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

TL;DR

This paper presents GPT-in-the-loop, a novel framework integrating GPT-4 into multiagent systems for enhanced decision-making and adaptability in IoT applications, reducing training time and improving performance.

Contribution

It introduces a new GPT-in-the-loop approach that leverages GPT-4 for adaptive multiagent decision-making in IoT, bypassing extensive training required by traditional methods.

Findings

GPT-in-the-loop outperforms neuroevolutionary methods.

Agents achieve better energy efficiency in IoT tasks.

Reduced training time compared to traditional approaches.

Abstract

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.