Workshop on Optimization And Learning in Multiagent Systems

Scope and Topics

Stimulated by emerging applications, such as those powered by the Internet of the Things, critical infrastructure network, and security games, intelligent agents commonly leverage different forms optimization and/or learning to solve complex problems. The goal of the workshop is to provide researchers with a venue to discuss 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 algorithms to compute Nash and other equilibria in games. This year, the workshop will have a special focus on contributions at the intersection of optimization and learning. For example, agents which use optimization often employ machine learning to predict unknown parameters appearing in their decision problem. Or, machine learning techniques may be used to improve the efficiency of optimization. While submissions across the spectrum of multi-agent optimization are welcome, contributions at the intersection with learning are especially encouraged.

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. 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.

Topics

The workshop organizers invite paper submissions on the following (and related) topics:

Optimization for learning agents
Learning for multiagent optimization problems
Distributed constraint satisfaction and optimization
Winner determination algorithms in auctions
Coalition 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
GPU for general purpose computations (GPGPU)
Multi-core and many-core computing
Cloud, distributed and grid computing

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 a number of (possibly parallel) technical sessions, a virtual poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges for the field of multiagent optimization and learning and will conclude with a panel discussion.

Attendance

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

Important Dates

March 3, 2021 – Submission Deadline
April 3, 2021 – Acceptance notification
April 30,2021 – AAMAS/IJCAI Fast Track Submission Deadline
May 1, 2021 – AAMAS/IJCAI Fast Track Acceptance Notification
May 3 or 4, 2021 – Workshop Date

Submission Information

Submission URL: https://easychair.org/conferences/?conf=optlearnmas21

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 initial work or the release of privacy-preserving benchmarks and datasets on the topics of interest.

Fast Track (Rejected AAMAS or IJCAI papers)

Rejected AAMAS or IJCAI papers with *average* scores of at least 5.0 may be submitted to OptLearnMAS along with previous reviews and scores and an optional letter indicating how the authors have addressed the reviewers comments.
Please use the submission link above and indicate that the submission is a resubmission from of an AAMAS/IJCAI rejected paper. Also OptLearnMAS submission, reviews and optimal letter need to be compiled into a single pdf file.
These submissions will not undergo the regular review process, but a light one, performed by the chairs, and will be accepted if the previous reviews are judged to meet the workshop standard.

All papers must be submitted in PDF format, using the AAMAS-21 author kit. Submissions should include the name(s), affiliations, and email addresses of all authors.
Submissions will be refereed on the basis of technical quality, novelty, significance, and clarity. Each submission will be thoroughly reviewed by at least two program committee members.
Submissions of papers rejected from the AAMAS 2021 and IJCAI 2021 technical program are welcomed.

For questions about the submission process, contact the workshop chairs.