  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.
