The goal of the workshop is to provide researchers with a venue to explore how to model and solve a variety of multi-agent optimization problems. We seek contributions in the general area of multi-agent optimization, including distributed constraint optimization, coalition formation, optimization under uncertainty, winner determination algorithms in auctions, and algorithms to compute Nash and other equilibria in games.

Scope

The number of novel applications of multi-agent systems has followed an exponential growth 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 these applications, generally, involve solving some form of optimization problems and have to cope with several important issues, including:
  • 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 Algorithms: 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 preserving the privacy of the information exchanged.
  • Solution quality bounds: problems requiring anytime and/or approximate algorithms with quality bounds.
  • Robust optimization: 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.

Topics of Interest

  • 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

Submission Information

Conference submission site: https://easychair.org/conferences/?conf=optmas19
Submissions should conform to the LNCS Springer format, the style file or Word templates can be found at https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

Submissions should include the name(s), affiliations, and email addresses of all authors. We welcome the submission of papers rejected from the AAMAS 2019 and IJCAI 2019 technical program.
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.

Submission Types

  • Long Papers: Full-length research papers detailing work in progress or work that could potentially be published at a major conference. These papers should not exceed *15* pages in LCNS format (excluding bibliography and appendices).
  • Short Papers: Position or demo papers that describe initial work or an application that has not yet been evaluated on the topics of interest. These papers should not exceed *5* pages in the LNCS format (excluding bibliography and appendices).

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

Important Dates

  • March 12 - Submission Deadline (extended)
  • April 10 - Acceptance Notification
  • April 27 - Camera-Ready Deadline
  • Tuesday May 14, 2019 - Workshop Date (Full day)

Technical Program

Location: Room: MB 9C

Invited Speaker

Pascal Van Hentenryck

Georgia Institute of Technology


Pascal Van Hentenryck is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He is an INFORMS Fellow and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). He holds two honorary doctoral degrees and is the recipient of an NSF Young Investigator Award, the 2006 ACP Award for Research Excellence in Constraint Programming, the 2002 ICS INFORMS Award for Research Excellence at the Intersection of Computer Science and Operations Research, and the Philip J. Bray Award for teaching excellence in the physical sciences at Brown University. Van Hentenryck is the designer of several optimization systems that are widely used commercially. His current research is focusing artificial intelligence, data science, and operations research with applications in energy systems, mobility, and privacy.

Optimization of Large-Scale Mobility Systems

Abstract: The convergence of several technology enablers, including ubiquitous connectivity, autonomous vehicles, and sophisticated analytics, provides unique opportunities to fundamentally transform mobility in the next decade. This talk reviews a number of new mobility concepts to solve the first/last mile problem, congestion, parking pressure, and greenhouse gas emissions and the optimization and machine learning technology to power them. Evaluations on real-case studies are also presented.



Panel Discussion

Topic Distributed Optimization and Machine Learning
Members

Program Committee

Contact

Workshop Co-Chairs:
contact us

Past Editions