Inaugural MOASEI Competition at AAMAS'2025: A Technical Report
This competition provides empirical insights and infrastructure for the multi-agent AI research community, though it is incremental as it builds on existing benchmarking efforts.
The MOASEI Competition tackled evaluating decision-making in open-world multi-agent systems using dynamic, partially observable domains, with results showing promising strategies for generalization and adaptation across three tracks involving 11 teams and diverse solutions.
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.