Resilience through Learning in Multi-Agent Cyber-Physical Systems

Abstract

The paper contributes to the design of secure and resilient supervisory Cyber-Physical Systems (CPS) through learning. The reported approach involves the inclusion of learning modules in each of the supervised agents, and considers a scenario where the system's coordinator privately transmits to individual agents their action plans in the form of symbolic strings. Each agent's plans belong in some particular class of (sub-regular) languages, which is identifiable in the limit from positive data. With knowledge of the class of languages their plans belong to, agents can observe their coordinator's instructions and utilize appropriate Grammatical Inference modules to identify the behavior specified for them. From that, they can then work collectively to infer the executive function of their supervisor. The paper proves that in cases where the coordinator fails, or communication to subordinates is disrupted, agents are able not only to maintain functional capacity but also to recover normalcy of operation by reconstructing their coordinator. Guaranteeing normalcy recovery in supervisory CPSs is critical in cases of a catastrophic failure or malicious attack, and is important for the design of next-generation Cyber-Physical Systems.