The rapid advancement of large language models, such as the Generative Pre-trained Transformer (GPT) series, has had significant implications across various disciplines. In this study, we investigate the potential of the state-of-the-art large language model (GPT-4) for planning tasks. We explore its effectiveness in multiple planning subfields, highlighting both its strengths and limitations. Through a comprehensive examination, we identify areas where large language models excel in solving planning problems and reveal the constraints that limit their applicability. Our empirical analysis focuses on GPT-4's performance in planning domain extraction, graph search path planning, and adversarial planning. We then propose a way of fine-tuning a domain-specific large language model to improve its Chain of Thought (CoT) capabilities for the above-mentioned tasks. The results provide valuable insights into the potential applications of large language models in the planning domain and pave the way for future research to overcome their limitations and expand their capabilities.

翻译：诸如生成式预训练Transformer系列等大型语言模型的快速发展，已对多个学科领域产生了深远影响。本研究探讨了先进大型语言模型（GPT-4）在规划任务中的潜力，考察其效用于多个规划子领域，凸显其优势与局限。通过系统性分析，我们识别出大型语言模型在解决规划问题中的擅长区域，并揭示制约其适用性的因素。实证分析聚焦于GPT-4在规划域提取、图搜索路径规划及对抗性规划中的表现，进而提出一种微调领域专用大型语言模型的方法，以增强其在上述任务中的思维链能力。研究结果为大型语言模型在规划领域的应用潜力提供了重要见解，并为后续克服其局限性、扩展其能力边界的研究铺平了道路。