Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting from our review is that the true potential of LLMs unfolds when they are integrated with traditional symbolic planners, pointing towards a promising neuro-symbolic approach. This approach effectively combines the generative aspects of LLMs with the precision of classical planning methods. By synthesizing insights from existing literature, we underline the potential of this integration to address complex planning challenges. Our goal is to encourage the ICAPS community to recognize the complementary strengths of LLMs and symbolic planners, advocating for a direction in automated planning that leverages these synergistic capabilities to develop more advanced and intelligent planning systems.

自主规划和调度是人工智能中增长最快的领域之一，本研究通过对126篇论文进行全面检视，调查了LLMs在地址规划问题的各个方面时的独特应用，并提出了八个类别：语言翻译、计划生成、模型构建、多智能体规划、交互规划、启发式优化、工具集成以及脑启发式规划，强调LLMs与传统符号规划器的结合展示出潜力，揭示出一种有前景的神经符号化方法。

关于将大型语言模型（LLMs）纳入自动规划和调度（APS）的前景展望