平行学习——机器学习的一个新型理论框架

李力; 林懿伦; 曹东璞; 郑南宁; 王飞跃

doi:10.16383/j.aas.2017.y000001

平行学习——机器学习的一个新型理论框架

doi: 10.16383/j.aas.2017.y000001

李力^1,,
林懿伦^2,3,,
曹东璞^4,5,,
郑南宁^6,,
王飞跃^2,7, ,

1.
清华信息科学与技术国家实验室(筹), 清华大学自动化系北京 100084 中国
2.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190 中国
3.
中国科学院大学北京 100049 中国
4.
英国克兰菲尔德大学驾驶员认知与自动驾驶实验室克兰菲尔德 MK43 0AL 英国
5.
青岛智能产业技术研究院青岛 266000 中国
6.
西安交通大学人工智能与机器人研究所西安 710049 中国
7.
国防科技大学军事计算实验与平行系统技术中心长沙 410073 中国

基金项目:

国家自然科学基金 91520301

详细信息

作者简介:
李力清华大学自动化系副教授.主要研究方向为人工智能和机器学习,智能交通系统和智能汽车.E-mail:li-li@tsinghua.edu.cn

林懿伦中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生.主要研究方向为社会计算,智能交通系统,深度学习和强化学习.E-mail:linyilun2014@ia.ac.cn

曹东璞英国克兰菲尔德大学驾驶员认知与自动驾驶实验室主任.中科院自动化所客座研究员.主要研究方向为自动驾驶,人车协同,与平行驾驶.E-mail:d.cao@cranfield.ac.uk

郑南宁西安交通大学人工智能与机器人研究所教授.中国工程院院士.主要研究方向为模式识别与智能系统,机器视觉与图象处理.E-mail:nnzheng@mail.xjtu.edu.cn

通讯作者:
王飞跃中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员.国防科学技术大学军事计算实验与平行系统技术研究中心主任.主要研究方向为智能系统和复杂系统的建模、分析与控制.本文通信作者.E-mail:feiyue.wang@ia.ac.cn.

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出版历程
- 收稿日期: 2016-11-25
- 录用日期: 2017-01-01
- 刊出日期: 2017-01-01

Parallel Learning——A New Framework for Machine Learning

LI Li^1
,,
LIN Yi-Lun^{2,3
,},
CAO Dong-Pu^{4,5
,},
ZHENG Nan-Ning^6
,,
WANG Fei-Yue^{2,7
, ,}

1.
National Laboratory for Information Science and Technology(TNList), Department of Automation, Tsinghua University, Beijing 100084, China
2.
the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China
4.
Driver Cognition and Automated Driving Laboratory, Cranfield University, Cranfield MK43 0AL, UK
5.
Qingdao Academy of Intelligent Industries, Qingdao 266000, China
6.
Institute of Artificial Intelligence and Robotics(IAIR), Xi'an Jiaotong University, Xi'an 710049, China
7.
Research Center of Military Computational Experiments and Parallel Systems, National University of Defense Technology, Changsha 410073, China

Funds:

Supported by National Natural Science Foundation of China 91520301

More Information

Author Bio:
LI Li Associate professor at Depart- ment of Automation, Tsinghua Univer-sity. His research interest covers arti- cial intelligence and machine learning, intelligent transportation systems and intelligent vehicles.

LIN Yi-Lun Ph.D at the State Key Laboratory of Management and Con-trol for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers social computing, intelligent transportation systems, deep learning and reinforcement learning.

CAO Dong-Pu Director of Driver Cognition and Automated Driving Lab- oratory, Cran¯eld University. Visiting professor at Institute of Automation, Chinese Academy of Sciences. His research interest covers automated driving, driver-automation collaboration and parallel driving.

ZHENG Nan-Ning Professor at Institute of Arti¯cial Intelligence and Robotics (IAIR), Xi0an Jiaotong Uni- versity. Member of Chinese Academy of Engineering. His research interest covers pattern recognition and intelligent systems, computer vision and image processing.

Corresponding author: WANG Fei-Yue Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. Director of the Research Center for Computational Experiments and Parallel Systems Technol- ogy, National University of Defense Technology. His re- search interest covers modeling, analysis, and control of intelligent systems and complex systems. Corresponding author of this paper.E-mail:feiyue.wang@ia.ac.cn.

摘要

摘要: 本文提出了一种新的机器学习理论框架.该框架结合了现有多种机器学习理论框架的优点，并针对如何使用软件定义的人工系统从大数据提取有效数据，如何结合预测学习和集成学习，以及如何利用默顿定律进行指示学习等目前机器学习领域面临的重要问题进行了特别设计.
- 机器学习 /
- 人工智能 /
- 平行学习 /
- 平行智能 /
- 平行系统及理论
Abstract: In this paper, we propose a new framework of machine learning theory, parallel learning,which incorporates and inherits many elements from various existing machine learning theories. Special designs are also presented to deal with some important problems in the machine learning research field, e.g., useful data retrieval from big data using software defined artificial systems, combination of predictive learning and ensemble learning, application of Merton's law to prescriptive learning.
- Machine learning /
- artificial intelligence /
- parallel learning /
- parallel intelligence /
- parallel system and theory

HTML全文

图 1 平行学习的理论框架图(虚线上方为通过软件定义的人工系统进行大数据预处理,虚线下方表示基于计算实验的预测学习和集成学习,以及平行控制和指示学习. 细线箭头代表数据生成或数据学习,粗线箭头代表行动和数据之间的交互.)

Fig. 1 The theoretical framework of parallel learning (The part above the dash line focuses on big data preprocessing using software defined artificial systems; the part beneath the dash line focuses on predictive learning and ensemble learning based computational experiments,as well as parallel control and prescriptive learning. The thin arrows represent either data generation or data learning; the thick arrows present interactions between data and actions.)

下载: 全尺寸图片幻灯片

图 2 AlphaGo将现实世界的数据映射到平行世界,进行多线迭代来求取预期行动

Fig. 2 AlphaGo maps data in realistic world into parallel world and uses multithread iterations to determine the expected actions

下载: 全尺寸图片幻灯片

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