A sufficient condition for backtrack-bounded search
Journal of the ACM (JACM)
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
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Multiply-constrained distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
No-commitment branch and bound search for distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Asynchronous Forward-Bounding for Distributed Constraints Optimization
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Resource constrained distributed constraint optimization with virtual variables
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
H-DPOP: using hard constraints for search space pruning in DCOP
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Techniques for efficient interactive configuration of distribution networks
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A quantified distributed constraint optimization problem
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Bounded decentralised coordination over multiple objectives
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Reducing the search space of resource constrained DCOPs
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Stochastic dominance in stochastic DCOPs for risk-sensitive applications
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Optimal decentralised dispatch of embedded generation in the smart grid
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Considering Equality on Distributed Constraint Optimization Problem for Resource Supply Network
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Distributed Gibbs: a memory-bounded sampling-based DCOP algorithm
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
iCO2: promoting eco-driving practice through multiuser challenge optimization
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents' resource consumption must be taken into account. To address such scenarios, an extension of DCOP- Resource Constrained DCOP- has been proposed. However, certain type of resources have an additional structure associated with them and exploiting it can result in more efficient algorithms than possible with a general framework. An example of these are distribution networks, where the flow of a commodity from sources to sinks is limited by the flow capacity of edges. We present a new model of structured resource constraints that exploits the acyclicity and the flow conservation property of distribution networks. We show how this model can be used in efficient algorithms for finding the optimal flow configuration in distribution networks, an essential problem in managing power distribution networks. Experiments demonstrate the efficiency and scalability of our approach on publicly available benchmarks and compare favorably against a specialized solver for this task. Our results extend significantly the effectiveness of distributed constraint optimization for practical multi-agent settings.