Dear Colleague(s),
The OptLearnMAS workshop at AAMAS 2025 is accepting submissions!
The goal of the workshop is to provide researchers with a venue to discuss
models or techniques for tackling a variety of multi-agent optimization
problems. We seek contributions in the general area of multi-agent
optimization, including distributed optimization, coalition formation,
optimization under uncertainty, winner determination algorithms in auctions and
procurements, and algorithms to compute Nash and other equilibria in games. Of
particular emphasis are contributions at the intersection of optimization and
learning. See below for a (non-exhaustive) list of topics.
This workshop invites works from different strands of the multi-agent systems
community that pertain to the design of algorithms, models, and techniques to
deal with multi-agent optimization and learning problems or problems that can
be effectively solved by adopting a multi-agent framework.
Topics
The workshop organizers invite paper submissions on the following (and related)
topics:
-
Optimization for learning (strategic and non-strategic) agents
-
Learning for multi-agent optimization problems
-
Distributed constraint satisfaction and optimization
-
Winner determination algorithms in auctions and procurements
-
Coalition or group formation algorithms
-
Algorithms to compute Nash and other equilibria in games
-
Optimization under uncertainty
-
Optimization with incomplete or dynamic input data
-
Algorithms for real-time applications
-
Cloud, distributed, and grid computing
-
Applications of learning and optimization in societally beneficial domains
-
Multi-agent planning
-
Multi-robot coordination
The workshop is of interest both to researchers investigating applications of
multi-agent systems to optimization problems in large, complex domains, as well
as to those examining optimization and learning problems that arise in systems
comprised of many autonomous agents. In so doing, this workshop aims to provide
a forum for researchers to discuss common issues that arise in solving
optimization and learning problems in different areas, to introduce new
application domains for multi-agent optimization techniques, and to elaborate
common benchmarks to test solutions.
Finally, the workshop will welcome papers that describe the release of
benchmarks and data sets that can be used by the community to solve fundamental
problems of interest, including machine learning and optimization for health
systems and urban networks, to mention but a few examples.
Visit the website:
https://optlearnmas.github.io
Important Dates
-
Mar 2, 2025 (23:59 AoE) – Submission deadline (tentative)
-
Mar 30, 2025 (23:59 AoE) – Acceptance notification (tentative)
-
May 19-20, 2025 – Workshop date
Cheers,
Filippo Bistaffa, Hau Chan, Sarah Keren, Xinrun Wang, Roger X. Lera-Leri
OptLearnMAS Co-Chairs
_______________________________________________
hol-info mailing list
hol-info@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/hol-info
