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.