯新:迈向知识自动化——《自动化学报》创刊50周年专刊序. 自动化学报, 2013, 39(11):1741-1743 doi: 10.3724/SP.J.1004.2013.01741

Wang Fei-Yue. The destiny:towards knowledge automation——preface of the special issue for the 50th anniversary of Acta Automatica Sinica. Acta Automatica Sinica, 2013, 39(11):1741-1743 doi: 10.3724/SP.J.1004.2013.01741