A java toolkit for teaching distributed algorithms
Proceedings of the 7th annual conference on Innovation and technology in computer science education
Using Cooperative Mediation to Solve Distributed Constraint Satisfaction Problems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Impact of problem centralization in distributed constraint optimization algorithms
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
On k-optimal distributed constraint optimization algorithms: new bounds and algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Anytime local search for distributed constraint optimization
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Balancing local resources and global goals in multiply-constrained DCOP
Multiagent and Grid Systems
Two decades of multiagent teamwork research: past, present, and future
CARE@AI'09/CARE@IAT'10 Proceedings of the CARE@AI 2009 and CARE@IAT 2010 international conference on Collaborative agents - research and development
Quality guarantees for region optimal DCOP algorithms
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Communication-constrained DCOPs: message approximation in GDL with function filtering
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Pseudo-tree-based algorithm for approximate distributed constraint optimization with quality bounds
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Pseudo-tree-based incomplete algorithm for distributed constraint optimization with quality bounds
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
SBDO: a new robust approach to dynamic distributed constraint optimisation
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Flood disaster mitigation: a real-world challenge problem for multi-agent unmanned surface vehicles
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Multi-agent coordination: dcops and beyond
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Max/min-sum distributed constraint optimization through value propagation on an alternating DAG
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Risk-neutral bounded max-sum for distributed constraint optimization
Proceedings of the 28th Annual ACM Symposium on Applied Computing
DeQED: an efficient divide-and-coordinate algorithm for DCOP
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
DeQED: an efficient divide-and-coordinate algorithm for DCOP
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Distributed Constraint Optimization (DCOP) is a popular framework for cooperative multi-agent decision making. DCOP is NP-hard, so an important line of work focuses on developing fast incomplete solution algorithms for large-scale applications. One of the few incomplete algorithms to provide bounds on solution quality is k-size optimality, which defines a local optimality criterion based on the size of the group of deviating agents. Unfortunately, the lack of a general-purpose algorithm and the commitment to forming groups based solely on group size has limited the use of k-size optimality. This paper introduces t-distance optimality which departs from k-size optimality by using graph distance as an alternative criteria for selecting groups of deviating agents. This throws open a new research direction into the tradeoffs between different group selection and coordination mechanisms for incomplete DCOP algorithms. We derive theoretical quality bounds for t-distance optimality that improve known bounds for k-size optimality. In addition, we develop a new efficient asynchronous local search algorithm for finding both k-size and t-distance optimal solutions --- allowing these concepts to be deployed in real applications. Indeed, empirical results show that this algorithm significantly outperforms the only existing algorithm for finding general k-size optimal solutions, which is also synchronous. Finally, we compare the algorithmic performance of k-size and t-distance optimality using this algorithm. We find that t-distance consistently converges to higher-quality solutions in the long run, but results are mixed on convergence speed; we identify cases where k-size and t-distance converge faster.