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
ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking
Autonomous Agents and Multi-Agent Systems
Distributed constraint optimization with structured resource constraints
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Directed soft arc consistency in pseudo trees
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
ODPOP: an algorithm for open/distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Resource constrained distributed constraint optimization with virtual variables
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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
Bounded decentralised coordination over multiple objectives
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Distributed resource allocation is an important application of multiagent systems. In this work, we focus on a resource allocation problem that is motivated from a power supply network that contains distributed sources. In the supply network, resources that are initially distributed among source nodes have to be shared among all nodes. The problem is formalized as a resource constrained distributed constraint optimization problem that is an extended class of distributed constraint optimization problems (DCOPs). In the formalization, cost functions represent preferences of agents on resource use. We specifically consider allocating the cost values to agents as evenly as possible. We present several methods to select optimal assignment in consideration of the equality. The characteristics of the proposed methods are experimentally evaluated. Employing histograms and distinguishing types are effective for reducing the variance while requiring high computational costs. The number of histograms is able to be limited without significant lack of the effects. The minimum-maximum cost value as the main objective reduces the number of iterations and histograms.