@inproceedings{yanfangzhou-etal-2025-m2pa,
    title = "{M}2{PA}: A Multi-Memory Planning Agent for Open Worlds Inspired by Cognitive Theory",
    author = "YanfangZhou, YanfangZhou  and
      Li, Xiaodong  and
      Liu, Yuntao  and
      Zhao, Yongqiang  and
      Wang, Xintong  and
      Li, Zhenyu  and
      Tian, Jinlong  and
      Xu, Xinhai",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.1191/",
    pages = "23204--23220",
    ISBN = "979-8-89176-256-5",
    abstract = "Open-world planning poses a significant challenge for general artificial intelligence due to environmental complexity and task diversity, especially in long-term tasks and lifelong learning. Inspired by cognitive theories, we propose M2PA, an open-world multi-memory planning agent. M2PA innovates by combining Large Language Models (LLMs) with human-like multi-memory systems, aiming to fully leverage the strengths of both while mitigating their respective limitations. By integrating the expansive world knowledge and language processing capabilities of LLMs with the perception and experience accumulation abilities of the human memory system, M2PA exhibits situation awareness, and experience generalization capabilities, as well as the potential for lifelong learning. In experiments, M2PA significantly outperforms current state-of-the-art agents across 50 Minecraft tasks in zero-shot learning. In exploratory lifelong learning experiments, M2PA demonstrates its continuous learning ability, achieving a \textbf{38.33{\%}} success rate in the ``ObtainDiamond'' task. Our findings provide a novel paradigm for constructing more effective agents in open-world environments."
}