Approximating Bayesian Belief Networks by Arc Removal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraint Processing
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
Anytime local search for distributed constraint optimization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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
Asynchronous algorithms for approximate distributed constraint optimization with quality bounds
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Quality guarantees for region optimal DCOP algorithms
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Dynamic multiagent load balancing using distributed constraint optimization techniques
Web Intelligence and Agent Systems
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A Distributed Constraint Optimization Problem (DCOP) is a fundamental problem that can formalize various applications related to multi-agent cooperation. Since it is NP-hard, considering faster incomplete algorithms is necessary for large-scale applications. Most incomplete algorithms generally do not provide any guarantees on the quality of solutions. Some notable exceptions are DALO, the bounded max-sum algorithm, and ADPOP. In this paper, we develop a new solution criterion called p-optimality and an incomplete algorithm for obtaining a p-optimal solution. The characteristics of this algorithm are as follows: (i) it can provide the upper bounds of the absolute/ relative errors of the solution, which can be obtained a priori/a posteriori, respectively, (ii) it is based on a pseudo-tree, which is a widely used graph structure in complete DCOP algorithms, (iii) it is a one-shot type algorithm, which runs in polynomial-time in the number of agents n assuming p is fixed, and (iv) it has adjustable parameter p, so that agents can trade-off better solution quality against computational overhead. The evaluation results illustrate that this algorithm can obtain better quality solutions and bounds compared to existing bounded incomplete algorithms, while the run time of this algorithm is shorter.