LLM在自动规划中能辅助但难独立胜任，需结合传统方法提升效率。
A Survey on Large Language Models for Automated Planning
- 结合传统规划方法与LLM的通用知识，实现灵活协同
- 长周期推理任务中表现受限，无法独立担当规划主体
- 适合希望融合灵活性与可靠性的规划系统研究者
近年来，大语言模型（LLMs）在多步推理和跨领域泛化方面展现出强大能力，使其在自动规划中的应用日益受到关注。尽管部分研究强调其执行复杂规划任务的潜力，但也有研究指出其在处理长周期推理时存在显著性能瓶颈。本文系统梳理了现有关于LLMs在自动规划中的研究，深入分析其优势与不足。研究表明，由于上述局限性，LLMs尚不适合作为独立规划器；然而，若与传统规划方法结合，仍可极大提升规划应用的效能。因此，本文主张采用平衡策略，充分发挥LLMs的灵活性与通用知识优势，同时融入传统方法的严谨性与高性价比。
原文摘要 · Abstract (English)
The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some researchers emphasize the potential of LLMs to perform complex planning tasks, others highlight significant limitations in their performance, particularly when these models are tasked with handling the intricacies of long-horizon reasoning. In this survey, we critically investigate existing research on the use of LLMs in automated planning, examining both their successes and shortcomings in detail. We illustrate that although LLMs are not well-suited to serve as standalone planners because of these limitations, they nonetheless present an enormous opportunity to enhance planning applications when combined with other approaches. Thus, we advocate for a balanced methodology that leverages the inherent flexibility and generalized knowledge of LLMs alongside the rigor and cost-effectiveness of traditional planning methods.
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