大模型如何改变AI规划，突破执行失败瓶颈
LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning
- 分析大模型在任务分解、推理与规划中的应用思路
- 指出直接提示生成的计划常因执行失败而失效
- 适合关注大模型辅助规划未来方向的研究者
有效的规划对任务成功至关重要，涵盖从安排行程到自动驾驶路径规划及企业战略制定。其核心包括目标设定、计划制定与资源分配。大语言模型凭借强大的常识推理能力，在自动化规划中表现出色，能从当前状态推导达成目标所需行动序列并识别有效策略。然而，直接提示生成的计划在实际执行中经常失败。本综述聚焦于大模型辅助规划中的关键挑战，涵盖具身环境、最优调度、博弈场景（竞争与合作）、任务分解、推理与规划等方向，系统探讨大模型如何重塑AI规划，并为未来发展方向提供独特见解。
原文摘要 · Abstract (English)
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.
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