基于大语言模型的任务规划智能体框架、技术与挑战

doi:10.11896/jsjkx.260100066

Abstract

Abstract: As a core research domain in artificial intelligence,task planning faces challenges with traditional methods struggling to address practical demands involving long-term planning,complex logical relationships,and dynamic task adjustments.Recent breakthroughs in large language models(LLM) have demonstrated remarkable contextual reasoning and human-like cognitive capabilities,offering novel solutions to these challenges.However,direct application of LLM to task planning still encounters bottlenecks including long-term memory deficits,limited task decomposition capacity,and delayed responses to dynamic changes.To tackle these issues,this paper proposes an intelligent agent framework for task planning based on large language models.Building upon the AI Agent architecture,the framework centers on LLM and achieves efficient dynamic task planning through the organic integration of modules such as abstract reasoning,action-driven mechanisms,reflection,and memory systems.This study thoroughly examines the current state of key technologies involved,compares the strengths and limitations of different approaches,and evaluates technical trade-offs.It also analyzes how the framework selects appropriate technologies under varying conditions of complexity,resource consumption,and real-time requirements.By summarizing existing research challenges and difficulties,the paper outlines future research directions,providing a theoretical framework and methodological guidance for task planning based on large language models.

Key words: Task planning, Large language model, Fine-tuning, Abstract reasoning, Action-driven, Reflection, Memory

CLC Number:

TP391

AN Ran, CHENG Kai, SHAO Tianhao, LI Changyuan, CHEN Yan. Framework,Techniques and Challenges of Task Planning Agent Based on Large Language Model[J].Computer Science, 2026, 53(9): 24-37.

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