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.

通过融合 Generative Pre-trained Transformers (GPT) 这类大型语言模型的高级推理能力和 Multiagent (MAS) 系统，这篇论文介绍了一种新的方法，“GPT-in-the-loop”，用于增强问题解决和解释能力。结合智能街灯物联网应用背景，该方法使代理人通过传感器、执行器和神经网络创建出一个能耗更低的照明系统，并与传统神经进化方法和软件工程师提供的解决方案进行了比较，凸显了 GPT 驱动的 Multiagent 系统在物联网中的潜力。