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知识和数据协同驱动的群体智能决策方法研究综述

蒲志强 易建强 刘振 丘腾海 孙金林 李非墨

蒲志强, 易建强, 刘振, 丘腾海, 孙金林, 李非墨. 知识和数据协同驱动的群体智能决策方法研究综述. 自动化学报, 2021, x(x): 1−17 doi: 10.16383/j.aas.c210118
引用本文: 蒲志强, 易建强, 刘振, 丘腾海, 孙金林, 李非墨. 知识和数据协同驱动的群体智能决策方法研究综述. 自动化学报, 2021, x(x): 1−17 doi: 10.16383/j.aas.c210118
Pu Zhi-Qiang, Yi Jian-Qiang, Liu Zhen, Qiu Teng-Hai, Sun Jin-Lin, Li Fei-Mo. Knowledge-based and data-driven integrating methodologies for collective intelligence decision making: A survey. Acta Automatica Sinica, 2021, x(x): 1−17 doi: 10.16383/j.aas.c210118
Citation: Pu Zhi-Qiang, Yi Jian-Qiang, Liu Zhen, Qiu Teng-Hai, Sun Jin-Lin, Li Fei-Mo. Knowledge-based and data-driven integrating methodologies for collective intelligence decision making: A survey. Acta Automatica Sinica, 2021, x(x): 1−17 doi: 10.16383/j.aas.c210118

知识和数据协同驱动的群体智能决策方法研究综述

doi: 10.16383/j.aas.c210118
基金项目: 科技创新2030—"新一代人工智能”重大项目(2020AAA0103404), 国家自然科学基金(62073323, 61806199), 中国科学院战略性先导研究项目(XDA27030403), 中国科学院对外合作重点项目(173211KYSB20200002)资助
详细信息
    作者简介:

    蒲志强:中国科学院自动化研究所综合信息系统研究中心副研究员. 2014年获得中国科学院大学控制理论与控制工程博士学位. 主要研究方向为群体智能、多智能体强化学习、无人系统鲁棒自适应控制等. 本文通信作者. E-mail: zhiqiang.pu@ia.ac.cn

    易建强:中国科学院自动化研究所综合信息系统研究中心研究员. 1992年获得日本九州工业大学自动控制博士学位. 主要研究方向为智能控制、智能机器人、自主无人系统等. E-mail: jianqiang.yi@ia.ac.cn

    刘振:中国科学院自动化研究所综合信息系统研究中心副研究员. 2015年获得中国科学院大学控制理论与控制工程博士学位. 主要研究方向为飞行控制、鲁棒自适应控制、多智能体强化学习等. E-mail: liuzhen@ia.ac.cn

    丘腾海:中国科学院自动化研究所综合信息系统研究中心助理研究员. 2016年获得北京航空航天大学控制理论与控制工程硕士学位. 主要研究方向为智能决策、多智能体、自主无人系统应用等. E-mail: tenghai.qiu@ia.ac.cn

    孙金林:江苏大学电气信息工程学院讲师. 主要研究方向为鲁棒与自适应控制, 计算智能, 抗干扰控制. E-mail: jinlinsun@outlook.com

    李非墨:中国科学院自动化研究所综合信息系统研究中心助理研究员. 2017年获得中国科学院大学计算机应用技术博士学位. 主要研究方向为遥感图像处理、计算机视觉、智能感知等. E-mail: lifeimo2012@ia.ac.cn

Knowledge-Based and Data-Driven Integrating Methodologies for Collective Intelligence Decision Making: A Survey

Funds: Supported by the National Key Research and Development Program of China (2020AAA0103404), National Natural Science Foundation of China (62073323, 61806199), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA27030403), the External Cooperation Key Project of Chinese Academy Sciences (173211KYSB20200002)
More Information
    Author Bio:

    PU Zhi-Qiang Associate professor at the Integrated Information System Research Center, Institute of Automation, Chinese Academy of Sciences. He received the Ph. D. degree in control theory and control engineering from University of Chinese Academy of Sciences, Beijing, China, in 2014. His research interests include collective intelligence, multi-agent reinforcement learning, and robust adaptive control of unmanned systems. Corresponding author of this paper

    YI Jian-Qiang Professor at the Integrated Information System Research Center, Institute of Automation, Chinese Academy of Sciences. He received the Ph. D. degree in automation control from the Kyushu Institute of Technology, Kitakyushu, Japan, in 1992. His research interests include intelligent control, intelligent robotics, and autonomous unmanned systems

    LIU Zhen Associate professor at the Integrated Information System Research Center, Institute of Automation, Chinese Academy of Sciences. He received the Ph. D. degree in control theory and control engineering from University of Chinese Academy of Sciences, Beijing, China, in 2015. His research interests include flight control, robust adaptive control, and multi-agent reinforcement learning

    QIU Teng-Hai Research assistant at the Integrated Information System Research Center, Institute of Automation, Chinese Academy of Sciences. He received the M.Eng. degree in control theory and control engineering from Beihang University, Beijing, China, in 2016. His research interests include intelligence decision making, multi-agent, and the applications of unmanned autonomous systems

    SUN Jin-Lin Assistant Professor at the School of Electrical and Information Engineering, Jiangsu University. His research interest covers robust and adaptive control, computational intelligence, and anti-disturbance control

    LI Fei-Mo Research assistant at the Integrated Information System Research Center, Institute of Automation, Chinese Academy of Sciences. He received the Ph. D. degree in computer applied technology from University of Chinese Academy of Sciences in 2017. His research interests include remote sensing image processing, computer vision, and intelligent perception

  • 摘要: 群体智能系统拥有广泛的应用前景. 当前的群体智能决策方法主要包括知识驱动、数据驱动两大类, 但各自存在优缺点. 本文指出, 知识与数据协同驱动将为群体智能决策提供新解法. 文章系统梳理了知识与数据协同驱动可能存在的不同方法路径, 从知识与数据的架构级协同、算法级协同两个层面对典型方法进行了分类, 同时将算法级协同方法进一步划分为算法的层次化协同和组件化协同, 前者包含神经网络树、遗传模糊树、分层强化学习等层次化方法, 后者进一步总结为知识增强的数据驱动、数据调优的知识驱动、知识与数据的互补结合等方法. 最后, 从理论发展与实际应用的需求出发, 指出了知识与数据协同驱动的群体智能决策中未来几个重要的研究方向.
  • 图  1  知识驱动和数据驱动各自优缺点

    Fig.  1  Advantages and disadvantages of knowledge- based and data-driven methodologies

    图  2  知识与数据协同驱动总体框架

    Fig.  2  Overall framework of knowledge-based and data-driven methods integration

    图  3  知识和数据架构级协同概念模型

    Fig.  3  Conceptual model for framework-level integration of knowledge-based and data-driven methods

    图  4  MDP与SMDP比较

    Fig.  4  Comparison between MDP and SMDP

    图  5  知识增强的数据驱动方法

    Fig.  5  Knowledge enhanced data-driven methods

    图  6  知识的网络化展开概念模型

    Fig.  6  Conceptual networking expansion of knowledge

    图  7  知识驱动与神经网络互补结合控制框架

    Fig.  7  Control diagrams of complementary knowledge-driven and neural network methods

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  • 收稿日期:  2021-02-04
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