Computer Science ›› 2021, Vol. 48 ›› Issue (5): 184-189.doi: 10.11896/jsjkx.210200161

• Artificial Intelligence • Previous Articles     Next Articles

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

YIN Zi-qiao1,2,3,4, GUO Bing-hui1,2,3,4, MA Shuang-ge5, MI Zhi-long1,2,3,4, SUN Yi-fan6, ZHENG Zhi-ming1,2,3,4   

  1. 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.(
    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).

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

CLC Number: 

  • TP181
[1]LI W,WU W,WANG H,et al.Crowd intelligence in AI 2.0 era[J].Frontiers of Information Technology & Electronic Engineering,2017,18(1):15-43.
[2]CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Deeplab:Semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(4):834-848.
[3]DONG M,WEN S,ZENG Z,et al.Sparse fully convolutional network for face labeling[J].Neurocomputing,2019,331:465-472.
[4]YIN Z,GUO B,MI Z,et al.Gene saturation:an approach to assess exploration stage of gene interaction networks[J].Scientific Reports,2019,9(1):1-17.
[5]YIN Z,GUO B,MA S,et al.DReSS:a method to quantitatively describe the influence of structural perturbations on state spaces of genetic regulatory networks[J/OL].Briefings in Bioinforma-tics.
[6]VIDAL M,CUSICK M E,BARABÁSIA L.Interactome net-works and human disease[J].Cell,2011,144(6):986-998.
[7]BARABÁSI A L,OLTVAI Z N.Network biology:understan-ding the cell's functional organization[J].Nature reviews gene-tics,2004,5(2):101-113.
[8]KRASENSKY J,JONAK C.Drought,salt,and temperaturestress-induced metabolic rearrangements and regulatory networks[J].Journal of Experimental Botany,2012,63(4):1593-1608.
[9]PHUKAN U J, JEENA G S, SHUKLA R K.WRKY transcription factors:molecular regulation and stress responses in plants[J].Frontiers in Plant Science,2016,7:760.
[10]BASSEL G W,LAN H,GLAAB E,et al.Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions[J].Proceedings of the Natio-nal Academy of Sciences,2011,108(23):9709-9714.
[11]WANG E,ZAMAN N,MCGEE S,et al.Predictive genomics:a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data[C]//Seminars in cancer biology.Academic Press,2015,30:4-12.
[12]NECSULEA A,SOUMILLON M,WARNEFORSM,et al.The evolution of lncRNA repertoires and expression patterns in tetrapods[J].Nature,2014,505(7485):635-640.
[13]CUPERUS J T,FAHLGREN N,CARRINGTONJ C.Evolution and functional diversification of MIRNA genes[J].The Plant Cell,2011,23(2):431-442.
[14]ELDAR A,ELOWITZM B.Functional roles for noise in genetic circuits[J].Nature,2010,467(7312):167-173
[15]XU C,DUAN H,LIU F.Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning[J].Aerospace Science and Technology,2010,14(8):535-541.
[16]ZHANG S,ZHOU Y,LI Z,et al.Grey wolf optimizer for unmanned combat aerial vehicle path planning[J].Advances in Engineering Software,2016,99:121-136.
[17]DENG W,XU J,ZHAO H.An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem[J].IEEE Access,2019,7:20281-20292.
[18]LI F,LONG T,LU Y,et al.The yeast cell-cycle network is robustly designed[J].Proceedings of the National Academy of Sciences,2004,101(14):4781-4786.
[19]DAVIDICH M I,BORNHOLDT S.Boolean network model predicts cell cycle sequence of fission yeast[J].PloS one,2008,3(2):e1672.
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