International Workshop on
Optimisation in Multi-Agent Systems

In conjunction with AAMAS 2016
May 10, 2016

This OptMAS 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 problems. We will also place a particular emphasis on DCR approaches, which include the modeling, formulation and solution of DCR problems, including both Distributed Constraint Satisfaction and Optimization Problems.


Important Dates:

  • February 1, 2016 February 8, 2016 - Extended Submission Deadline
  • March 7, 2016 - Acceptance Notification
  • March 10, 2016 - Camera-Ready Deadline
  • May 10, 2016 - Workshop Date


The number of novel applications of multi-agent systems has followed an exponential trend over the last few years, ranging from online auction design, through multi-sensor networks, to scheduling of tasks in multi-actor systems. Multi-agent systems designed for all these applications generally involve some form of very hard optimization problems that are substantially different from problems traditionally dealt with in other areas (e.g. industrial processes or scheduling applications). More specifically, the technical issues that multi-agent algorithm designers have to deal with include:

  • Open systems: algorithms to compute solutions to mechanisms that deal with different stakeholders, who may be self-interested or may have different computation or communication capabilities from their peers.
  • Distributed systems: algorithms that are across different system components, such as those that deal with agents that are tied to physical devices. This involves considerations of computation and communication constraints, and the possibility of failures of the components and/or communication links.
  • Privacy concerns: solving optimization problems while minimising the exchange of private information.
  • Solution quality bounds: problems requiring anytime and/or approximate algorithms with quality bounds.
  • Robust optimisation: techniques to deal with optimizations that are repeated with only slight changes in the input data and/or with unreliable input data, which require solutions that are robust to these differences.
  • Highly parallel architectures: e.g., multi-core, GPGPU, which deal with large-scale problems with massive data and task parallelism.

In particular, workshop organizers are seeking papers on the following (but not limited to) topics:

  • Distributed constraint optimization/satisfaction
  • 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

OptMAS places particular emphasis on distributed constraint reasoning (DCR) approaches, which include the modeling, formulation and solution of DCR problems, including both Distributed Constraint Satisfaction and Optimization Problems. DCR problems arise when pieces of information about variables, constraints or both are relevant to independent but communicating agents. They provide a promising framework to deal with the increasingly diverse range of distributed real world problems emerging from the evolution of computation and communication technologies. Example DCR-specific technical issues include:

  • unified frameworks for distributed constraint reasoning
  • complete and incomplete algorithms for solving distributed constraint reasoning problems
  • privacy issues in distributed constraint reasoning
  • problem solving in systems with self-interested agents
  • negotiation among self-interested agents
  • distributed constraint propagation and consistency
  • generation and formulation/modeling of distributed constraint reasoning
  • applications of distributed constraint reasoning


Participants should submit a paper (maximum 15 pages), describing their work on one or more of the topics relevant to the workshop. Alternatively, participants may submit a shorter paper (maximum 5 pages) presenting a research statement or perspective on topics relevant to the workshop. Accepted papers will be presented during the workshop and will be published in the workshop proceedings.

Authors are requested to prepare their papers by following the LNCS Springer instructions found at:

All submissions are conducted via the OptMAS 2016 EasyChair website:

Submissions should include the name(s), affiliations, and email addresses of all authors. We welcome the submission of papers rejected from the AAMAS 2016 technical program. The deadline for receipt of submissions is February 1, 2016. Papers received after this date may not be reviewed.

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.

The most "visionary paper" will be published by Springer in a book under the Lecture Notes in Artificial Intelligence (LNAI) - Hot Topics series. The book will be a compilation of the most visionary papers of the AAMAS-2016 Workshops, where one paper will be selected from each AAMAS-2016 workshop. Additionally, the "best paper" will be published by Springer in a book under the Communications in Computer and Information Science (CCIS) series. The book will be a compilation of the best papers of the AAMAS-2016 Workshops, where one paper will be selected from each AAMAS-2016 workshop. Authors of the selected most visionary paper and the best paper are expected to provide their latex files promptly upon request.

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


08.30-09.00: Registration

09.00-09.30: Evaluating the Efficiency of Robust Team Formation Algorithms
Chad Crawford, Zenefa Rahaman and Sandip Sen

09.30-10.00: Optimising Congenial Teams
Ewa Andrejczuk, Juan Antonio Rodriguez-Aguilar and Carles Sierra

10.00-10.30: Speeding Up GDL-based Message Passing Algorithms for Large-Scale DCOPs
Md. Mosaddek Khan, Sarvapali D. Ramchurn, Long Tran-Thanh and Nicholas R. Jennings

10.30-11.00: Coffee Break

11.00-12.30: Two-sided Matching: Optimization under Incentive, Stability, and Distributional Constraints
Invited talk by Makoto Yokoo

The theory of two-sided matching (e.g., assigning residents to hospitals, students to schools) has been extensively developed, and it has been applied to design clearinghouse mechanisms in various markets in practice. As the theory has been applied to increasingly diverse types of environments, however, researchers and practitioners have encountered various forms of distributional constraints. As these features have been precluded from consideration until recently, they pose new challenges for market designers. One example of such distributional constraints is a minimum quota, e.g., school districts may need at least a certain number of students in each school in order for the school to operate. In this talk, I present an overview of research on designing mechanisms that work under distributional constraints.

About the speaker:
Makoto Yokoo received the B.E., M.E., and Ph.D. degrees in 1984, 1986, and 1995, respectively, form the University of Tokyo, Japan. He is currently a Distinguished Professor of Information Science and Electrical Engineering, Kyushu University, Japan. He served as a general co-chair of International Conference on Autonomous Agents and Multi-Agent Systems in 2007 (AAMAS-2007), and as a program co-chair of AAMAS-2003. He is the past president of International Foundation for Autonomous Agent and Multiagent Systems (IFAAMAS). He is a fellow of the Association for Advancement of Artificial Intelligence (AAAI). He received the ACM SIGART Autonomous Agents Research Award in 2004, and the IFAAMAS influential paper award in 2010.

12.30-14.00: Lunch

14.00-14.30: Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network
Hala Mostafa, Akshat Kumar and Hoong Chuin Lau

14.30-15.00: Improving Approximate Algorithms for DCOPs Using Ranks
Andreas Flueckiger, Mihaela Verman and Abraham Bernstein

15.00-15.30: Exploring Hybrid Iterative Approximate Best-Response Algorithms for Solving DCOPs
Mihaela Verman, Philip Stutz, Robin Hafen and Abraham Bernstein

15.30-16.00: Coffee Break

16.00-16.30: Solving Distributed Constraint Optimization Problems Using Logic Programming
Tiep Le, Tran Cao Son, Enrico Pontelli and William Yeoh

16.30-17.00: Probabilistic Inference Based Message-Passing For Resource Constrained DCOPs
Supriyo Ghosh, Akshat Kumar and Pradeep Varakantham

17.00-17.30: Applying DCOP MST to a Team of Mobile Robots with Directional Sensing Abilities
Harel Yedidsion and Roie Zivan




Workshop Co-Chairs: