Parallel Learning: a Perspective and a Framework

Li Li; Yilun Lin; Nanning Zheng; Fei-Yue Wang

doi:10.1109/JAS.2017.7510493

Volume 4 Issue 3

Jul. 2017

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(3): 389-395

Li Li, Yilun Lin, Nanning Zheng and Fei-Yue Wang, "Parallel Learning: a Perspective and a Framework," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 389-395, July 2017. doi: 10.1109/JAS.2017.7510493

Citation:

Li Li, Yilun Lin, Nanning Zheng and Fei-Yue Wang, "Parallel Learning: a Perspective and a Framework," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 389-395, July 2017. doi: 10.1109/JAS.2017.7510493

Li Li, Yilun Lin, Nanning Zheng and Fei-Yue Wang, "Parallel Learning: a Perspective and a Framework," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 389-395, July 2017. doi: 10.1109/JAS.2017.7510493

Citation:

Li Li, Yilun Lin, Nanning Zheng and Fei-Yue Wang, "Parallel Learning: a Perspective and a Framework," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 389-395, July 2017. doi: 10.1109/JAS.2017.7510493

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Parallel Learning: a Perspective and a Framework

doi: 10.1109/JAS.2017.7510493

Li Li^1
,,
Yilun Lin^2
,,
Nanning Zheng^3
,,
Fei-Yue Wang^{4
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1.
Department of Automation, TNList, Tsinghua University, Beijing 100084, China
2.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China; and also with the University of Chinese Academy of Sciences, Beijing 100049, China
3.
Institute of Artificial Intelligence and Robotics (IAIR), Xi'an Jiaotong University, Xi'an 710049, China
4.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and also with the Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073, China

Funds:

the National Natural Science Foundation of China 91520301

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Author Bio:
Li Li (S'05-M'06-SM'10-F'17) is currently an Aassociate Professor with Department of Automation, Tsinghua University, China. His research interests include complex and networked systems, intelligent control and sensing, intelligent transportation systems and intelligent vehicles. He had published over 50 SCI indexed international journal papers and over 50 international conference papers as a first/corresponding author. He serves as an Associate Editor for IEEE Transactions on Intelligent Transportation Systems. (email: li-li@mail.tsinghua.edu.cn)

YiLun Lin is a Ph.D. candidate at The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interests include social computing, intelligent transportation systems, deep learning and reinforcement learning. (email: linyilun2014@ia.ac.cn)

Nanning Zheng (SM'93-F'06) is a Professor at Institute of Artificial Intelligence and Robotics (IAIR), Xi0an Jiaotong University. Member of Chinese Academy of Engineering. His research interests include pattern recognition and intelligent systems, computer vision and image processing. (email: nnzheng@mail.xjtu.edu.cn)

Fei-Yue Wang (S'87-M'89-SM'94-F'03) received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a professor and Director of the Robotics and Automation Laboratory (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Oversea Chinese Talents Program from the State Planning Council and "100 Talent Program" from CAS, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory of Management and Control for Complex Systems. Dr. Wang's current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of China's Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), and the Vice President of the ACM China Council (2010-2011). Since 2008, he is the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang is elected Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the 2nd Class National Prize in Natural Sciences of China and awarded the Outstanding Scientist by ACM for his work in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009 and 2011, and IEEE SMC Norbert Wiener Award in 2014. (email: feiyue.wang@ia.ac.cn)
Corresponding author: Fei-Yue Wang, e-mail:feiyue.wang@ia.ac.cn
Received Date: 2017-05-01
Accepted Date: 2017-06-22

Abstract

Abstract

The development of machine learning in complex system is hindered by two problems nowadays.The first problem is the inefficiency of exploration in state and action space, which leads to the data-hungry of some state-of-art data-driven algorithm.The second problem is the lack of a general theory which can be used to analyze and implement a complex learning system.In this paper, we proposed a general methods that can address both two issues.We combine the concepts of descriptive learning, predictive learning, and prescriptive learning into a uniform framework, so as to build a parallel system allowing learning system improved by self-boosting.Formulating a new perspective of data, knowledge and action, we provide a new methodology called parallel learning to design machine learning system for real-world problems.
- Descriptive learning,
- machine learning,
- parallel learning,
- parallel systems,
- predictive learning,
- prescriptive learning

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References(36)

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Parallel Learning: a Perspective and a Framework

doi: 10.1109/JAS.2017.7510493

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