  This paper introduces the "GPT-in-the-loop" approach, a novel method
combining the advanced reasoning capabilities of Large Language Models (LLMs)
like Generative Pre-trained Transformers (GPT) with multiagent (MAS) systems.
Venturing beyond traditional adaptive approaches that generally require long
training processes, our framework employs GPT-4 for enhanced problem-solving
and explanation skills. Our experimental backdrop is the smart streetlight
Internet of Things (IoT) application. Here, agents use sensors, actuators, and
neural networks to create an energy-efficient lighting system. By integrating
GPT-4, these agents achieve superior decision-making and adaptability without
the need for extensive training. We compare this approach with both traditional
neuroevolutionary methods and solutions provided by software engineers,
underlining the potential of GPT-driven multiagent systems in IoT.
Structurally, the paper outlines the incorporation of GPT into the agent-driven
Framework for the Internet of Things (FIoT), introduces our proposed
GPT-in-the-loop approach, presents comparative results in the IoT context, and
concludes with insights and future directions.
