Workshop on Multi-Agent Learning and Its Opportunities in the Era of Generative AI

The rapid emergence of generative AI has revitalized interest in multi-agent learning as a foundation for building systems that can reason, coordinate, and adapt across diverse environments. This workshop seeks to explore the growing convergence between multi-agent learning and generative AI, emphasizing their mutual potential to advance both theoretical understanding and practical capability. We focus on three interrelated fronts where this integration is most visible: (1) LLM-based multi-agent systems, where large language models interact, cooperate, or compete in structured settings; (2) real-world distributed system control, where multi-agent learning offers scalable and data-driven coordination strategies for complex real-world systems such as smart cities; and (3) human-AI interaction, where generative AI enables richer modelling of human preferences, values, and behaviours, supporting more human-aligned multi-agent systems. By bringing together researchers from machine learning, game theory, cognitive science, and human-computer interaction, this workshop aims to bridge methodological insights and emerging applications, fostering a shared agenda for the age of multi-agent generative AI systems.