Scope and Topics

Stimulated by various emerging applications involving agents to solve complex problems in real-world domains, such as intelligent sensing systems for the Internet of the 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 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

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

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 technical sessions, a 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.

Attendance

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

Important Dates

  • Feb 19, 2024 (23:59 UTC-12) – Submission Deadline
  • Mar 18, 2024 (23:59 UTC-12) – Acceptance notification
  • May 6-7, 2024 – Workshop Date

Submission Information

Submission URL: https://cmt3.research.microsoft.com/OptLearnMAS2024/Submission/Index

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.

All papers must be submitted in PDF format, using the AAMAS-24 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.

Fast Track

TBD

Best Papers

Per the AAMAS Workshop organizers:
There will be a Springer issue for best workshop papers and visionary papers, so each workshop should nominate two papers, one for each special issue. The authors should be aware that if the nominated workshop paper is also an AAMAS paper (or some other conference paper), the version in the Springer books should have additional material (at least 30% more).

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

Workshop Chairs

Hau Chan

University of Nebraska-Lincoln

hchan3@unl.edu

Jiaoyang Li

Carnegie Mellon University

jiaoyangli@cmu.edu

Filippo Bistaffa

IIIA-CSIC

filippo.bistaffa@gmail.com

Xinrun Wang

Nanyang Technological University

xinrun.wang@ntu.edu.sg