Understanding the planning of LLM agents: A survey

• URL: http://arxiv.org/abs/2402.02716v1
• Date: Mon, 5 Feb 2024 04:25:24 GMT
• Title: Understanding the planning of LLM agents: A survey
• Authors: Xu Huang and Weiwen Liu and Xiaolong Chen and Xingmei Wang and Hao Wang and Defu Lian and Yasheng Wang and Ruiming Tang and Enhong Chen
• Abstract summary: This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. Comprehensive analyses are conducted for each direction, and further challenges in the field of research are discussed.
• Score: 98.82513390811148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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