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.

翻译：自动规划关注于开发高效算法，以在给定环境中生成实现特定目标的计划或行动序列。新兴的大型语言模型（LLMs）能够回答问题、编写高质量编程代码以及预测蛋白质折叠，展示了其在解决语言类问题之外多种任务中的通用性。本文旨在探索LLMs如何也能用于自动规划。为此，我们试图回答四个关键问题。首先，我们想了解LLMs在多大程度上可用于计划生成。其次，我们旨在识别哪些预训练数据最有效地促进计划生成。第三，我们研究微调或提示工程在计划生成中哪种方法更有效。最后，我们探索LLMs是否具备计划泛化能力。通过回答这些问题，本研究力图阐明LLMs在解决复杂规划问题中的能力，并为在此背景下使用LLMs的最有效方法提供见解。