[fg-arc] CFP [OptLearnMAS 2021] The 12th International Workshop on Optimization and Learning in Multi-Agent Systems

[Gauthier Picard]

[Apologies for cross-posting. Please share with anyone may be interested]

The 12th Workshop on Optimization and Learning in Multiagent Systems 
(OptLearnMAS'21)

To be held in conjunction with the International Joint Conference on 
Autonomous Agents and Multiagent Systems (AAMAS), Online, from May 3-7, 
2021.

=======================================================

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.

OptMAS 2021 website: https://optlearnmas21.github.io/

Workshop submission site: 
https://easychair.org/conferences/?conf=optlearnmas21


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



Background
----------

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.


Keywords
--------

Topics include but are not limited to the theory and applications of:

     * 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
     * Cloud, distributed and grid computing
     * Learning and Optimization in Societally Beneficial Domains


Submission Information
----------------------

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

Submission Types:

     * Technical Papers: Full-length research papers of up to 7 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 directly to OptLearnMAS along with previous reviews. 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.


Reviewing process
-----------------

Papers will be reviewed by at least 2 program committee members. 
Criteria for selection of papers will include technical quality, 
novelty, significance, and clarity.


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.


Organizing committee
--------------------

     * Ferdinando Fioretto - Syracuse University, NY, USA
     * Gauthier Picard - ONERA, Toulouse, France
     * Amulya Yadav - Penn State University, PA, USA
     * Bryan Wilder - Harvard University, MA, USA


Programme Committee
-------------------

     * Ana L. C. Bazzan - Universidade Federal do Rio Grande do Sul
     * Filippo Bistaffa - IIIA-CSIC
     * Alessandro Farinelli - Computer Science Department, Verona University
     * Tal Grinshpoun - Ariel University
     * Md. Mosaddek Khan - University of Dhaka
     * Rene Mandiau - LAMIH, Université de Valenciennes
     * Zinovi Rabinovich - Nanyang Technological University
     * Juan Antonio Rodriguez Aguilar - IIIA-CSIC
     * Marius Silaghi - FIT
     * William Yeoh - Washington University in St. Louis
     * Makoto Yokoo - Kyushu University
     * Roie Zivan - Ben Gurion University of the Negev
     * Maryam Tabar - Penn State University
     * Hangzhi Guo - Penn State University


-- 
Gauthier Picard, PhD, HDR
Senior Research Fellow
ONERA - DTIS - SYD
BP74025 - 2 avenue Edouard Belin, FR-31055 TOULOUSE CEDEX 4
Tel. +33 (0)5 62 25 26 54

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