KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
Yuqi Zhu Shuofei Qiao Yixin Ou Shumin Deng Ningyu Zhang Shiwei Lyu Yue Shen Lei Liang Jinjie Gu Huajun Chen
Large Language Models (LLMs) have demonstrated great potential in complex
reasoning tasks, yet they fall short when tackling more sophisticated
challenges, especially when interacting with environments through generating
executable actions. This inadequacy primarily stems from the lack of built-in
action knowledge in language agents, which fails to effectively guide the
planning trajectories during task solving and results in planning
hallucination. To address this issue, we introduce KnowAgent, a novel approach
designed to enhance the planning capabilities of LLMs by incorporating explicit
action knowledge. Specifically, KnowAgent employs an action knowledge base and
a knowledgeable self-learning strategy to constrain the action path during
planning, enabling more reasonable trajectory synthesis, and thereby enhancing
the planning performance of language agents. Experimental results on HotpotQA
and ALFWorld based on various backbone models demonstrate that KnowAgent can
achieve comparable or superior performance to existing baselines. Further
analysis indicates the effectiveness of KnowAgent in terms of planning
hallucinations mitigation. Code is available in
https://github.com/zjunlp/KnowAgent.