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arXiv · 2607.03399

Second MOASEI Competition at AAMAS'2026: A Technical Report

Ceferino Patino·Tyler J. Billings·Alireza Saleh Abadi·Daniel Redder·Adam Eck·Prashant Doshi·Leen-Kiat Soh
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Abstract

We describe the 2026 Methods for Open Agent Systems Evaluation Initiative (MOASEI) Competition, a benchmark event for evaluating multi-agent decision-making under open-system conditions. Building on the inaugural 2025 competition, the 2026 edition retained wildfire fighting, cybersecurity, and ride-sharing domains while adding a bonus wildfire track with frame openness, in which agent equipment states such as suppressant capacities and firefighting range vary over time. The competition also expanded its reporting metrics to emphasize total task completions, mean task-completion time, and mean value of completed tasks. Participation in 2026 was limited: eight teams registered, but only one team submitted a final entry, and that entry targeted the ride-sharing track. The submitted DLC approach used planning and replanning to solve routing problems across agents as passengers appeared. This report summarizes the 2026 competition design, highlights differences from the previous year, and reports ride-sharing evaluation results against baseline policies. DLC is recognized as the 2026 ride-sharing track winner among submitted teams.

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Ceferino Patino, Tyler J. Billings, Alireza Saleh Abadi, Daniel Redder, Adam Eck, Prashant Doshi, Leen-Kiat Soh. 2026-07-03. Second MOASEI Competition at AAMAS'2026: A Technical Report. https://arxiv.org/abs/2607.03399

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