摘要
伴随着人工智能的发展和进步,大量智能设备被应用于战场。在复杂多变的战场环境下,这些智能设备存在着复杂军事环境下识别困难、移动端计算资源紧缺以及人工智能技术解释性差等问题。为此,通过将网络科学的理论、方法和工具应用于人工智能系统的设计和分析,为解决上述问题提供了一种可行的方案。梳理总结了国内外网络科学赋能人工智能的研究进展,从输入输出端的图表征、模型架构端的图表征、决策逻辑端的图表征3个方面,分别阐述了网络科学赋能人工智能的研究现状,并讨论了网络科学赋能人工智能面临的挑战以及未来可能的发展方向。
With the development and progress of artificial intelligence,a large number of smart devices are used on the battlefield.In complex and ever-changing battlefield environments,these smart devices face challenges such as difficulty in recognition under complex military environments,limited computational resources on mobile devices,and poor interpretability of AI technologies.To address these issues,this paper proposes a practical solution for applying the theories,methods,and tools of network science to the design and analysis of artificial intelligence systems,and summarizes the research progress of network science empowering artificial intelligence at home and abroad and elaborates on the current research status of network science empowering artificial intelligence from three aspects:graph representation on the input and output end,graph representation on the model architecture end,and graph representation on the decision logic end.It also discusses the challenges faced by network science in empowering artificial intelligence and possible future development directions.
作者
陆耀
陈奕帆
杨淇然
方宇杰
宣琦
LU Yao;CHEN Yifan;YANG Qiran;FANG Yujie;XUAN Qi(Institute of Cyberspace Security,Zhejiang University of Technology,Hangzhou 310023,China;Binjiang Institute of Artificial Intelligence,Zhejiang University of Technology,Hangzhou 310056,China)
出处
《指挥与控制学报》
北大核心
2025年第5期540-549,共10页
Journal of Command and Control
基金
国家自然科学基金(62301492,U21B2001)
浙江省重点研发计划项目(2022C01018)资助。
关键词
网络科学
人工智能
可解释
边缘计算
模型优化
network science
artificial intelligence
interpretability
edge computing
model optimization
