Emergent Behaviour Recognition using Reinforcement Learning-Driven Control in Complex Cybernetic IoT Architectures
Abstract
In massive cybernetic IoT architectures, interconnected devices can exhibit emergent behavior, or unintended behaviors that arise as a result of interacting with other devices, which can create challenges in stability, security and performance. Rule-based or fixed control methods are inadequate when it comes to real-time adaptation; this will lead to inefficiency and vulnerableness to an abnormal situation. In this study, a reinforcement learning-informed control approach to discovering and managing emergent behavior in IoT dynamic systems is proposed. The strategy employs dynamic feedback and active redefinition of policy to detect unnatural working conditions and to optimize the response of the system. Multi-agent reinforcement learning framework is a device-level approach, which provides local discretion and connectivity to a global system. The plan capitalizes on the existence of feedback loops and adjustment of policies to detect abnormalities in the manner in which systems should operate and how to best respond. A multi-agent reinforcement learning model coordinates device-level decision-making supporting local adaptability and global system concordance.