Effective planning is essential for the success of any task, from organizing a vacation to routing autonomous vehicles and developing corporate strategies. It involves setting goals, formulating plans, and allocating resources to achieve them. LLMs are particularly well-suited for automated planning due to their strong capabilities in commonsense reasoning. They can deduce a sequence of actions needed to achieve a goal from a given state and identify an effective course of action. However, it is frequently observed that plans generated through direct prompting often fail upon execution. Our survey aims to highlight the existing challenges in planning with language models, focusing on key areas such as embodied environments, optimal scheduling, competitive and cooperative games, task decomposition, reasoning, and planning. Through this study, we explore how LLMs transform AI planning and provide unique insights into the future of LM-assisted planning.

本研究探讨了大型语言模型在自动化规划中的应用挑战，尤其是计划执行失败的问题。通过审视现有的关键领域，如任务分解和推理，研究提供了对大型语言模型如何变革人工智能规划的独特见解，并展望了未来的发展方向。

LASP：大型语言模型辅助人工智能规划的最先进调查