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.

翻译：本文提出了一种“GPT-in-the-loop”方法，这是一种将大型语言模型（如生成式预训练Transformer，GPT）的先进推理能力与多智能体系统相结合的新颖方法。有别于通常需要长时间训练过程的传统自适应方法，我们的框架采用GPT-4来增强问题解决与解释能力。我们以智能路灯物联网应用作为实验场景。在此场景中，智能体通过传感器、执行器和神经网络构建节能照明系统。通过集成GPT-4，这些智能体在无需大量训练的情况下实现了更优的决策与自适应能力。我们将该方法与传统的神经进化方法及软件工程师提供的解决方案进行了对比，凸显了GPT驱动的多智能体系统在物联网领域的潜力。在结构上，本文阐述了如何在面向智能体的物联网框架中集成GPT，介绍了所提出的GPT-in-the-loop方法，展示了物联网场景下的对比结果，并在结论中总结了见解与未来方向。