大语言模型视角下的智能规划方法综述

doi:10.16182/j.issn1004731x.joss.23-1468

摘要/Abstract

摘要：

从大语言模型的视角入手，对智能规划的定义和发展进行概述，简要介绍了传统智能规划的方法；基于大语言智能体与智能规划的紧密关系，介绍了大语言模型的架构和典型的大模型智能体；围绕大模型的智能规划，梳理了规划语言学习、思维链推理、反馈优化和流程自动化共4类规划方法；结合当前的挑战与困难，介绍大模型进行智能规划的前沿研究展望。

关键词: 智能规划, 大语言模型, 生成式智能, 思维链

Abstract:

. Starting from the perspective of large language models, this paper gives an overview of the definition and development of intelligent planning, and briefly introduces the traditional methods of intelligent planning; based on the close relationship between large language model intelligent agents and intelligent planning, introduces the architecture of large language models and typical large model intelligent agents; focusing on the intelligent planning for large language models, combs through the learning of planning languages, chain of thought, feedback optimization, and process automation; combining with the current challenges and difficulties, introduces the outlook of cutting-edge research on intelligent planning with large models.

Key words: intelligent planning, large language models, generative intelligence, chain of thought

中图分类号:

TP391

周棪忠,罗俊仁,谷学强等 . 大语言模型视角下的智能规划方法综述[J]. 系统仿真学报, 2025, 37(4): 823-844.

Zhou Yanzhong,Luo Junren,Gu Xueqiang,et al . Survey on Intelligent Planning Methods from Large Language Models Perspective[J]. Journal of System Simulation, 2025, 37(4): 823-844.

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表1

基于大语言模型的规划方法

方法	名称	特点
规划语言学习	LLM-P^[50]	将经典规划器的优势融入到LLM框架中，实现用户用自然语言进行任务规划领域建模
	LLM-DP^[51]	利用LLM将观测值、当前世界状态和目标状态转换为PDDL，使其能够完整且准确地表示规划问题
	CO-LLM^[52]	结合外部低级规划器来有效地执行基于高层次规划的操作
	LLM-Planner^[53]	当在任务完成过程中遇到对象不匹配和无法实现的规划结果时，该算法会动态更新LLM生成的规划结果
思维推理	CoT^[54]	模仿人类思考的过程，给出逐步解决问题的依据，将一个多步问题分解为多个可被单独解答的中间步骤
	零样本CoT^[55]	不需要构建思维链的演示样例，减少了人工成本
	CoT-SC^[56]	进行分步骤思考的过程中采样生成不同的思维链，获取多个答案结果，通过投票机制选取最终结果
	ToT^[57]	分解问题进行规划的过程中使用树状结构生成规划的步骤
	RecMind^[58]	利用自我启发机制，将在思维树的规划过程中被丢弃的一些历史步骤利用起来
	GoT^[59]	将思维树中的树状分解结构扩展为图结构
	AoT^[60]	将算法示例合并到提示中
	DAG推理^[61]	通过有向无环图对思维过程进行更一般性的建模
	XoT^[62]	可以探索不同的思维结构，如链、树、图等
	SoT^[63]	生成答案的骨架，进行并行工具调用或分批解码，并行完成每个骨架点的内容
	PoT^[64]	通过大语言模型不仅生成推理问题的答案，而且生成能够反映推理逻辑的程序代码
	JUDEC^[65]	采用基于Elo的评分机制给思维步骤进行打分判定
反馈优化	ReAct^[66]	使用思维-行动-观察三元组构建提示，思想组件旨在促进高级规划和规划，从而指导行为
	Voyager^[45]	结合了环境反馈、执行错误，以及自我验证3种反馈
	Ghost^[67]	将环境状态以及每个执行动作的成功或者失败信息作为反馈
	SayPlan^[68]	利用来自场景图模拟器的环境反馈来验证和完善其规划结果
	DEPS^[69]	告知任务失败的详细原因，有助于在长期规划过程中更好地从反馈中纠正错误
	Inner Monologue^[70]	主动征求用户关于外部场景描述的反馈意见，将用户的反馈意见作为提示输入
	Self-Refine^[71]	输出结果，然后收到反馈进行迭代优化，直到达到用户满意的规划结果
	Self-Check^[72]	对智能体在各个阶段生成的规划结果进行检查和评估，纠正在规划过程中的错误
	InterAct^[73]	使用不同的语言模型对规划的结果进行检查和排序，帮助主要语言模型避免错误和低效的操作
	Reflexion^[74]	提出了一种不通过更新权重，而是通过语言反馈来增强大模型的模型框架
流程自动化	LangChain^[75][76]	大语言模型与外部计算和数据源相结合，通过组件的方式构建遵循一般流程的语言模型应用程序
	HuggingGPT^[77]	让大语言模型充当一个控制器来管理现有的AI模型，解决复杂的AI任务
	低代码LLM^[78]	通过6种简单的低代码可视化编程交互，包括支持点击、拖动或文本编辑等，用户可以通过交互操作将自己的想法融入规划流程
	MetaGPT^[79]	将SOP编码为提示，让智能体生成规范化的设计文档、接口等，明确角色责任、提高协作质量
	自动化智能体框架^[80]	把智能体的状态抽象为状态类
	Prompt Flow^[81]	简化基于LLM的应用程序的端到端开发流程，使提示工程变得更加容易，使用户能够构建具有更高质量的LLM应用程序
	RAP-LLM^[82][83]	大模型通过自然语言处理技术将用户需求转换为RPA领域的表述，同时生成自动化流程，完成之后，RPA执行既定方案，完成业务流程自动化闭环
	ProAgent^[84]	结合大模型智能体帮助人类进行工作流构建，并让智能体自主处理工作流中涉及复杂决策与动态处理的环节

表1

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