用大模型把模糊任务转为可执行的规划，让自动规划更靠谱。
LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models
- 用大模型将自然语言需求转化为结构化规划形式
- 提升现有规划工具对复杂长周期任务的处理能力
- 适合做智能助手、自动化系统设计的研究者
大语言模型在自然语言任务中表现优异，但在需要结构化推理的长期规划问题上表现不佳。这一局限促使自动规划（AP）与自然语言处理（NLP）领域探索神经符号结合方法。然而，如何选择最优的部署框架仍具挑战性。本文系统综述当前研究，深入分析方法路径，将大模型定位为形式化和优化规划规范的工具，以支持可靠即用的自动规划求解器。通过梳理研究现状，本文揭示关键方法、挑战与未来方向，旨在推动NLP与自动规划领域的协同创新。
原文摘要 · Abstract (English)
Large Language Models (LLMs) excel in various natural language tasks but often struggle with long-horizon planning problems requiring structured reasoning. This limitation has drawn interest in integrating neuro-symbolic approaches within the Automated Planning (AP) and Natural Language Processing (NLP) communities. However, identifying optimal AP deployment frameworks can be daunting and introduces new challenges. This paper aims to provide a timely survey of the current research with an in-depth analysis, positioning LLMs as tools for formalizing and refining planning specifications to support reliable off-the-shelf AP planners. By systematically reviewing the current state of research, we highlight methodologies, and identify critical challenges and future directions, hoping to contribute to the joint research on NLP and Automated Planning.
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