Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
New methods to color the vertices of a graph
Communications of the ACM
Bumping strategies for the multiagent agreement problem
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Impact of problem centralization in distributed constraint optimization algorithms
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Solving abduction by computing joint explanations
Annals of Mathematics and Artificial Intelligence
An Efficient Algorithm for Solving Dynamic Complex DCOP Problems
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Balancing local resources and global goals in multiply-constrained DCOP
Multiagent and Grid Systems
Overlay networks for task allocation and coordination in large-scale networks of cooperative agents
Autonomous Agents and Multi-Agent Systems
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The Multiagent Agreement Problem (MAP) is a special form of Distributed Constraint Optimization (DCOP) that requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. We introduce the AdoptMVA algorithm, an extension of the existing Adopt algorithm, designed to take advantage of MAP domains where agents often control multiple variables. We also propose an approach to agent ordering which leverages known ordering techniques from the centralized and distributed constraint satisfaction literature and applies them to MAPs. By combining ordering at the agent level with orderings at the variable level, we hope to obtain efficient global orderings. While the contributions discussed in this paper are applicable to general DCOPs, we focus our evaluation on MAPs because we feel it is a significant problem class worthy of specific attention.