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基于ACP的动态网民群体运动组织建模与计算实验研究

王晓 韩双双 杨林瑶 曾轲 王飞跃

王晓, 韩双双, 杨林瑶, 曾轲, 王飞跃. 基于ACP的动态网民群体运动组织建模与计算实验研究. 自动化学报, 2020, 46(4): 653−669 doi: 10.16383/j.aas.c190641
引用本文: 王晓, 韩双双, 杨林瑶, 曾轲, 王飞跃. 基于ACP的动态网民群体运动组织建模与计算实验研究. 自动化学报, 2020, 46(4): 653−669 doi: 10.16383/j.aas.c190641
Wang Xiao, Han Shuang-Shuang, Yang Lin-Yao, Zeng Ke, Wang Fei-Yue. The research on ACP-based modeling and computational experiment for cyber movement organizations. Acta Automatica Sinica, 2020, 46(4): 653−669 doi: 10.16383/j.aas.c190641
Citation: Wang Xiao, Han Shuang-Shuang, Yang Lin-Yao, Zeng Ke, Wang Fei-Yue. The research on ACP-based modeling and computational experiment for cyber movement organizations. Acta Automatica Sinica, 2020, 46(4): 653−669 doi: 10.16383/j.aas.c190641

基于ACP的动态网民群体运动组织建模与计算实验研究

doi: 10.16383/j.aas.c190641
基金项目: 国家自然科学基金项目(61702519), 中国科协青年人才托举工程(2017QNRC001), 英特尔智能网联汽车大学合作研究中心项目(ICRI-IACV), 北京市科委项目(Z181100008918007)资助
详细信息
    作者简介:

    王晓: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副研究员. 2016年获得中国科学院大学社会计算博士学位. 主要研究方向为群体行为的激发与汇聚激励, 群体智能和社交网络挖掘与分析. E-mail: x.wang@ia.ac.cn

    韩双双: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副研究员. 2013年获得加拿大阿尔伯塔大学博士学位. 主要研究方向为平行网络, 社会网络, 无线网络关键技术.E-mail: shuangshuang.han@ia.ac.cn

    杨林瑶: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. 2017年获得山东大学信息科学与工程学院学士学位. 主要研究方向为社交网络分析、多智能体建模和复杂网络. E-mail: yanglinyao2017@ia.ac.cn

    曾轲:2014年于西安交通大学电信学院获得博士学位. 早期研究方向包括社交网络、社会计算、用户仿真建模研究. 近期他工作于美团网语音交互中心, 开展知识图谱、智能交互与知识计算的技术研究与实践应用, 重点关注知识计算与用户推荐方法的结合. E-mail: zengke02@meituan.com

    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主任, 国防科技大学军事计算实验与平行系统技术研究中心主任, 中国科学院大学中国经济与社会安全研究中心主任, 青岛智能产业技术研究院院长. 主要研究方向为平行系统的方法与应用, 社会计算, 平行智能以及知识自动化. 本文通信作者. E-mail: feiyue.wang@ia.ac.cn

  •  收稿日期 2019-09-06    录用日期 2020-01-09 Manuscript received September 6, 2019; accepted January 9, 2020 国家自然科学基金项目(61702519), 中国科协青年人才托举工程(2017QNRC001), 英特尔智能网联汽车大学合作研究中心项目(ICRI-IACV), 北京市科委项目(Z181100008918007)资助 Supported by National Natural Science Foundation of China (61702519), Young Elite Scientists Sponsorship Program of China Association of Science and Technology (2017QNRC001), Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles ( ICRI-IACV), and Beijing Municipal Science & Technology Commission (Z181100008918007) 本文责任编委 莫红 Recommended by Associate Editor MO Hong
  •  1. 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    2. 青岛智能产业技术研究院 青岛 266000    3. 青岛慧拓机器智能有限公司 青岛 266000 4. 中国科学院大学 北京 100049 5. 北京三快在线科技有限公司 北京 100083 6. 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 长沙 410073 1. The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 2. Qingdao Academy of Intelligent Industries, Qingdao 266000 3. Waytous Inc., Qingdao 266000 4. University of Chinese Academy of Sciences, Beijing 100049 5. Beijing Sankuai Online Technology Co., Ltd., Beijing 100083 6. The Research Center for Military Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073

The Research on ACP-based Modeling and Computational Experiment for Cyber Movement Organizations

Funds: Supported by National Natural Science Foundation of China (61702519), Young Elite Scientists Sponsorship Program of China Association of Science and Technology (2017QNRC001), Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles ( ICRI-IACV) , and Beijing Municipal Science & Technology Commission (Z181100008918007)
  • 摘要: 由互联网促成的社会运动组织一经出现, 就受到了广大社会学者以及计算机领域专家的广泛关注. 一方面, 互联网特别是移动互联网在整合信息、引发共振、实时分享及高度互动等方面的特性, 为网民行为的大规模快速聚集提供了直通渠道, 使得多角度超视距观察并研究在线人群复杂行为及其组织特性成为可能; 另一方面, 这一研究在社会化媒体营销、共享经济、非军事组织行动中的应用意义愈加显著. 本文引入群体行为动力学和社会运动组织理论的研究, 提出基于ACP的动态网民群体运动组织(Cyber movement organizations, CMOs)研究方法. 本文工作首先使用多智能体建模方法构造双层结构的人工社区模型, 以此为基础对动态网民的个体以及群体动态组织行为展开计算实验探讨, 重点阐释了社区用户的交互行为机制及群体组织活动的建模机制, 为揭示微观个体简单行为对于宏观群体复杂涌现现象的影响奠定基础.
    1)   收稿日期 2019-09-06    录用日期 2020-01-09 Manuscript received September 6, 2019; accepted January 9, 2020 国家自然科学基金项目(61702519), 中国科协青年人才托举工程(2017QNRC001), 英特尔智能网联汽车大学合作研究中心项目(ICRI-IACV), 北京市科委项目(Z181100008918007)资助 Supported by National Natural Science Foundation of China (61702519), Young Elite Scientists Sponsorship Program of China Association of Science and Technology (2017QNRC001), Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles ( ICRI-IACV), and Beijing Municipal Science & Technology Commission (Z181100008918007) 本文责任编委 莫红 Recommended by Associate Editor MO Hong
    2)   1. 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    2. 青岛智能产业技术研究院 青岛 266000    3. 青岛慧拓机器智能有限公司 青岛 266000 4. 中国科学院大学 北京 100049 5. 北京三快在线科技有限公司 北京 100083 6. 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 长沙 410073 1. The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 2. Qingdao Academy of Intelligent Industries, Qingdao 266000 3. Waytous Inc., Qingdao 266000 4. University of Chinese Academy of Sciences, Beijing 100049 5. Beijing Sankuai Online Technology Co., Ltd., Beijing 100083 6. The Research Center for Military Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073
  • 图  1  CMOs的线上线下互动模式

    Fig.  1  Online and offline interaction mode of CMOs

    图  2  网络集群行为的理论解释模型

    Fig.  2  Theoretical interpretation model of network cluster behavior

    图  3  CMOs的人工社会建模和计算实验评估框架

    Fig.  3  The artificial society modeling and computational experiment evaluation framework for CMOs

    图  4  Agent通用结构模型

    Fig.  4  General structure of an agent

    图  5  人工社区的双层结构

    Fig.  5  The double-layer structure of artificial community

    图  6  基于多智能体建模的网络社区计算实验框架

    Fig.  6  Computational experiment framework of network community based on multi-agent modeling

    图  7  兴趣相似度阈值ω变化时, 群体发帖行为的分布情况

    Fig.  7  The distribution of crowd posting behavior when ω changes

    图  8  兴趣相似度阈值ω取[0.01,0.99]时,群体发帖行为分布的参数变化情况

    Fig.  8  The distribution of crowd posting behavior when ω changes

    图  9  初始智能体数量发生变化时,群体评论行为的分布情况

    Fig.  9  The distribution of crowd comment behavior when the number of initial agents changes

    表  1  个体知识相似度阈值$\omega$对于群体发帖行为分布的影响实验参数

    Table  1  Computational experimental parameters for the experiment on the influence of crowd behavior distribution by individual knowledge similarity threshold $\omega$

    Parameters Values
    $\omega$ $0.01\sim 0.09$
    $\varphi$ $0.5$
    ${Max\_A}_a$ $p({Max\_A}_a=X)=0.1$, $X=1.1, 1.2,\cdots,2.0$
    $GrowthRate_a$ $p(gr_a = Y) = 0.1$, $Y = 0.01, 0.02, \cdots, 0.1$
    Initial num of Agents $3\;000$
    Num of new Agents added at each time step 30
    $Knowledge$ $[a,z]$
    $|{AK\_Value}^{T_l}_{a}|=|TV\_Theme^{T_l}_{k}|$ $26$
    $value_{ak\_\omega}$ $p(value_{ak\_\omega} = Z)=0.1$, $Z=0.05, 0.1, \cdots, 0.5$
    C $10$
    Time $1\;000$
    下载: 导出CSV

    表  2  初始智能体数量变化对群体评论行为影响的实验参数

    Table  2  Computational experimental parameters of the effect crowd comment behavior by the number of initial agents

    Parameters Values
    ${Max\_A}_a$ $p({Max\_A}_a=X)=0.1$, $X=1.1, 1.2,\cdots,2.0$
    $GrowthRate_a$ $p(gr_a = Y) = 0.1$, $Y = 0.01, 0.02, \cdots, 0.1$
    Initial num of Agents $100\sim 3\;000$
    Num of new Agents added at each time step 30
    Number of Topics 1 000
    $Knowledge$ $[a,z]$
    $|{AK\_Value}^{T_l}_{a}|=|TV\_Theme^{T_l}_{k}|$ $26$
    $value_{ak\_\omega}$ $p(value_{ak\_\omega} = Z)=0.1$, $Z=0.05, 0.1, \cdots, 0.5$
    $c_1$ $0.9$
    C $10$
    Time $1\;000$
    下载: 导出CSV
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  • 收稿日期:  2019-09-06
  • 录用日期:  2020-01-09
  • 网络出版日期:  2020-04-25
  • 刊出日期:  2020-04-24

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