GPT-in-the-Loop: Supporting Adaptation in Multiagent Systems

This paper introduces the 'GPT-in-the-loop' approach, which seeks to investigate the reasoning capabilities of Large Language Models (LLMs) like Generative Pre-trained Transformers (GPT) within multiagent systems (MAS). Moving beyond traditional adaptive approaches that generally require long training processes, our framework employs GPT-4 to enhance problem-solving and explanation skills. To explore this approach, we apply it to a smart streetlight application in the Internet of Things (IoT) context, wherein each streetlight is controlled by an autonomous agent equipped with sensors and actuators, tasked with creating an energy-efficient lighting system. With the integration of GPT-4, these agents have shown enhanced decision-making and adaptability, without necessitating prolonged training. We compare this approach with both conventional neuroevolutionary methods and manually crafted solutions by software engineers, underscoring the potential of GPT-driven behavior in multiagent systems. It is important to note that these comparisons are preliminary, and further, more extensive testing is critical to determine the approach's applicability across a wider range of MAS scenarios. Structurally, the paper delineates the incorporation of GPT into the agent-driven Framework for the Internet of Things (FIoT), details our proposed GPT-in-the-loop approach, presents comparative results within the IoT setting, and concludes with insights and prospective future directions.