首个开放世界多智能体评估竞赛，测试智能体在动态环境中的适应能力。
Inaugural MOASEI Competition at AAMAS'2025: A Technical Report
- 基于自由游园环境，构建动态可变的开放世界测试场景。
- 11支队伍参与，3个赛道验证不同开放性与协作复杂度。
- 结果揭示了智能体在变化环境中自适应的新策略，适合研究开放系统者参考。
我们介绍方法论开放智能体系统评估倡议（MOASEI）竞赛，这是一个多智能体AI基准评测活动，旨在评估开放世界条件下的决策能力。该竞赛基于free-range-zoo环境套件，引入动态、部分可观测的领域，支持实体的出现、消失或行为改变。2025年竞赛包含三个赛道：野火、拼车和网络安全，分别突出不同的开放性维度与协作复杂度。来自国际机构的11支团队参与，其中4支提交了多样化解决方案，包括图神经网络、卷积架构、预测建模以及大语言模型驱动的元优化。评估指标聚焦于期望效用、对扰动的鲁棒性以及对环境变化的响应速度。结果揭示了开放环境中泛化与自适应的有前景策略，为未来研究提供了实证依据与基础设施支持。本报告详细阐述了竞赛设计、发现及其对开放智能体系统研究社区的贡献。
原文摘要 · Abstract (English)
We present the Methods for Open Agent Systems Evaluation Initiative (MOASEI) Competition, a multi-agent AI benchmarking event designed to evaluate decision-making under open-world conditions. Built on the free-range-zoo environment suite, MOASEI introduced dynamic, partially observable domains with agent and task openness--settings where entities may appear, disappear, or change behavior over time. The 2025 competition featured three tracks--Wildfire, Rideshare, and Cybersecurity--each highlighting distinct dimensions of openness and coordination complexity. Eleven teams from international institutions participated, with four of those teams submitting diverse solutions including graph neural networks, convolutional architectures, predictive modeling, and large language model--driven meta--optimization. Evaluation metrics centered on expected utility, robustness to perturbations, and responsiveness to environmental change. The results reveal promising strategies for generalization and adaptation in open environments, offering both empirical insight and infrastructure for future research. This report details the competition's design, findings, and contributions to the open-agent systems research community.
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