Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210900249-7.doi: 10.11896/jsjkx.210900249

• Artificial Intelligence • Previous Articles     Next Articles

Cooperation and Confrontation in Crowd Intelligence

ZHU Di-di1, WU Chao2   

  1. 1 College of Computer Science and Technology,Zhejiang University,Hangzhou 310013,China
    2 School of Public Affairs,Zhejiang University,Hangzhou 310058,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:ZHU Di-di,born in 1998,postgraduate.Her main research interests include fe-derated learning and domain adaptation.
    WU Chao,born in 1972,Ph.D,professor,Ph.D supervisor.Hismain research interests include federated learning and so on.
  • Supported by:
    National Natural Science Foundation of China(U19B2042) and Zhejiang Provincial Natural Science Foundation of China(LY19F020051).

Abstract: Crowd intelligence has rich connotations and denotations.Its algorithms include both the early algorithms based on the characteristics of biological groups(particle swarm optimization,ant colony algorithm,etc.) and the later large-scale crowd algorithms based on network interconnection(multi-agent system,crowd intelligence perception,federated learning,etc.).The core idea of these crowd intelligence algorithms is cooperation or confrontation.Collaboration can combine the limited intelligence of individuals into the powerful intelligence of the group.However,collaboration itself has certain limitations,which may lead to the over-dependence between individuals and the unfairness of the system.Confrontation can overcome this limitation,and its basic idea is that individuals seek their maximum interests through the game.Therefore,cooperation and confrontation are indispensable.It is the inevitable development trend of a crowd intelligence to promote cooperation with confrontation,and to build a crowd intelligence ecology in which cooperation and confrontation coexist.This paper mainly focuses on the cooperation and confrontation methods of crowd intelligence algorithms,expounds on the classical crowd intelligence algorithms,and prospects the next development direction of emerging crowd intelligence algorithms.

Key words: Crowd intelligence, Federated learning, Multi-agent system, Confrontation, Game

CLC Number: 

  • TP191
[1]BENI G.The concept of cellular robotic system[C]//Procee-dings IEEE International Symposium on Intelligent Control 1988.IEEE,1988:57-62
[2]ZHAO J,ZHANG X T,LI J M,et al.Research review of crowd intelligence 2.0 [J].Computer Engineering,2019,45(12):7.
[3]MCMAHAN B,MOORE E,RAMAGE D,et al.Communica-tion-efficient learning of deep networks from decentralized data[C]//Artificial Intelligence and Statistics.PMLR,2017:1273-1282.
[4]DORIGO M,GAMBARDELLA L M.Ant colonies for the traveling salesman problem[J].Biosystems,1997,43(2):73-81.
[5]KENNEDY J,EBERHART R.Particle swarm optimization[C]//ICNN95-International Conference on Neural Networks.2002.
[6]LIU J,CHEN Z Q,LIU Z X.Research Progress of Multi-agent System and its Cooperative Control [J].Journal of Intelligent Systems,2010,5(1):1-9.
[7]LIU Y H.Crowd Sourcing computing[J].Communications ofthe China Computer Federation,2012,8(10):38-41.
[8]SUN J,WANG J,CHEN J,et al.Cooperative communicationbased on swarm intelligence:vision,model,and key technology[J].SCIENTIASINICA Informationis,2020,50(3):307-317.
[9]DUAN H,QIU H.Unmanned aerial vehicle swarm autonomous control based on swarm intelligence[M].Beijing:Science Press,2018.
[10]LAN S F,LIU S.Overview of research on Cuckoo search algorithm[J].Computer Engineering and Design,2015,36(4):1063-1067.
[11]LI X L.A New Intelligent Optimization Method-Artificial Fish School Algorithm [D].Hangzhou:Zhejiang University,2003.
[12]WU H,ZHANG F,WU L.New swarm intelligence algorithm-wolf pack algorithm [J].System Engineering and Electronics,2010,35(11):2430-2438.
[13]ZHANG W,MEI H.A constructive model for collective intelligence[J].National Science Review,2020,7(8):1273-1277.
[14]LITTMAN M L.Markov games as a framework for multi-agentreinforcement learning[C]//Machine Learning Proceedings 1994.Elsevier,1994:157-163.
[15]WU Y,ZENG J R,PENG H,et al.Survey on incentive mechanisms for crowd sensing[J].Ruan Jian Xue Bao/Journal of Softwar,2016,27(8):2025-2047.
[16]DORIGO M,MANIEZZO V,COLORNI A.Ant system:optimization by a colony of cooperating agents[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),1996,26(1):29-41.
[17]CLAUS C,BOUTILIER C.The dynamics of reinforcementlearning in cooperative multi-agent systems[C]//Proc. AAAI-98.1998.
[18]SPIROS K,DANIEL K.Reinforcement learning of coordination in cooperative mas[C]//The 18th National Conference on AI.Alberta,Canada:ACM Press.2002:326-331.
[19]YANG Y,LUO R,LI M,et al.Mean field multi-agent reinforcement learning[C]//International Conference on Machine Lear-ning.PMLR,2018:5571-5580.
[20]LIN T,KONG L,STICH S U,et al.Ensemble distillation forrobust model fusion in federated learning[J].arXiv:2006.07242,2020.
[21]HINTON G,VINYALS O,DEAN J.Distilling the knowledge in a neural network[J].arXiv:1503.02531,2015.
[22]NASH J F.Equilibrium points in n-person games[J].Procee-dings of the National Academy of Sciences,1950,36(1):48-49.
[23]HU J,WELLMAN M P.Nash q-learning for general-sum sto-chastic games[J].Journal of Machine Learning Research,2003,4(Nov):1039-1069.
[24]WU Y,ZENG J R,PENG H,et al.Survey on incentive mechanisms for crowd sensing[J].Journal of Software,2016,27(8):2025-2047.
[25]NG K L,CHEN Z,LIU Z,et al.A multi-player game for stu-dying federated learning incentive schemes[C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence(IJCAI 2020).2020:5179-5281.
[26]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial networks[J].arXiv:1406.2661,2014.
[27]JEONG E,OH S,KIM H,et al.Communication-efficient on-device machine learning:Federated distillation and augmentation under non-iid private data[J].arXiv:1811.11479,2018.
[28]AUGENSTEIN S,MCMAHAN H B,RAMAGE D,et al.Ge-nerative models for effective ml on private,decentralized datasets[J].arXiv:1911.06679,2019.
[29]ZHANG J,CHEN J,WU D,et al.Poisoning attack in federated learning using generative adversarial nets[C]//2019 18th IEEE International Conference on Trust,Security and Privacy in Computing And Communications/13th IEEE International Conference On Big Data Science and Engineering(TrustCom/BigDataSE).IEEE,2019:374-380.
[30]ZHAO Y,CHEN J,ZHANG J,et al.Pdgan:a novel poisoning defense method in federated learning using generative adversarialnetwork[C]//International Conference on Algorithms and Architectures for Parallel Processing.Springer,2019:595-609.
[31]RASOULI M,SUN T,RAJAGOPAL R.Fedgan:Federated ge-nerative adversarial networks for distributed data[J].arXiv:2006.07228,2020.
[32]FAN C,LIU P.Federated generative adversarial learning[C]//Chinese Conference on Pattern Recognition and Computer Vision(PRCV).Springer,2020:3-15.
[33]QU H,ZHANG Y,CHANG Q,et al.Learn distributedgan with temporary discriminators[C]//European Conference on Computer Vision.Springer,2020:175-192.
[34]WANG H,KAPLAN Z,NIU D,et al.Optimizing federatedlearning on non-iid data with reinforcement learning[C]//IEEE INFOCOM 2020-IEEE Conference on Computer Communications.IEEE,2020:1698-1707.
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