Automated Extraction of Hierarchical Action Sequences Using Large Language Models for Multi-Agent Planning

Abstract

Multi-agent planning, which combines immediacy and deliberateness, has been proposed to enable robots to achieve their goals while adapting to dynamic environments. However, the design of agents must be done manually, making efficiency and scale a challenge. In this study, we propose a method to automatically generate agents by extracting knowledge of action sequences from Large Language Models. The proposed method extracts hierarchical action sequences by generating and decomposing abstract tasks using Large Language Models. By generating agents based on the smallest unit action, the terminal action, we construct a multi-agent behavior network. Experimental results show that it is possible to automatically extract hierarchical action sequences and construct an agent action network. The analysis of the terminal actions revealed that most of the actions can be expressed by a small number of verbs.