PDF(1109 KB)
PDF(1109 KB)
PDF(1109 KB)
群体智能及产业集群发展战略研究
Development Strategy of Collective Intelligence and Its Industrial Clusters
群体智能是新一代人工智能的重要组成部分，在激发汇聚创新力量、耦合集成大规模智能系统方面发挥决定性作用，对于促进人工智能与传统产业的深度融合、推动国民经济持续发展都具有重大意义。本文凝练了群体智能的技术体系，总结了其主要的技术方向，包括：多智能体系统与优化决策、无人集群系统、开源群智软件和群智联邦学习等；论述了这些核心技术面向应用场景，形成感知 ‒ 认知 ‒ 决策 ‒ 行动的智能化回路，以分布式数智化模式支撑平台经济，重塑产业发展和数字经济产业生态。根据上述技术体系的赋能对象和应用模式，深入分析了与群体智能相关的核心产业，特别是软件服务产业、智慧城市产业群、基于无人集群的智慧农业和智慧港口产业等对群体智能技术的不同需求和赋能路径。最后，提出了群体智能赋能相关产业的发展建议：持续推动群智开源社区的建设，打造人工智能科技创新生态系统的智力内核，加速无人集群系统的国产化替代、集成攻关和推广应用。
Collective intelligence is an important component of the new generation of artificial intelligence (AI). It plays a decisive role in stimulating and converging innovative forces as well as coupling and integrating large-scale intelligent systems. It is of great significance for promoting deep integration of AI and traditional industries and enabling the sustainable development of the national economy. This study summarizes the overall technical framework of collective intelligence and its major research areas, including:multi-agent systems and optimal decision-making, unmanned swarm systems, open source collective intelligence software, and federated learning. Moreover, it analyzes how these core technologies can be applied in industrial scenarios, in order to establish intelligent processing loops of perception‒cognition‒decision‒action, to support platform economy with distributed intelligence, and to reshape industrial development and digital economy ecosystems. Based on the subjects and application modes of the technical framework, this study analyzes the core industries related to collective intelligence, particularly the software service industry, the smart city industrial cluster, and the intelligent agriculture and port industries based on unmanned swarm systems, by highlighting their significant requirements and empowerment approaches for collective intelligence technologies. Furthermore, this study presents suggestions on how to utilize collective intelligence technologies to foster development of rated industries. It is suggested that we should continuously promote the establishment of open source communities of collective intelligence, enhance the intellectual core of the AI technological innovation ecosystem, and accelerate the domestic substitute of unmanned swarm systems through integrated system research.
群体智能 / 开源软件 / 无人集群 / 联邦学习 / 平台经济
collective intelligence / open source software / unmanned swarm / federated learning / platform economy
| [1] |
Li W, Wu W J, Wang H M, et al. Crowd intelligence in AI 2.0 era [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 15‒43.
|
| [2] |
罗杰, 姜鑫, 郭炳晖, 等. 群体智能系统的动力学模型与群体熵度量 [J]. 中国科学: 信息科学, 2022, 52(1): 99‒110.
|
| [3] |
Cerquides J, Farinelli A, Meseguer P, et al. A tutorial on optimization for multi-agent systems [J]. The Computer Journal, 2014, 57(6): 799‒824.
|
| [4] |
Ramaswamy S. The mechanics and statistics of active matter [J]. Annual Review of Condensed Matter Physics, 2010, 1: 323‒345.
|
| [5] |
Reynolds C W. Flocks, herds and schools: A distributed behavioral model [J]. ACM SIGGRAPH Computer Graphics, 1987, 21(4): 25‒34.
|
| [6] |
Vicsek T, Czirók A, Ben-Jacob E, et al. Novel type of phase transition in a system of self-driven particles [J]. Physical Review Letters, 1995, 75(6): 1226‒1229.
|
| [7] |
Gronauer S, Diepold K. Multi-agent deep reinforcement learning: A survey [J]. Artificial Intelligence Review, 2022, 55(2): 895‒943.
|
| [8] |
Dorigo M, Theraulaz G, Trianni V. Reflections on the future of swarm robotics [J]. Science Robotics, 2020, 5(49): eabe4385.
|
| [9] |
Wang H M, Yin G, Li X, et al. TRUSTIE: A software development platform for crowdsourcing [M]//Li W, Huhns M, Tsai W T, et al. Crowdsourcing. Berlin: Springer, 2015: 165‒190.
|
| [10] |
王怀民, 余跃, 王涛, 等. 群智范式: 软件开发范式的新变革 [J]. 中国科学: 信息科学, 2023, 53(8): 1490‒1502.
|
| [11] |
Yang Q, Liu Y, Chen T J, et al. Federated machine learning: Concept and applications [J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 1‒19.
|
| [12] |
Liu Y, Kang Y, Xing C P, et al. A secure federated transfer learning framework [J]. IEEE Intelligent Systems, 2020, 35(4): 70‒82.
|
| [13] |
Park J S, O´Brien J, Cai C J, et al. Generative agents: Interactive simulacra of human behavior [C]. San Francisco: The 36th Annual ACM Symposium on User Interface Software and Technology, 2023.
|
| [14] |
Reed S, Zolna K, Parisotto E, et al. A generalist agent [EB/OL]. (2022-11-11)[2024-01-31]. http://arxiv.org/abs/2205.06175.pdf.
|
| [15] |
Duan J F, Yu S, Tan H L, et al. A survey of embodied AI: From simulators to research tasks [J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, 6(2): 230‒244.
|
| [16] |
Chen M, Tworek J, Jun H, et al. Evaluating large language models trained on code [EB/OL]. (2021-07-14)[2024-01-31]. http://arxiv.org/abs/2107.03374.pdf.
|
| [17] |
Warnat-Herresthal S, Schultze H, Shastry K L, et al. Swarm learning for decentralized and confidential clinical machine learning [J]. Nature, 2021, 594(7862): 265‒270.
|
| [18] |
Kenney M, Zysman J. The rise of the platform economy [J]. Issues in Science and Technology, 2016, 32(3): 61‒69.
|
| [19] |
"十四五"数字经济发展规划 [EB/OL]. (2022-01-12)[2024-01-31]. https://www.gov.cn/zhengce/content/2022-01/12/content_5667817.htm.
|
| [20] |
"十四五"软件和信息技术服务业发展规划 [EB/OL]. (2021-01-15)[2024-01-31]. https://www.gov.cn/zhengce/zhengceku/2021-12/01/content_5655205.htm.
|
| [21] |
2022 Annual report on China open source contribution [EB/OL].(2022-10-24)[2024-01-31]. https://baike.baidu.com/item/2022中国开源贡献度报告/62103445.
|
| [21] |
2022中国开源贡献度报告 [EB/OL]. (2022-10-24)[2024-01-31]. https://baike.baidu.com/item/2022中国开源贡献度报告/62103445.
|
| [22] |
新思科技. 2020开源安全与风险分析报告 [EB/OL]. (2020-06-01)[2024-01-31]. https://www.synopsys.com/content/dam/synopsys/china/software-integrity/reports.
|
| [23] |
中国开源发展蓝皮书 [EB/OL]. (2023-06-15)[2024-01-31]. https://gitcode.net/COPU/2023.
|
| [24] |
国家信息中心.中国共享经济发展报告2023 [EB/OL]. (2023-02-23)[2024-01-31]. http://www.sic.gov.cn/sic/93/552/557/0223/11819_pc.html.
|
/
| 〈 |
|
〉 |
