Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
Secure Distributed Constraint Satisfaction: Reaching Agreement without Revealing Private Information
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Message delay and DisCSP search algorithms
Annals of Mathematics and Artificial Intelligence
Algorithmic Game Theory
Distributed Search by Constrained Agents: Algorithms, Performance, Communication (Advanced Information and Knowledge Processing)
Decentralised coordination of low-power embedded devices using the max-sum algorithm
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
Social and Economic Networks
Analysis of privacy loss in distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
ODPOP: an algorithm for open/distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Completeness and performance of the APO algorithm
Journal of Artificial Intelligence Research
Asynchronous forward bounding for distributed COPs
Journal of Artificial Intelligence Research
Quality guarantees on k-optimal solutions for distributed constraint optimization problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distributed Constraint Optimization for Large Teams of Mobile Sensing Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Agent-based control for decentralised demand side management in the smart grid
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Graphical models for game theory
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Concurrent forward bounding for distributed constraint optimization problems
Artificial Intelligence
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Distributed Constraint Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. Many multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the standard DCOP model. The present paper proposes a general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may have different valuations for constraints that they are involved in. The new framework bridges the gap between multi-agent problems which tend to have asymmetric structure and the standard symmetric DCOP model. The benefits of the proposed model over previous attempts to generalize the DCOP model are discussed and evaluated. Innovative algorithms that apply to the special properties of the proposed ADCOP model are presented in detail. These include complete algorithms that have a substantial advantage in terms of runtime and network load over existing algorithms (for standard DCOPs) which use alternative representations. Moreover, standard incomplete algorithms (i.e., local search algorithms) are inapplicable to the existing DCOP representations of asymmetric constraints and when they are applied to the new ADCOP framework they often fail to converge to a local optimum and yield poor results. The local search algorithms proposed in the present paper converge to high quality solutions. The experimental evidence that is presented reveals that the proposed local search algorithms for ADCOPs achieve high quality solutions while preserving a high level of privacy.