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.

翻译：自动规划与调度是人工智能领域中不断发展的方向之一，其中大语言模型的应用已受到广泛关注。基于对126篇论文的全面综述，本文针对大语言模型在解决规划问题不同方面的独特应用，归纳出八个类别：语言翻译、计划生成、模型构建、多智能体规划、交互式规划、启发式优化、工具集成及脑启发式规划。针对每个类别，我们阐述了所探讨的问题及现有研究空白。本综述的关键洞见在于：当大语言模型与传统符号规划器集成时，其真正潜力得以显现，这指向了一种极具前景的神经符号方法。该方法有效结合了大语言模型的生成能力与经典规划方法的精确性。通过综合现有文献的见解，我们强调了这种集成在应对复杂规划挑战方面的潜力。本文旨在鼓励ICAPS社区认识大语言模型与符号规划器的互补优势，倡导在自动规划领域利用这种协同能力，开发更先进、更智能的规划系统。