The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
A General Scheme for Multiple Lower Bound Computation in Constraint Optimization
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Constraint Processing
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
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Nogood based asynchronous distributed optimization (ADOPT ng)
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
MB-DPOP: a new memory-bounded algorithm for distributed optimization
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
Improving tree decomposition methods with function filtering
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Unifying tree decompositions for reasoning in graphical models
Artificial Intelligence
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Sensor networks and distributed CSP: communication, computation and complexity
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Communication-constrained DCOPs: message approximation in GDL with function filtering
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Max/min-sum distributed constraint optimization through value propagation on an alternating DAG
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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DPOP is an algorithm for distributed constraint optimization which has, as main drawback, the exponential size of some of its messages. Recently, some algorithms for distributed cluster tree elimination have been proposed. They also suffer from exponential size messages. However, using the strategy of cost function filtering, in practice these algorithms obtain important reductions in maximum message size and total communication cost. In this paper, we explain the relation between DPOP and these algorithms, and show how cost function filtering can be combined with DPOP. We present experimental evidence of the benefits of this new approach.