群体智能的四个发展阶段

肖人彬

doi:10.1631/FITEE.2300459

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群体智能的四个发展阶段

常规文章 | Updated：2024-07-28

- 群体智能的四个发展阶段
  Cover Article
- Four development stages of collective intelligence
- 信息与电子工程前沿(英文版) 2024年25卷第7期页码：903-916
- Affiliations：
  
  1.School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
  2.Key Laboratory of Image Information Processing and Intelligent Control, Ministry of Education, Wuhan 430074, China
- Author bio：
  
  E-mail: rbxiao@hust.edu.cn
- Funds：
  
  the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China(2018AAA0101200)
- DOI：10.1631/FITEE.2300459
  中图分类号： TP18
- 收稿：2023-07-09，
  
  修回：2024-04-06，
  
  网络出版：2024-06-22，
  
  纸质出版：2024-07-0
- Accepted：
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肖人彬. 群体智能的四个发展阶段[J]. 信息与电子工程前沿(英文版), 2024,25(7):903-916.

Renbin XIAO. Four development stages of collective intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(7): 903-916.
肖人彬. 群体智能的四个发展阶段[J]. 信息与电子工程前沿(英文版), 2024,25(7):903-916. DOI： 10.1631/FITEE.2300459.

Renbin XIAO. Four development stages of collective intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(7): 903-916. DOI： 10.1631/FITEE.2300459.

摘要

中国学者发起的新一代人工智能研究顺应了信息新环境变化的需求，力图将传统人工智能（人工智能1.0）推进到人工智能2.0的新阶段。作为人工智能的重要组成部分之一，群体智能1.0（群智能）正在向群体智能2.0（众智能）阶段发展。通过深度剖析和翔实论证，发现群体智能1.0与群体智能2.0存在不相容性，据此搭建它们之间的桥梁——以生物合作行为仿生为主的群体智能1.5，作为群体智能1.0到群体智能2.0的过渡，以实现两者的相容。进而对钱学森提出的大成智慧进行新的诠释，将其作为人类智慧仿生的高级阶段——群体智能3.0，指出在深度不确定性下的大模型和大数据的双轮驱动是从群体智能2.0通向群体智能3.0的进化途径并加以阐述，由此提出群体智能的4个发展阶段，形成由上述阶段共居一体所组成的群体智能发展的完整架构，这些不同阶段渐进发展，具有良好的相容性。鉴于合作在群体智能发展阶段中的主导作用，分别论述群体智能中的3种合作类型：低等生物的间接调节型合作、高等生物的直接沟通型合作和人的共享意向型合作。在群体智能中，分工乃是实现合作的主要形式，为此阐释分工行为复杂性与分工类型的关系。最后，基于对所提出的群体智能4个发展阶段整体架构的全方位认识，对群体智能未来的发展方向和研究前景进行展望。

Abstract

The new generation of artificial intelligence (AI) research initiated by Chinese scholars conforms to the needs of a new information environment changes

and strives to advance traditional artificial intelligence (AI 1.0) to a new stage of AI 2.0. As one of the important components of AI

collective intelligence (CI 1.0)

i.‍e.

swarm intelligence

is developing to the stage of CI 2.0 (crowd intelligence). Through in-depth analysis and informative argumentation

it is found that an incompatibility exists between CI 1.0 and CI 2.0. Therefore

CI 1.5 is introduced to build a bridge between the above two stages

which is based on bio-collaborative behavioral mimicry. CI 1.5 is the transition from CI 1.0 to CI 2.0

which contributes to the compatibility of the two stages. Then

a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given. The meta-synthesis of wisdom

as an improvement of crowd intelligence

is an advanced stage of bionic intelligence

i.‍e.

CI 3.0. It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0

and some elaboration is made. As a result

we propose four development stages (CI 1.0

CI 1.5

CI 2.0

and CI 3.0)

which form a complete framework for the development of CI. These different stages are progressively improved and have good compatibility. Due to the dominant role of cooperation in the development stages of CI

three types of cooperation in CI are discussed: indirect regulatory cooperation in lower organisms

direct communicative cooperation in higher organisms

and shared intention based collaboration in humans. Labor division is the main form of achieving cooperation and

for this reason

this paper investigates the relationship between the complexity of behavior and types of labor division. Finally

based on the overall understanding of the four development stages of CI

the future development direction and research issues of CI are explored.

关键词

Keywords

references

An XM , Ma GH , Song G , 2018 . Origins and evolution of meta-synthesis approach . Syst Eng , 36 ( 10 ): 1 - 13 (in Chinese) .

Askarzadeh A , 2016 . A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm . Comput Struct , 169 : 1 - 12 . https://doi.org/10.1016/j.compstruc.2016.03.001 https://doi.org/10.1016/j.compstruc.2016.03.001

Axelrod R , Hamilton WD , 1981 . The evolution of cooperation . Science , 211 ( 4489 ): 1390 - 1396 . https://doi.org/10.1126/science.7466396 https://doi.org/10.1126/science.7466396

Bernstein E , Shore J , Lazer D , 2018 . How intermittent breaks in interaction improve collective intelligence . Proc Nat Acad Sci USA , 115 ( 35 ): 8734 - 8739 . https://doi.org/10.1073/pnas.1802407115 https://doi.org/10.1073/pnas.1802407115

Bonabeau E , Dorigo M , Theraulaz G , 1999 . Swarm Intelligence: from Natural to Artificial Systems . Oxford University Press , New York, USA .

Cai W , Yang CY , 2013 . Basic theory and methodology on extenics . Chin Sci Bull , 58 ( 13 ): 1190 - 1199 (in Chinese) . https://doi.org/10.1360/972012-1472 https://doi.org/10.1360/972012-1472

Chen X , Xiao RB , 2023 . A Computational Experimental Study of Rumor Propagation and Opinion Evolution . Huazhong University of Science & Technology Press , Wuhan, China (in Chinese) .

China Artificial Intelligence 2.0 Development Strategy Research Project Team , 2018 . Strategic Research on Artificial Intelligence 2.0 in China (Volume I) . Zhejiang University Press , Hangzhou, China (in Chinese) .

Dai RW , 2009 . The proposal and recent development of metasynthetic method(M) from qualitative to quantitative . Chin J Nat , 31 ( 6 ): 311 - 314, 326 (in Chinese) . https://doi.org/10.3969/j.issn.0253-9608.2009.06.001 https://doi.org/10.3969/j.issn.0253-9608.2009.06.001

Galesic M , Barkoczi D , Berdahl AM , et al. , 2023 . Beyond collective intelligence: collective adaptation . J Royal Soc Interf , 20 ( 200 ): 20220736 . https://doi.org/10.1098/rsif.2022.0736 https://doi.org/10.1098/rsif.2022.0736

Grinnell J , McComb K , 2001 . Roaring and social communication in African lions: the limitations imposed by listeners . Anim Behav , 62 ( 1 ): 93 - 98 . https://doi.org/10.1006/anbe.2001.1735 https://doi.org/10.1006/anbe.2001.1735

Grinnell J , Packer C , Pusey AE , 1995 . Cooperation in male lions: kinship, reciprocity or mutualism? Anim Behav , 49 ( 1 ): 95 - 105 . https://doi.org/10.1016/0003-3472(95)80157-X https://doi.org/10.1016/0003-3472(95)80157-X

Hare B , Call J , Tomasello M , 2001 . Do chimpanzees know what conspecifics know? Anim Behav , 61 ( 1 ): 139 - 151 . https://doi.org/10.1006/anbe.2000.1518 https://doi.org/10.1006/anbe.2000.1518

Hilbert M , López P , 2011 . The world's technological capacity to store, communicate, and compute information . Science , 332 ( 6025 ): 60 - 65 . https://doi.org/10.1126/science.1200970 https://doi.org/10.1126/science.1200970

Hills TT , Todd PM , Lazer D , et al. , 2015 . Exploration versus exploitation in space, mind, and society . Trends Cogn Sci , 19 ( 1 ): 46 - 54 . https://doi.org/10.1016/j.tics.2014.10.004 https://doi.org/10.1016/j.tics.2014.10.004

Jiang XY , Li S , 2018 . BAS: beetle antennae search algorithm for optimization problems . Int J Robot Contr , 1 ( 1 ): 1 - 5 . https://doi.org/10.5430/ijrc.v1n1p1 https://doi.org/10.5430/ijrc.v1n1p1

Karsai I , 1999 . Decentralized control of construction behavior in paper wasps: an overview of the stigmergy approach . Artif Life , 5 ( 2 ): 117 - 136 . https://doi.org/10.1162/106454699568719 https://doi.org/10.1162/106454699568719

Kennedy J , Eberhart RC , Shi YH , 2001 . Swarm Intelligence . Morgan Kaufmann Publishers , San Francisco, USA .

Kshetri N , Dwivedi YK , Davenport TH , et al. , 2024 . Generative artificial intelligence in marketing: applications, opportunities, challenges, and research agenda . Int J Inform Manag , 75 : 102716 . https://doi.org/10.1016/j.ijinfomgt.2023.102716 https://doi.org/10.1016/j.ijinfomgt.2023.102716

Li SY , Li Y , Lin YM , 2019 . Intelligent Optimization Algorithms and Emergent Computation . Tsinghua University Press , Beijing, China (in Chinese) .

Li W , Wu WJ , Wang HM , et al. , 2017 . Crowd intelligence in AI 2.0 era . Front Inform Technol Electron Eng , 18 ( 1 ): 15 - 43 . https://doi.org/10.1631/FITEE.1601859 https://doi.org/10.1631/FITEE.1601859

Lin SJ , Dong C , Chen MZ , et al. , 2018 . Summary of new group intelligent optimization algorithms . Comput Eng Appl , 54 ( 12 ): 1 - 9 (in Chinese) . https://doi.org/10.3778/j.issn.1002-8331.1803-0260 https://doi.org/10.3778/j.issn.1002-8331.1803-0260

Liu SJ , Yang Y , Zhou YQ , 2018 . A swarm intelligence algorithm—lion swarm optimization . Patt Recogn Artif Intell , 31 ( 5 ): 431 - 441 (in Chinese) . https://doi.org/10.16451/j.cnki.issn1003-6059.201805005 https://doi.org/10.16451/j.cnki.issn1003-6059.201805005

Melis AP , Hare B , Tomasello M , 2008 . Do chimpanzees reciprocate received favours? Anim Behav , 76 ( 3 ): 951 - 962 . https://doi.org/10.1016/j.anbehav.2008.05.014 https://doi.org/10.1016/j.anbehav.2008.05.014

Nick , 2017 . A Brief History of Artificial Intelligence . Posts & Telecom Press , Beijing, China (in Chinese) .

Pan YH , 2016 . Heading toward artificial intelligence 2.0 . Engineering , 2 ( 4 ): 409 - 413 . https://doi.org/10.1016/J.ENG.2016.04.018 https://doi.org/10.1016/J.ENG.2016.04.018

Passino KM , 2002 . Biomimicry of bacterial foraging for distributed optimization and control . IEEE Contr Syst Mag , 22 ( 3 ): 52 - 67 . https://doi.org/10.1109/MCS.2002.1004010 https://doi.org/10.1109/MCS.2002.1004010

Pei J , Deng L , Song S , et al. , 2019 . Towards artificial general intelligence with hybrid Tianjic chip architecture . Nature , 572 ( 7767 ): 106 - 111 . https://doi.org/10.1038/s41586-019-1424-8 https://doi.org/10.1038/s41586-019-1424-8

Predic B , Stojanovic D , 2015 . Enhancing driver situational awareness through crowd intelligence . Expert Syst Appl , 42 ( 11 ): 4892 - 4909 . https://doi.org/10.1016/j.eswa.2015.02.013 https://doi.org/10.1016/j.eswa.2015.02.013

Qian XS , Yu JY , Dai RW , 1990 . A new area of science—‍open complex giant system and its methodology . Chin J Nat , 13 ( 1 ): 3 - 10, 64 (in Chinese) .

Rajakumar BR , 2012 . The lion's algorithm: a new nature-inspired search algorithm . Proc Technol , 6 : 126 - 135 . https://doi.org/10.1016/j.protcy.2012.10.016 https://doi.org/10.1016/j.protcy.2012.10.016

Reynolds CW , 1987 . Flocks, herds and schools: a distributed behavioral model . ACM SIGGRAPH Comput Graph , 21 ( 4 ): 25 - 34 . https://doi.org/10.1145/37402.37406 https://doi.org/10.1145/37402.37406

Riedl C , Kim YJ , Gupta P , et al. , 2021 . Quantifying collective intelligence in human groups . Proc Nat Acad Sci USA , 118 ( 21 ): e2005737118 . https://doi.org/10.1073/pnas.2005737118 https://doi.org/10.1073/pnas.2005737118

Samuelson PA , Nordhaus WD , 2010 . Economics ( 19 th Ed. ). McGraw-Hill , New York, USA .

Schaller GB , 1972 . The Serengeti Lion: a Study of Predator-Prey Relations . University of Chicago Press , Chicago, USA .

Senge PM , 1990 . The Fifth Discipline: the Art and Practice of the Learning Organization . Doubleday/Currency , New York, USA .

Stander PE , Stander J , 1988 . Characteristics of lion roars in Etosha National Park . Madoqua , 1988 ( 4 ): 315 - 318 .

Stanton MCB , Roelich K , 2021 . Decision making under deep uncertainties: a review of the applicability of methods in practice . Technol Forecast Soc Change , 171 : 120939 . https://doi.org/10.1016/j.techfore.2021.120939 https://doi.org/10.1016/j.techfore.2021.120939

Wang B , Jin XP , Cheng B , 2012 . Lion pride optimizer: an optimization algorithm inspired by lion pride behavior . Sci China Inform Sci , 55 ( 10 ): 2369 - 2389 . https://doi.org/10.1007/s11432-012-4548-0 https://doi.org/10.1007/s11432-012-4548-0

Wang WH , 2007 . Qian Xuesen's Academic Thought . Sichuan Science and Technology Press , Chengdu, China (in Chinese) .

Wei J , Tay Y , Bommasani R , et al. , 2022 . Emergent abilities of large language models . https://doi.org/10.48550/arXiv.2206.07682 https://doi.org/10.48550/arXiv.2206.07682

Wu F , Lu CW , Zhu MJ , et al. , 2020 . Towards a new generation of artificial intelligence in China . Nat Mach Intell , 2 ( 6 ): 312 - 316 . https://doi.org/10.1038/s42256-020-0183-4 https://doi.org/10.1038/s42256-020-0183-4

Wu HS , Xiao RB , 2020 . Flexible wolf pack algorithm for dynamic multidimensional knapsack problems . Research , 2020 : 1762107 . https://doi.org/10.34133/2020/1762107 https://doi.org/10.34133/2020/1762107

Wu HS , Xiao RB , 2021 . A new approach to swarm intelligence: role-matching labor division of a wolf pack . CAAI Trans Intell Syst , 16 ( 1 ): 125 - 133 (in Chinese) . https://doi.org/10.11992/tis.202007043 https://doi.org/10.11992/tis.202007043

Wu LF , Wang DS , Evans JA , 2019 . Large teams develop and small teams disrupt science and technology . Nature , 566 ( 7744 ): 378 - 382 . https://doi.org/10.1038/s41586-019-0941-9 https://doi.org/10.1038/s41586-019-0941-9

Xiao RB , 2013 . Swarm Intelligence in Complex Systems . Science Press , Beijing, China (in Chinese) .

Xiao RB , Chen ZZ , 2023 . From swarm intelligence optimization to swarm intelligence evolution . J Nanchang Inst Technol , 42 ( 1 ): 1 - 10 (in Chinese) . https://doi.org/10.3969/j.issn.1006-4869.2023.01.001 https://doi.org/10.3969/j.issn.1006-4869.2023.01.001

Xiao RB , Hou JD , 2024 . Running mechanism of the new national system‍—from the view of meta-synthesis approach and meta-synthesis of wisdom . Chin J Syst Sci , 32 ( 2 ):‍ 7‍3 - 79, 85 (in Chinese) .

Xiao RB , Tao ZW , 2007 . Research progress of swarm intelligence . J Manag Sci China , 10 ( 3 ): 80 - 96 (in Chinese) . https://doi.org/10.3321/j.issn:1007-9807.2007.03.011 https://doi.org/10.3321/j.issn:1007-9807.2007.03.011

Xiao RB , Wang YC , 2019 . Research progress of self-organized labor division in swarm intelligence . Inform Contr , 48 ( 2 ): 129 - 139, 148 (in Chinese) . https://doi.org/10.13976/j.cnki.xk.2019.8643 https://doi.org/10.13976/j.cnki.xk.2019.8643

Xiao RB , Feng ZH , Wang JH , 2022 . Collective intelligence: conception, research progresses and application analyses . J Nanchang Inst Technol , 41 ( 1 ): 1 - 21 (in Chinese) . https://doi.org/10.3969/j.issn.1006-4869.2022.01.002 https://doi.org/10.3969/j.issn.1006-4869.2022.01.002

Xiao RB , Li G , Chen ZZ , 2023 . Research progress and prospect of evolutionary many-objective optimization . Contr Dec , 38 ( 7 ): 1761 - 1788 (in Chinese) . https://doi.org/10.13195/j.kzyjc.2022.2167 https://doi.org/10.13195/j.kzyjc.2022.2167

Xue JK , Shen B , 2020 . A novel swarm intelligence optimization approach: sparrow search algorithm . Syst Sci Contr Eng , 8 ( 1 ): 22 - 34 . https://doi.org/10.1080/21642583.2019.1708830 https://doi.org/10.1080/21642583.2019.1708830

Yazdani M , Jolai F , 2016 . Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm . J Comput Des Eng , 3 ( 1 ): 24 - 36 . https://doi.org/10.1016/j.jcde.2015.06.003 https://doi.org/10.1016/j.jcde.2015.06.003

Zhang B , Zhu J , Su H , 2023 . Toward the third generation artificial intelligence . Sci China Inform Sci , 66 ( 2 ): 121101 . https://doi.org/10.1007/s11432-021-3449-x https://doi.org/10.1007/s11432-021-3449-x

Zhang W , Mei H , 2020 . A constructive model for collective intelligence . Nat Sci Rev , 7 ( 8 ): 1273 - 1277 . https://doi.org/10.1093/nsr/nwaa092 https://doi.org/10.1093/nsr/nwaa092

Zheng ZM , Lv JH , Wei W , et al. , 2021 . Refined intelligence theory: artificial intelligence regarding complex dynamic objects . Sci Sin Inform , 51 ( 4 ): 678 - 690 (in Chinese) . https://doi.org/10.1360/SSI-2020-0158 https://doi.org/10.1360/SSI-2020-0158

Zhong YX , 2018 . Mechanism-based artificial intelligence theory: a universal theory of artifical intelligence . CAAI Trans Intell Syst , 13 ( 1 ): 2 - 18 (in Chinese) . https://doi.org/10.11992/tis.201711032 https://doi.org/10.11992/tis.201711032

Zhou J , Ke P , Qiu X , et al. , 2024 . ChatGPT: potential, prospects, and limitations . Front Inform Technol Electron Eng , 25 ( 1 ): 6 - 11 . https://doi.org/10.1631/FITEE.2300089 https://doi.org/10.1631/FITEE.2300089

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