Autonomous Agent-Based Intelligence for Continuous Learning and Adaptation in Cyberphysical Systems

Abstract

The Cyber-Physical Systems (CPS) experience great operational complexity in the stochastic and non-stationary contexts when the control logic can be considered static. The current paper introduces an autonomous agent-based intelligence framework, which can be used in perpetually adaptive and decentralized organization of heterogeneous CPS systems. The given framework implements a multi-agent system (MAS) to bridge the gap between the cyber and physical layers with the help of built-in perception-action loops and planning modules. With the help of online reinforcement learning (RL) and predictive analytics, individual agents change control policies in real-time to alleviate disturbances in a system and changing operational constraints. A shared knowledge layer that is distributed is put in place to coordinate inter-agent coordination and policy refinement making the system-wide scalable and fault-tolerant. Experimental validation in a wide range of CPS situations proves that the suggested autonomous structure can be much more efficient and self-optimizing than centralized baseline frameworks, providing a stable channel of self-evolutionary industrial and robotic systems.