5 papers
PLATO: Pointer Learner for Agent and Task Openness
Alireza Saleh Abadi, Leen-Kiat Soh, Daniel Alan Redder +2
Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time. Such openness, including agent openne…
Second MOASEI Competition at AAMAS'2026: A Technical Report
Ceferino Patino, Tyler J. Billings, Alireza Saleh Abadi +4
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 condi…
EVENT5Ws: A Large Dataset for Open-Domain Event Extraction from Documents
Praval Sharma, Ashok Samal, Leen-Kiat Soh +1
Event extraction identifies the central aspects of events from text. It supports event understanding and analysis, which is crucial for tasks such as informed decision-making in em…
Challenges in Credit Assignment for Multi-Agent Reinforcement Learning in Open Agent Systems
Alireza Saleh Abadi, Leen-Kiat Soh
In the rapidly evolving field of multi-agent reinforcement learning (MARL), understanding the dynamics of open systems is crucial. Openness in MARL refers to the dynam-ic nature of…
Inaugural MOASEI Competition at AAMAS'2025: A Technical Report
Ceferino Patino, Tyler J. Billings, Alireza Saleh Abadi +4
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…