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
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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
Algorithmic Game Theory
Distributed Search by Constrained Agents: Algorithms, Performance, Communication (Advanced Information and Knowledge Processing)
Decentralised coordination of continuously valued control parameters using the max-sum algorithm
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Analysis of privacy loss in distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Anytime local search for distributed constraint optimization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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
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
A negotiation framework for linked combinatorial optimization problems
Autonomous Agents and Multi-Agent Systems
Partial cooperation in multi-agent search
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Finding a nash equilibrium by asynchronous backtracking
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Taxation search in boolean games
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Distributed Constraints Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. DCOP algorithms search for the optimal solution, optimizing the total gain (or cost) that is composed of all gains of all agents. Local search (LS) DCOP algorithms search locally for an approximate such solution. 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). The new framework is described and its differences from former attempts are discussed. New local search algorithms for ADCOPs are introduced and their advantages over existing algorithms and over former representations are discussed in detail. The new proposed algorithms for the ADCOP framework are evaluated experimentally and their performance compared to existing algorithms. Two measures of performance are used: quality of solutions and loss of privacy. The results show that the new algorithms significantly outperform existing DCOP algorithms with respect to both measures.