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This paper introduces the "GPT-in-the-loop" approach, a novel methodcombining 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 longtraining processes, our framework employs GPT-4 for enhanced problem-solvingand explanation skills. Our experimental backdrop is the smart streetlightInternet of Things (IoT) application. Here, agents use sensors, actuators, andneural networks to create an energy-efficient lighting system. By integratingGPT-4, these agents achieve superior decision-making and adaptability withoutthe need for extensive training. We compare this approach with both traditionalneuroevolutionary 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-drivenFramework for the Internet of Things (FIoT), introduces our proposedGPT-in-the-loop approach, presents comparative results in the IoT context, andconcludes with insights and future directions.

GPT-in-the-Loop: Adaptive Decision-Making for Multiagent Systems