将大语言模型（LLM）融入自动规划与调度（APS）的前景
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
AI总结：
本文综述126篇论文，将LLM在自动规划与调度中的应用归纳为八类，指出其与传统符号规划器结合的神经符号方向最具潜力。
摘要：
自动规划与调度是人工智能（AI）中不断发展且提及LLM日益流行的领域之一。基于对126篇论文的全面综述，本文根据LLM在解决规划问题各方面时的独特应用，考察了八个类别：语言翻译、规划生成、模型构建、多智能体规划、交互式规划、启发式优化、工具集成以及类脑规划。对于每个类别，我们阐明了已被考虑的问题和现存差距。综述得出的一个关键洞见是：当LLM与传统符号规划器集成时，其真正潜力才会展现，这指向一种有前景的神经符号方法。该方法有效结合了LLM的生成能力与经典规划方法的精确性。通过综合现有文献中的洞见，我们强调这种集成在应对复杂规划挑战方面的潜力。我们的目标是鼓励ICAPS社区认识到LLM与符号规划器的互补优势，倡导自动规划领域利用这些协同能力，开发更先进、更智能的规划系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
