群智体系网络结构的自治调节:从生物调控网络结构谈起

doi:10.11896/jsjkx.210200161

计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 184-189.doi: 10.11896/jsjkx.210200161

群智体系网络结构的自治调节:从生物调控网络结构谈起

殷子樵^1,2,3,4, 郭炳晖^1,2,3,4, 马双鸽⁵, 米志龙^1,2,3,4, 孙怡帆⁶, 郑志明^1,2,3,4

1 北京航空航天大学数学科学学院北京100191
2 鹏城实验室广东深圳518055
3 教育部数学信息与行为重点实验室北京100191
4 北京大数据科学与脑机智能高精尖中心北京100191
5 耶鲁大学公共卫生学院康涅狄格州纽黑文06520
6 中国人民大学统计学院北京100872

收稿日期:2021-02-25 修回日期:2021-04-09 出版日期:2021-05-15 发布日期:2021-05-09
通讯作者: 郭炳晖(guobinghui@buaa.edu.cn)
基金资助:
科技创新2030-“新一代人工智能”重大项目(2018AAA0102301);国家自然科学基金项目(11671025);民机项目(MJ-F2012-04)

Autonomous Structural Adjustment of Crowd Intelligence Network: Begin from Structure of Biological Regulatory Network

YIN Zi-qiao^1,2,3,4, GUO Bing-hui^1,2,3,4, MA Shuang-ge⁵, MI Zhi-long^1,2,3,4, SUN Yi-fan⁶, ZHENG Zhi-ming^1,2,3,4

1 School of Mathematical Sciences,Beihang University,Beijing 100191,China
2 Peng Cheng Laboratory,Shenzhen,Guangdong 518055,China
3 Key Laboratory of Mathematics Informatics and Behavioral Semantics,Ministry of Education,Beijing 100191,China
4 Beijing Advanced Innovation Center for Big Data Brain Computing,Beijing 100191,China
5 School of Public Health,Yale University,New Haven,Connecticut 06520,USA
6 School of Statistics,Renmin University of China,Beijing 100872,China

Received:2021-02-25 Revised:2021-04-09 Online:2021-05-15 Published:2021-05-09
About author:YIN Zi-qiao,born in 1996,Ph.D,is a member of China Computer Federation.His main research interests include computational biology and complex intelligent systems.(yinziqiao@buaa.edu.cn)
GUO Bing-hui,born in 1982,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include data science and complex intelligent systems.
Supported by:
Artificial Intelligence Project(2018AAA0102301) ,National Natural Science Foundation of China (11671025) and Fundamental Research of Civil Aircraft (MJ-F-2012-04).

摘要/Abstract

摘要： 群体智能作为人工智能2.0时代最突出的研究方向之一,受到了工业界和学术界研究者们的广泛关注。传统的人工智能模型倾向于使用全连通网络结构,认为全连通网络结构的人工智能模型具有更高的准确率。然而,在面对存在强干扰的复杂对抗环境时,智能决策体系需要面对由通信干扰甚至针对性攻击所造成的系统结构扰动。在不失准确性的前提下,为了能够更快、更稳定地进行实时响应,需要智能系统的结构具有实时自治响应调整机制。此类自治响应调整机制在自然界中的调控网络中很常见。文中通过引入DReSS表征族来定量分析随机网络与真实网络中结构扰动对于系统演化的影响,对比了不同网络结构对于结构扰动的抗干扰能力,并提出了一套群智体系网络结构的自治调节构想。

关键词: 布尔网络, 动力系统, 复杂网络, 计算生物学, 群体智能

Abstract: As one of the most important research directions in the artificial intelligence 2.0 era,crowd intelligence has received extensive attention from researchers in the industry and academia.Traditional artificial intelligence models tend to use the fully connected network structure to achieve higher accuracy.However,in a complex confrontation environment with stronginterfe-rence,the intelligent decision-making system needs to face system structural perturbations caused by communication interference or even targeted attack.Without losing too much accuracy,in order to achieve the demand for faster and more stable real-time response,it is necessary for intelligent system to have a real-time autonomous response structural adjustment mechanism.Such autonomous corresponding adjustment mechanisms are common in regulatory networks for biological systems.By introducing DReSS index family,this research quantitatively analyzes the impact of structural perturbations on state spaces in random and real networks.The anti-interference feature of different network structures against structural perturbations is compared.An autonomous adjustment concepts for the network structure of the crowd intelligence systems is proposed in this research.

Key words: Boolean networks, Complex networks, Computational biology, Crowd intelligence, Dynamic systems

中图分类号:

TP181

殷子樵, 郭炳晖, 马双鸽, 米志龙, 孙怡帆, 郑志明. 群智体系网络结构的自治调节:从生物调控网络结构谈起[J]. 计算机科学, 2021, 48(5): 184-189. https://doi.org/10.11896/jsjkx.210200161

YIN Zi-qiao, GUO Bing-hui, MA Shuang-ge, MI Zhi-long, SUN Yi-fan, ZHENG Zhi-ming. Autonomous Structural Adjustment of Crowd Intelligence Network: Begin from Structure of Biological Regulatory Network[J]. Computer Science, 2021, 48(5): 184-189. https://doi.org/10.11896/jsjkx.210200161

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群智体系网络结构的自治调节:从生物调控网络结构谈起

Autonomous Structural Adjustment of Crowd Intelligence Network: Begin from Structure of Biological Regulatory Network

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