OptLearnMAS-25

Stimulated by various emerging applications involving agents to solve complex problems in real-world domains, such as intelligent sensing systems for the Internet of Things (IoT), automated configurators for critical infrastructure networks, and intelligent resource allocation for social domains (e.g., security games for the deployment of security resources or auctions/procurements for allocating goods and services), agents in these domains commonly leverage different forms of optimization and/or learning to solve complex problems.

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

• 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. This workshop aims to provide a forum for researchers to discuss common issues that arise in solving optimization and learning problems in different areas, introduce new application domains for multi-agent optimization techniques, and 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 in machine learning and optimization for health systems and urban networks, to mention but a few examples.

Format

The workshop will be a one-day meeting. It will include several technical sessions, a poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges in the field of multiagent optimization and learning.

Attendance

Attendance is open to all. At least one author of each accepted submission must be present at the workshop.

• Mar 2, 2025 (23:59 AoE) – Submission deadline (tentative)
• Mar 30, 2025 (23:59 AoE) – Acceptance notification (tentative)
• May 19-20, 2025 – Workshop date

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Submission URL

https://cmt3.research.microsoft.com/OptLearnMAS2025

Submission Types

• Technical Papers: Full-length research papers of up to 8 pages (excluding references and appendices) detailing high-quality work in progress or work that could potentially be published at a major conference.
• Short Papers: Position or short papers of up to 4 pages (excluding references and appendices) that describe the initial work or the release of privacy-preserving benchmarks and datasets on the topics of interest.

All papers must be submitted in PDF format, using the AAMAS author kit. Submissions should include the name(s), affiliations, and email addresses of all authors. Submissions will be refereed based on technical quality, novelty, significance, and clarity. Each submission will be thoroughly reviewed by at least two program committee members.

Best Papers

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