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.

Scope

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 of Interest

  • 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

Submission Information

Conference submission site: https://easychair.org/conferences/?conf=optmas20
Submissions should conform to the AAMAS-20 template

Submissions should include the name(s), affiliations, and email addresses of all authors. We welcome the submission of papers rejected from the AAMAS 2020 technical program.
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.

Submission Types

  • Long Papers: Full-length research papers detailing work in progress or work that could potentially be published at a major conference. These papers should not exceed *7* pages in AAMAS format (excluding bibliography and appendices).
  • Short Papers: Position or demo papers that describe initial work or an application that has not yet been evaluated on the topics of interest. These papers should not exceed *4* pages in the AAMAS format (excluding bibliography and appendices).

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

Important Dates

  • Submission Deadline: 25 February 2020 (Extended)
  • Notification of acceptance: 10 March 2020
  • Camera-ready (informal proceedings): 24 March 2020
  • Workshop: 9 or 10 May 2020

Technical Program

Location: TBA

Invited Speaker

TBA

Panel Discussion

TBA

Program Committee

Contact

Workshop Co-Chairs:
contact us

Past Editions