[2402.02716] Understanding the planning of LLM agents: A survey

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• [2402.02716] Understanding the planning of LLM agents: A survey

Understanding the planning of LLM agents: A survey

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"Computer Science > Artificial Intelligence
[Submitted on 5 Feb 2024]
Understanding the planning of LLM agents: A survey

Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Hao Wang, Defu Lian, Yasheng Wang, Ruiming Tang, Enhong Chen
As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. We provide a taxonomy of existing works on LLM-Agent planning, which can be categorized into Task Decomposition, Plan Selection, External Module, Reflection and Memory. Comprehensive analyses are conducted for each direction, and further challenges for the field of research are discussed.
Comments: 9 pages, 2 tables, 2 figures
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2402.02716 [cs.AI]
(or arXiv:2402.02716v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2402.02716
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