GPT-in-the-Loop: Adaptive Decision-Making for Multiagent Systems
This addresses the need for efficient and adaptable multiagent systems in IoT domains, offering a novel integration that could reduce training overhead, though it appears incremental as it builds on existing LLM and MAS technologies.
The paper tackles the problem of adaptive decision-making in multiagent systems for IoT applications like smart streetlights, by introducing a GPT-in-the-loop approach that uses GPT-4 to enhance reasoning without extensive training, achieving superior performance compared to traditional methods and human-engineered solutions.
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.