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

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

蒲志强^{1, 2, 3,},
易建强^{1, 2, 3,},
刘振^{1, 3,},
丘腾海^{1, 3,},
孙金林^{1, 4,},
李非墨^1,

1.
中国科学院自动化研究所综合信息系统研究中心北京 100190
2.
中国科学院大学人工智能学院北京 100049
3.
泰州智能制造研究院泰州 225321
4.
江苏大学电气信息工程学院镇江 212013

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)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. 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. 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. 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. 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. E-mail: lifeimo2012@ia.ac.cn

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出版历程
- 收稿日期: 2021-02-04
- 录用日期: 2021-06-18
- 网络出版日期: 2021-07-16
- 刊出日期: 2022-03-25

Knowledge-based and Data-driven Integrating Methodologies for Collective Intelligence Decision Making: A Survey

PU Zhi-Qiang^{1, 2, 3
,},
YI Jian-Qiang^{1, 2, 3
,},
LIU Zhen^{1, 3
,},
QIU Teng-Hai^{1, 3
,},
SUN Jin-Lin^{1, 4
,},
LI Fei-Mo^1
,

1.
Integrated Information System Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049
3.
Taizhou Institute of Intelligent Manufacturing, Taizhou 225321
4.
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013

Funds: Supported by National Key Research and Development Program of China (2020AAA0103404) and National Natural Science Foundation of China (62073323)

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 his Ph.D. degree in control theory and control engineering from University of Chinese Academy of Sciences, in 2014. His research interest covers 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 his Ph.D. degree in automation control from the Kyushu Institute of Technology, Kitakyushu, Japan, in 1992. His research interest covers 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 his Ph.D. degree in control theory and control engineering from University of Chinese Academy of Sciences, in 2015. His research interest covers 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 his master degree in control theory and control engineering from Beihang University, in 2016. His research interest covers intelligence decision making, multi-agent, and the applications of unmanned autonomous systems

SUN Jin-Lin　Lecturer 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 his Ph.D. degree in computer applied technology from University of Chinese Academy of Sciences in 2017. His research interest covers remote sensing image processing, computer vision, and intelligent perception

摘要

摘要: 群体智能(Collectire intelligence, CI)系统具有广泛的应用前景. 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. 本文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.
- 群体智能 /
- 知识与数据协同 /
- 多智能体 /
- 决策智能
Abstract: Collective intelligence (CI) shows promising application prospects. Current research methodologies of intelligent decision making for CI systems can be categorized as knowledge-based and data-driven methods, both showing inherent advantages and disadvantages. Therefore, we claim that integrating knowledge-based and data-driven paradigms offers a new and prospective research direction. In this paper, possible methods of this integration are systematically introduced, and all of these methods are classified into a framework level and an algorithm level. Specifically, the methods integrated in the algorithm level are further categorized as hierarchical and componentized methods. In the hierarchical taxonomy, neural network tree, genetic fuzzy tree, and hierarchical reinforcement learning are included. In the componentized taxonomy, knowledge enhanced data-driven, data optimized knowledge-driven, and complementary knowledge and data driven methods are introduced. Finally, several future research priorities on the knowledge-based and data-driven integrating paradigms are proposed for the considerations of theoretical development and application requirement.
- Collective intelligence (CI) /
- knowledge and data integrating /
- multi-agent /
- decision intelligence

HTML全文

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

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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

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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

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图 4 MDP与SMDP比较

Fig. 4 Comparison between MDP and SMDP

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图 5 知识增强的数据驱动方法

Fig. 5 Knowledge enhanced data-driven methods

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图 6 知识的网络化展开概念模型

Fig. 6 Conceptual networking expansion of knowledge

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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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