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<p><font face="monospace">[Apologies for cross-posting. Please share
with anyone may be interested]</font></p>
<p><font face="monospace">The 12th Workshop on Optimization and
Learning in Multiagent Systems (OptLearnMAS'21)<br>
<br>
To be held in conjunction with the International Joint
Conference on Autonomous Agents and Multiagent Systems (AAMAS),
Online, from May 3-7, 2021.<br>
<br>
=======================================================<br>
<br>
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. <br>
<br>
OptLearnMAS 2021 website: <a class="moz-txt-link-freetext"
href="https://optlearnmas21.github.io/" moz-do-not-send="true">https://optlearnmas21.github.io/</a><br>
<br>
Workshop submission site: <a class="moz-txt-link-freetext"
href="https://easychair.org/conferences/?conf=optlearnmas21"
moz-do-not-send="true">https://easychair.org/conferences/?conf=optlearnmas21</a><br>
<br>
<br>
Important dates<br>
---------------<br>
<br>
* March 17, 2021 – Submission Deadline (Extended)<br>
* April 17, 2021 – Acceptance notification (Extended)<br>
* April 30,2021 – AAMAS/IJCAI Fast Track Submission Deadline<br>
* May 1, 2021 – AAMAS/IJCAI Fast Track Acceptance Notification<br>
* May 3 or 4, 2021 – Workshop Date<br>
<br>
<br>
<br>
Background<br>
----------<br>
<br>
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. <br>
<br>
<br>
Keywords<br>
--------<br>
<br>
Topics include but are not limited to the theory and
applications of:<br>
<br>
* Optimization for learning agents<br>
* Learning for multiagent optimization problems<br>
* Distributed constraint satisfaction and optimization<br>
* Winner determination algorithms in auctions<br>
* Coalition formation algorithms<br>
* Algorithms to compute Nash and other equilibria in games<br>
* Optimization under uncertainty<br>
* Optimization with incomplete or dynamic input data<br>
* Algorithms for real-time applications<br>
* Cloud, distributed and grid computing<br>
* Learning and Optimization in Societally Beneficial Domains<br>
<br>
<br>
Submission Information<br>
----------------------<br>
<br>
Submission URL: <a class="moz-txt-link-freetext"
href="https://easychair.org/conferences/?conf=optlearnmas21"
moz-do-not-send="true">https://easychair.org/conferences/?conf=optlearnmas21</a><br>
<br>
Submission Types:<br>
<br>
* 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.<br>
<br>
* 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.<br>
<br>
Fast Track (Rejected AAMAS or IJCAI papers):<br>
<br>
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.<br>
<br>
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.<br>
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.<br>
Submissions of papers rejected from the AAMAS 2021 and IJCAI
2021 technical program are welcomed.<br>
<br>
For questions about the submission process, contact the workshop
chairs. <br>
<br>
<br>
Reviewing process<br>
-----------------<br>
<br>
Papers will be reviewed by at least 2 program committee members.
Criteria for selection of papers will include technical quality,
novelty, significance, and clarity.<br>
<br>
<br>
Format<br>
------<br>
<br>
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.<br>
<br>
<br>
Attendance<br>
----------<br>
<br>
Attendance is open to all. At least one author of each accepted
submission must be present at the workshop. <br>
<br>
<br>
Organizing committee<br>
--------------------<br>
<br>
* Ferdinando Fioretto - Syracuse University, NY, USA<br>
* Gauthier Picard - ONERA, Toulouse, France<br>
* Amulya Yadav - Penn State University, PA, USA<br>
* Bryan Wilder - Harvard University, MA, USA<br>
<br>
<br>
Programme Committee<br>
-------------------</font></p>
<p><font face="monospace"> * Ana L. C. Bazzan - Universidade
Federal do Rio Grande do Sul<br>
* Filippo Bistaffa - IIIA-CSIC<br>
* Alessandro Farinelli - Computer Science Department, Verona
University<br>
* Tal Grinshpoun - Ariel University<br>
* Md. Mosaddek Khan - University of Dhaka<br>
* Rene Mandiau - LAMIH, Université de Valenciennes<br>
* Zinovi Rabinovich - Nanyang Technological University<br>
* Juan Antonio Rodriguez Aguilar - IIIA-CSIC<br>
* Marius Silaghi - FIT<br>
* William Yeoh - Washington University in St. Louis<br>
* Makoto Yokoo - Kyushu University<br>
* Roie Zivan - Ben Gurion University of the Negev<br>
* Maryam Tabar - Penn State University<br>
* Hangzhi Guo - Penn State University<br>
* Archie Chapman - University of Queensland<br>
* Harel Yedidsion - University of Texas at Austin<br>
* Pierre Rust - Orange Labs, France<br>
* Mohamed Wahbi - Collins Aerospace<br>
* Rica Gonen - Open University of Israel<em></em></font></p>
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<p><br>
</p>
<pre class="moz-signature" cols="72">--
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</pre>
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