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

  • March 3, 2023 (23:59 UTC-12) – Submission Deadline
  • March 27, 2023 (23:59 UTC-12) – Acceptance notification
  • April 26, 2023 (23:59 UTC-12) – IJCAI Fast Track Submission Deadline
  • May 3, 2023 (23:59 UTC-12) – IJCAI Fast Track Acceptance Notification
  • May 29, 2023 – Workshop Date

Program

Schedule

Time (UK) GMT+1 Talk
8:30 Introductory remarks
8:40 Invited Talk 1 by Erich Wehrle: "Challenges in Design Space Exploration and Design Optimization of Aerospace Systems"
Session: Optimization and Learning -- Session chair: Jiaoyang Li
9:30 Contributed Talk: Asynchronous Communication Aware Multi-Agent Task Allocation
9:45 Contributed Talk: A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation
10:00 Coffee Break
Session 2: Optimization and Learning -- Session chair: Hau Chan
10:45 Contributed Talk: A DCOP Approach to Distributed Multi-Agent Pathfinding
11:00 Contributed Talk: A Novel Degree Planning Approach based on Mixed-Integer Programming
11:15 Contributed Talk: Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search
11:30 Contributed Talk: A Norm Approximation Approach for Research Project Planning
11:45 Contributed Talk: Towards a Unifying Model of Rationality in Multiagent Systems
12:00 Contributed Talk: Cooperative Personalized Bilevel Optimization over Random Directed Networks
12:15 Contributed Talk: Coordinating Fully-Cooperative Agents Using Hierarchical Learning Anticipation
12:30 Lunch Break
14:00 Invited Talk by Georgios Piliouras: "Towards a Unified Theory of Learning in Games: Chaos, Anarchy, Regret & Equilibration"
Session 3: Optimization and Learning with Strategic Agents -- Session chair: James Kotary
14:45 Contributed Talk: Efficient Size-based Hybrid Algorithm for Optimal Coalition Structure Generation
15:00 Contributed Talk: GENEPI: a GENEric Parameter-sharing for Intrinsically motivated MARL agents
15:15 Contributed Talk: Bayesian Rationality in Satisfaction Games
15:30 Contributed Talk: The Computational Complexity of Single-Player Imperfect-Recall Games
15:45 Coffee Break
Session 4: Optimization and Learning with Strategic Agents -- Session chair: Filippo Bistaffa
16:30 Contributed Talk: Learning not to Regret
16:45 Contributed Talk: Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
17:00 Contributed Talk: Ascending-Price Mechanism for General Multi-Sided Markets
17:15 Contributed Talk: A Recommendation System for Participatory Budgeting
17:30 Contributed Talk: Promoting Non-Cooperation Through Ordering
17:45 Closing Remarks

Accepted Papers

  • Asynchronous Communication Aware Multi-Agent Task Allocation.
    Ben Rachmut, Sofia Amador Nelke, and Roie Zivan.
  • Ascending-Price Mechanism for General Multi-Sided Markets.
    Dvir Gilor, Rica Gonen, and Erel Segal-Halevi.
  • A DCOP Approach to Distributed Multi-Agent Pathfinding.
    Arseni Pertzovskiy, Roie Zivan, and Roni Stern.
  • A Norm Approximation Approach for Research Project Planning.
    Roger X Lera Leri
  • A Recommendation System for Participatory Budgeting.
    Gil Leibiker and Nimrod Talmon
  • Learning not to Regret.
    David Sychrovský, Michal Sustr, Elnaz Davoodi, Marc Lanctot, and Martin Schmid
  • Bayesian Rationality in Satisfaction Games.
    Langford White, Oskar Rynkiewicz, Duong D Nguyen, and Hung Nguyen
  • Cooperative Personalized Bilevel Optimization over Random Directed Networks.
    Naoyuki Terashita and Satoshi Hara
  • GENEPI: a GENEric Parameter-sharing for Intrinsically motivated MARL agents.
    Joris DINNEWETH, Abderrahmane Boubezoul, René Mandiau, and Stéphane Espié
  • Promoting Non-Cooperation Through Ordering.
    David Sychrovský, Sameer Desai, and Martin Loebl
  • A Novel Degree Planning Approach based on Mixed-Integer Programming.
    Roger X Lera Leri, Tomas Trescak, and Juan Antonio Rodriguez Aguilar.
  • Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning.
    Filippos Christianos, Georgios Papoudakis, and Stefano V Albrecht
  • The Computational Complexity of Single-Player Imperfect-Recall Games
    Emanuel Tewolde, Caspar Oesterheld, Vincent Conitzer, and Paul W Goldberg
  • Coordinating Fully-Cooperative Agents Using Hierarchical Learning Anticipation.
    Ariyan Bighashdel, Daan de Geus, Pavol Jancura, and Gijs Dubbelman
  • Towards a Unifying Model of Rationality in Multiagent Systems.
    Robert Loftin, Mustafa Mert Celikok, and Frans A Oliehoek
  • A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation.
    Redha Taguelmimt, Samir Aknine, Djamila Boukredera, Narayan Mr Changder, and Tuomas Sandholm
  • Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search.
    Redha Taguelmimt, Samir Aknine, Djamila Boukredera, Narayan Mr Changder, and Tuomas Sandholm
  • Efficient Size-based Hybrid Algorithm for Optimal Coalition Structure Generation.
    Redha Taguelmimt, Samir Aknine, Djamila Boukredera, Narayan Mr Changder, and Tuomas Sandholm

Invited Talks

Challenges in design space exploration and design optimization of aerospace systems

Erich Wehrle, Collins Aerospace
Abstract:
New requirements for sustainable, efficient and economic aviation present the sector with new design possibilities and constraints. The aviation industry has committed to net-zero carbon emissions by 2050. This ambitious goal necessitates a new thinking in aircraft design to accelerate development with special attention to these goals in the development at aircraft as well as system and component levels.
Algorithmic-supported design methods in concert with accurate parametric models are powerful tools in the conception and development of aerospace systems and components. As such, applications range from optimal design of aircraft system architecture and high-performance structures of fiber-reinforced polymer to optimal parameters for manufacturing systems to optimal flight path design.
This talk will discuss reason-based system architecture exploration and design optimization of complex aircraft systems. As central role of multidisciplinary design optimization, this includes the use of physics-based parametric modeling and advanced multi-fidelity surrogating. The challenges to application of optimization and reason-based design exploration will be outlined and needs for future research in this area motivated.
Bio:
Dr.-Ing. Erich Wehrle leads the Multidisciplinary Design Optimization research group at Collins Aerospace. The team’s research focuses on algorithmic support in the design of complex aircraft systems, especially via system architecture exploration and design optimization considering multiple disciplines and physics-based simulation models.
He previously held roles in academia as Assistant Professor at the Free University of Bozen-Bolzano (Italy) and as postdoctoral researcher at the Technical University Munich (Germany) in which his research addressed design optimization of lightweight structures and mechanical systems under a multidisciplinary perspective, including structural analysis, multibody dynamics, crash mechanics and uncertainty. He holds a doctorate and a master in Mechanical Engineering from the Technical University of Munich and a bachelor in Mechanical Engineering from the State University of New York at Buffalo (USA).

Towards a Unified Theory of Learning in Games: Chaos, Anarchy, Regret & Equilibration

Georgios Piliouras, Google DeepMind
Abstract:
We examine some classic questions in game theory and online learning. How do standard online optimization, regret-minimizing dynamics such as multiplicative weights update, online gradient descent, and follow-the-regularized-leader (and variants thereof) behave when applied in games? The standard textbook answer to this question is that these dynamics converge in a time-average sense to weak notions of equilibria. We discuss weaknesses of such results in practice and turn our focus towards understanding the day-to-day behavior instead. In the seminal class of zero-sum games, continuous-time dynamics ``cycle" around the equilibrium, whereas discrete time-algorithms can behave in all possible ways (equilibration, recurrence/cycles, or unstable chaos) based only on minor changes. In the standard class of potential/congestion games, such dynamics experience phase transitions from stability to chaos as the total system demand increases. Such instabilities lead to high social inefficiency even in games where the Price of Anarchy is equal to one (i.e. where all equilibria are optimal). Our analysis is derived by a combination of distinct tools: optimization theory (regret analysis), dynamical systems (chaos theory) and game theory (equilibria, Price of Anarchy).
Bio:
Georgios Piliouras is a senior research scientist at Google DeepMind and an associate professor at the Singapore University of Technology and Design (on leave). His research interests lie in the areas of multi-agent learning, algorithmic game theory, blockchain and dynamical systems. He received his PhD in Computer Science from Cornell University. He has held research positions at the Georgia Institute of Technology (GaTech, ECE Dept.) and California Institute of Technology (Caltech, Dept. of Computing and Mathematical Sciences) as well as a visiting professor position at UC Berkeley. He is the recipient of a Singapore NRF Fellowship (2018) and a Simons/UC Berkeley Fellowship (2015). His work has been recognized with several distinctions including the outstanding paper award at AAAI (2021) and best paper runner-up at AAMAS (2019, 2022).

Submission Information

Submission URL: https://cmt3.research.microsoft.com/OptLearnMAS2023/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-23 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 IJCAI 2023 technical program are welcomed.

Fast Track (Rejected IJCAI papers)

Rejected IJCAI papers with *average* scores of at least weak reject/weak accept 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 a IJCAI rejected paper. Also OptLearnMAS submission, reviews and optional 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.

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

Ferdinando Fioretto

Syracuse University

ffiorett@syr.edu

Jiaoyang Li

Carnegie Mellon University

jiaoyangli@cmu.edu

Filippo Bistaffa

IIIA-CSIC

filippo.bistaffa@gmail.com

James Kotary

Syracuse University

jkotary@syracuse.edu