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可解释的多代理强化学习框架,用于无人机群集式网络中安全和适应性通信.
Hend Khalid Alkahtani1, Ybytayeva Galiya2, Bekarystankyzy Akbayan3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia.
Scientific reports
|March 2, 2026
概括
本研究介绍了一种可解释的多代理强化学习 (EMARL) 框架,用于安全和可解释的无人机群通信. 该EMARL系统提高了数据包的交付,准确性和效率,同时减少了延迟和错误的阳性,即使在攻击下.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 无人驾驶飞行器 (UAV) 群需要安全,透明和可解释的通信来执行关键操作.
- 现有的通信协议难以满足动态和潜在的敌对无人机群环境的需求.
- 在自主系统中对可解释AI (XAI) 的需求正在增长,以获得信任和问责制.
研究的目的:
- 为智能和安全的飞行特设网络 (FANET) 提出一个可解释的多代理强化学习 (EMARL) 框架.
- 提高无人机群通信的安全性,可解释性和性能.
- 确保基于当地观察,学习政策和信任估计的自主决策.
主要方法:
- 开发了一个EMARL系统,集成多代理深度决定性政策梯度 (MADDPG) 以实现分散式学习.
- 整合了基于信任的安全系统和可解释的AI (XAI) 技术 (SHAP,LIME,注意力可视化).
- 使用NS-3和AirSim模拟网络和UAV移动性,使用基于Python的MARL引擎进行政策培训.
主要成果:
- 在数据包交付比率 (PDR),准确性和能源效率方面,EMARL在传统协议 (AODV,Q-Routing) 上表现优越.
- 该框架显著降低了延迟和错误阳性率,证明了对干扰和Sybil攻击的稳定性.
- 废弃研究证实了XAI和信任模块在系统稳定性和决策问责制方面的重要作用.
结论:
- EMARL框架为安全,可解释和可扩展的无人机群通信提供了至关重要的进步.
- 该系统通过明确和负责任的决策过程提高了人类的解释性和可信度.
- 在动态和敌对的操作环境中,EMARL为无人机群通信提供了强大的解决方案.
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