I am excited to announce a workshop on LLM-Based Multi-Agent Collaboration at AAAI 2025 (Philadelphia, February 25 - March 4, 2025). As research continues to show the immense potential of multi-agent systems powered by large language models (LLMs), we’re organizing this event to foster discussion and explore cutting-edge challenges and opportunities in this rapidly evolving field. I’m reaching out to see if you or any of your students would be interested in submitting a paper to this workshop.
Workshop Highlights
This full-day event will delve into critical topics like:
Architectures for multi-agent collaboration, decision-making, and hierarchy
Cross-agent knowledge sharing & communication protocols
Distributed, decentralized agents & group behavior learning
Strategic planning in multi-agent systems
Guardrails and ethical considerations for multi-agent systems
Key Dates
Submission Deadline: November 24, 2024
Acceptance Notification: December 9, 2024
Workshop Date: March 4th
Submission Guidelines
We invite short papers (up to 4 pages) and long papers (up to 8 pages) in AAAI format. Submissions can include published work, papers under review, work in progress, and position papers. All papers will undergo single-blind peer review, and accepted papers will be featured on our website with the authors’ permission. For more information and to submit your work, please visit our workshop website: https://multiagents.org/workshop.
We would be thrilled to see your submissions and welcome you to join us for engaging discussions at AAAI 2025!
Organizing Committee
Alborz Geramifard https://sites.google.com/view/alborz-geramifard/home (Meta)
Alex Marin https://www.linkedin.com/in/alex-marin-210802b7/ (Microsoft)
Raphael Shu https://www.amazon.science/author/raphael-shu (Amazon)
Tao Yu https://taoyds.github.io/ (The University of Hong Kong)
Weiyan Shi https://wyshi.github.io/ (Northeastern University)
Yi Zhang https://www.amazon.science/author/yi-zhang (Amazon)
Best,
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Alborz Geramifard https://sites.google.com/view/alborz-geramifard/home