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

Jianhong Wang, Caroline Wang, Feng Chen, Arrasy Rahman, Felipe Leno da Silva, Rupali Bhati, Bo Liu, Mustafa Mert Çelikok

Keywords: Multi-Agent Learning, Generative AI, Multi-Agent Systems

TL;DR: The workshop explores how generative AI and multi-agent learning together enable the next-generation multi-agent generative AI systems.

Abstract: 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.

Submission Number: 28