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

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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.