Multi-Agent Reinforcement Learning Framework towards Automotive Resiliency and Survivability of Mission-Critical Networks against Volatile Resource Flow

多智能体强化学习框架，提高汽车弹性和关键任务网络的生存能力，应对不稳定的资源流

• 批准号: 503355275
• 负责人: Professorin Dr.-Ing. Setareh Maghsudi
• 金额: --
• 依托单位: Lehrstuhl für Lernende technische Systeme
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/503355275?language=en
• 关键词: Multi; Agent; Reinforcement; Learning; Framework

The synergy between wireless communication, cyber-physical system design, and artificial intelligence enables the autonomous operation of modern networked systems. For such infrastructures that underpin several critical missions, the vitality of resiliency is evident and unquestionable. Nevertheless, the scarcity of resources, the inevitable implementation of technologies for opportunistic resource acquisition, and security threats, render resiliency challenging to achieve, as they introduce volatility in the essential resource flow. In this proposal, we focus on two scenarios, namely resource sharing and backup resource reservation, to boost the resilience of a mission-critical system of systems against oblivious and non-oblivious adversaries that create a volatile resource flow; As such, uncertainty and information shortage count as the focal points of our research. We maintain a generic framework of resiliency via network adaptivity so that our proposal accommodates a variety of applications. Our solutions lie at the intersection of multiagent online convex optimization with bandit feedback, online hide-and-seek games, and statistical concepts such as change point detection. The proposed methods are amenable to distributed implementation, thus reducing the feedback and signaling overhead significantly. We will provide rigorous theoretical analysis concerning efficiency, scalability, and convergence. Also, we will investigate performance bounds.

无线通信、网络物理系统设计和人工智能之间的协同作用使现代网络系统能够自主运行。对于支撑多个关键任务的此类基础设施，弹性的生命力是显而易见且毋庸置疑的。然而，资源的稀缺性、不可避免的机会性资源获取技术的实施以及安全威胁使得实现弹性具有挑战性，因为它们在基本资源流中引入了波动性。在这个建议中，我们专注于两个场景，即资源共享和备份资源预留，以提高系统的关键任务系统的弹性，对遗忘和非遗忘的对手，创造一个不稳定的资源流;因此，不确定性和信息短缺计数为我们的研究重点。我们通过网络自适应性维护一个通用的弹性框架，以便我们的建议适应各种应用。我们的解决方案位于多智能体在线凸优化与强盗反馈，在线捉迷藏游戏，统计概念，如变点检测的交叉点。所提出的方法适合于分布式实现，从而显著减少了反馈和信令开销。我们将提供有关效率、可扩展性和收敛性的严格理论分析。此外，我们将研究性能边界。