An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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
Distributed constraint optimization for multiagent systems
Distributed constraint optimization for multiagent systems
Hierarchical variable ordering for distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Optimal Solution Stability in Dynamic, Distributed Constraint Optimization
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Superstabilizing, fault-containing distributed combinatorial optimization
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Dynamic distributed constraint reasoning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Partial constraint satisfaction
IJCAI'89 Proceedings of the 11th international joint 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
Multiagent based scheduling of elective surgery
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
An intelligent approach to surgery scheduling
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
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Multi Agent Systems and the Distributed Constraint Optimization Problem (DCOP) formalism offer several asynchronous and optimal algorithms for solving naturally distributed optimization problems efficiently. There has been good application of this technology in addressing real world problems in areas like Sensor Networks and Meeting Scheduling. Most of these algorithms however exploit static tree structures and are thus not well suited to modeling and solving problems in rapidly changing domains. Also, while in theory most DCOP algorithms can be extended to handle complex local sub-problems, we argue that this generally results in making their performance sub-optimal, and thus their application less suitable. In this paper we present new measures that emphasize the interconnectedness between each agent's local and inter-agent sub-problems and use these measures to guide dynamic agent ordering during distributed constraint reasoning. The resulting algorithm, DCDCOP, offers a robust, flexible, and efficient mechanism for modeling and solving dynamic complex problems. Experimental evaluation of the algorithm shows that DCDCOP significantly outperforms ADOPT, the gold standard in search-based DCOP algorithms.