International Joint Workshop on
Optimisation in Multi-Agent Systems and
Distributed Constraint Reasoning
(OptMAS-DCR)

In conjunction with AAMAS 2014
Paris, France
May 5-6, 2014

This joint OptMAS-DCR 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 10, 2014 - Extended Submission Deadline
  • March 1, 2014 - Acceptance Notification
  • March 10, 2014 - Camera-Ready Deadline
  • May 5-6, 2014 - Workshop Date

About

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

As this is a joint OptMAS-DCR workshop, there will be particular emphasis on 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

Submissions

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: http://www.springer.de/comp/lncs/authors.html.

All submissions are conducted via the OptMAS-DCR 2014 EasyChair website: http://www.easychair.org/conferences/?conf=optmasdcr2014.

Submissions should include the name(s), affiliations, and email addresses of all authors in the body of the email. We welcome the submission of papers rejected from the AAMAS 2014 technical program. The deadline for receipt of submissions is January 22, 2014. 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.

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

PROGRAM + TUTORIAL

DAY 1: MAY 5, 2014

SESSION 1 (9:00 - 10:30)
Security And Patrolling

Using Machine Learning for Operational Decisions in Adversarial Environments
Yevgeniy Vorobeychik and John Ross Wallrabenstein

Computing Minimax Strategy for Discretized Spatio-Temporal Zero-Sum Security Games
Haifeng Xu, Fei Fang, Albert Xin Jiang, Vincent Conitzer, Shaddin Dughmi and Milind Tambe 

Multi-Agent Patrolling under Uncertainty and Threats
Shaofei Chen, Feng Wu, Lincheng Shen, Jing Chen and Sarvapali Ramchurn

Opportunistic Security Game: An Initial Report
Chao Zhang, Albert Xin Jiang, Martin B. Short, P. Jeffrey Brantingham and Milind Tambe


SESSION 2 (11:00 - 12:30)
Multi-Objective DCOP

Discriminative MO-COP Operators
Nicolas Schwind, Tenda Okimoto, Tony Ribeiro, Sebastien Konieczny and Katsumi Inoue

PaCcET: An Objective Space Transformation to Shape the Pareto Front and Eliminate Concavity
Logan Yliniemi and Kagan Tumer

Model and Algorithm for Dynamic Multi-Objective Distributed Optimization
Maxime Clement, Tenda Okimoto, Tony Ribeiro and Katsumi Inoue


SESSION 3 (14:00 - 17:00)
Tutorial [slides]

 

Invited Talk (by Toby Walsh, NICTA and UNSW, Australia)

Title: Decentralized Mechanisms for Fair Division

Abstract: Fair division is the problem of dividing a set of goods between several agents, such that each agent receives a due share. This problem arises in various real-world settings when allocating resource: auctions, divorce settlements, electronic spectrum and frequency allocation, and airport traffic management. It brings together Mathematics, Economics (especially Social choice theory), and Game theory. In this talk, I will discuss some of our recent results about decentralized mechanisms for fair division. Such mechanisms have a number of advantages including privacy and trust, but raise interesting questions regarding efficiency and strategic behaviour.


SESSION 4 (17:00 - 18:00)
Constraints

A Simple Polynomial-Time Randomized Distributed Algorithm for Connected Row Convex Constraints
T.K. Satish Kumar, Duc Thien Nguyen, William Yeoh and Sven Koenig

Distributed Problem Solving in Geometrically-Structured Constraint Networks
Roger Mailler and Huimin Zheng


DAY 2: MAY 6, 2014

SESSION 5 (9:00 - 10:30)
Coalition Structure Generation

Anytime Coalition Structure Generation on Scale-Free and Community Networks
Filippo Bistaffa, Alessandro Farinelli, Jesus Cerquides, Juan A. Rodriguez-Aguilar and Sarvapali D. Ramchurn

Binary max-sum for multi-team task allocation in RoboCup Rescue
Marc Pujol-Gonzalez, Jesus Cerquides, Alessandro Farinelli, Pedro Meseguer and Juan A. Rodriguez-Aguilar

Learning and Evolutionary Approaches

Neuroevolution of a Multi-Agent System for the Dynamic Pickup and Delivery Problem
Jonathan Merlevede, Rinde R.S. van Lon and Tom Holvoet

Decentralized Multi-Agent Reinforcement Learning in Average-Reward Dynamic DCOPs
Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein and Chongjie Zhang


SESSION 6 (11:00 - 12:30)
Applications and Simulations

Equilibria of EV Charging
Benny Lutati, Vadim Levit, Tal Grinshpoun and Amnon Meisels

Applying DCOP_MST to a Team of Mobile Robots with Directional Sensing Abilities
Harel Yedidsion and Roie Zivan

Simulation vs Real Execution in DCOP Solving
Francisco Cruz, Patricia Gutierrez and Pedro Meseguer

AgentZero: A Framework for Simulating and Evaluating Multi-Agent Algorithms
Benny Lutati, Vadim Levit and Amnon Meisels

PROGRAM COMMITTEE

Contact

Workshop Co-Chairs: