@inproceedings{dong-etal-2024-villageragent,
    title = "{V}illager{A}gent: A Graph-Based Multi-Agent Framework for Coordinating Complex Task Dependencies in {M}inecraft",
    author = "Dong, Yubo  and
      Zhu, Xukun  and
      Pan, Zhengzhe  and
      Zhu, Linchao  and
      Yang, Yi",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-acl.964/",
    doi = "10.18653/v1/2024.findings-acl.964",
    pages = "16290--16314",
    abstract = "In this paper, we aim to evaluate multi-agent systems against complex dependencies, including spatial, causal, and temporal constraints. First, we construct a new benchmark, named VillagerBench, within the Minecraft environment. VillagerBench comprises diverse tasks crafted to test various aspects of multi-agent collaboration, from workload distribution to dynamic adaptation and synchronized task execution. Second, we introduce a Directed Acyclic Graph Multi-Agent Framework (VillagerAgent) to resolve complex inter-agent dependencies and enhance collaborative efficiency. This solution incorporates a task decomposer that creates a directed acyclic graph (DAG) for structured task management, an agent controller for task distribution, and a state manager for tracking environmental and agent data.Our empirical evaluation on VillagerBench demonstrates that VillagerAgentoutperforms the existing AgentVerse model, reducing hallucinations and improving task decomposition efficacy. The results underscore VillagerAgent`s potential in advancing multi-agent collaboration, offering a scalable and generalizable solution in dynamic environments. Source code is open-source on GitHub."
}