Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Distributed Sensor Networks: A Multiagent Perspective
Distributed Sensor Networks: A Multiagent Perspective
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
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
Comparing two approaches to dynamic, distributed constraint satisfaction
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Examining DCSP coordination tradeoffs
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Journal of Artificial Intelligence Research
Solving distributed CSPs using dynamic, partial centralization without explicit constraint passing
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Completeness and performance of the APO algorithm
Journal of Artificial Intelligence Research
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Distributed Constraint Satisfaction Problems (DCSPs) involve a vast number of AI and Multi-Agent problems. Many important efforts have been recently accomplished to solve these kinds of problems using both backtracking based and mediation based methods. One of the most successful algorithms in this field is Asynchronous Partial Overlay (APO). By choosing some agents as mediators, APO tries to centralize portions of the distributed problem. Each mediator tries to solve its centralized subproblem. This paper presents a new strategy for selecting mediators. The main idea behind this strategy is that the number of a mediator's conflicts (violated constraints) impacts directly on its performance. Experimental results show that choosing a mediator with the most conflicts leads to a considerable decrease in APO complexity. The results show a rapid and desirable improvement over various parameters in comparison with APO.