@inproceedings{chai-etal-2025-causalmace,
title = "{C}ausal{MACE}: Causality Empowered Multi-Agents in {M}inecraft Cooperative Tasks",
author = "Chai, Qi and
Zheng, Zhang and
Ren, Junlong and
Ye, Deheng and
Lin, Zichuan and
Wang, Hao",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.777/",
pages = "14410--14426",
ISBN = "979-8-89176-335-7",
abstract = "Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy sequences of actions, single-agent approaches often face challenges related to inefficiency and limited fault tolerance. Despite these issues, research on multi-agent collaboration remains scarce. In this paper, we propose CausalMACE, a holistic causality planning framework designed to enhance multi-agent systems, in which we incorporate causality to manage dependencies among subtasks. Technically, our proposed framework introduces two modules: an overarching task graph for global task planning and a causality-based module for dependency management, where inherent rules are adopted to perform causal intervention. Experimental results demonstrate our approach achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft. The code will be open-sourced upon the acceptance of this paper."
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%0 Conference Proceedings
%T CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks
%A Chai, Qi
%A Zheng, Zhang
%A Ren, Junlong
%A Ye, Deheng
%A Lin, Zichuan
%A Wang, Hao
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F chai-etal-2025-causalmace
%X Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy sequences of actions, single-agent approaches often face challenges related to inefficiency and limited fault tolerance. Despite these issues, research on multi-agent collaboration remains scarce. In this paper, we propose CausalMACE, a holistic causality planning framework designed to enhance multi-agent systems, in which we incorporate causality to manage dependencies among subtasks. Technically, our proposed framework introduces two modules: an overarching task graph for global task planning and a causality-based module for dependency management, where inherent rules are adopted to perform causal intervention. Experimental results demonstrate our approach achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft. The code will be open-sourced upon the acceptance of this paper.
%U https://aclanthology.org/2025.findings-emnlp.777/
%P 14410-14426
Markdown (Informal)
[CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks](https://aclanthology.org/2025.findings-emnlp.777/) (Chai et al., Findings 2025)
ACL