Abstract
Automated planning is concerned with developing efficient algorithms to generate plans or sequences of actions to achieve a specific goal in a given environment. Emerging Large Language Models (LLMs) can answer questions, write high-quality programming code, and predict protein folding, showcasing their versatility in solving various tasks beyond language-based problems. In this paper, we aim to explore how LLMs can also be used for automated planning. To do so, we seek to answer four key questions. Firstly, we want to understand the extent to which LLMs can be used for plan generation. Secondly, we aim to identify which pre-training data is most effective in facilitating plan generation. Thirdly, we investigate whether fine-tuning or prompting is a more effective approach for plan generation. Finally, we explore whether LLMs are capable of plan generalization. By answering these questions, the study seeks to shed light on the capabilities of LLMs in solving complex planning problems and provide insights into the most effective approaches for using LLMs in this context.
Abstract (translated)
自动规划关注开发高效的算法，在给定环境中生成计划或行动序列，以实现特定目标。新兴的大型语言模型(LLMs)可以回答问题、编写高质量的编程代码，并预测蛋白质折叠，展示出它们在解决基于语言的问题之外的多种任务方面的多功能性。在本文中，我们旨在探索LLMs如何也可以用于自动规划。为此，我们寻求回答四个关键问题。首先，我们希望理解LLMs可以用于计划生成的程度。其次，我们旨在确定哪些预处理数据最有利于促进计划生成。第三，我们研究是否微调或Prompting是更有效的计划生成方法。最后，我们探索LLMs是否具备计划泛化的能力。通过回答这些问题，研究旨在阐明LLMs在解决复杂规划问题方面的能力，并提供在此背景下使用LLMs的最 effective方法的启示。
URL
https://arxiv.org/abs/2305.16151
