Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed Algorithms
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
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
Preprocessing techniques for accelerating the DCOP algorithm ADOPT
Proceedings of the fourth 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
Message delay and DisCSP search algorithms
Annals of Mathematics and Artificial Intelligence
AND/OR search spaces for graphical models
Artificial Intelligence
Distributed Search by Constrained Agents: Algorithms, Performance, Communication (Advanced Information and Knowledge Processing)
Evaluating the performance of DCOP algorithms in a real world, dynamic problem
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Using relaxations to improve search in distributed constraint optimisation
Artificial Intelligence Review
Decentralised coordination of continuously valued control parameters using the max-sum algorithm
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
ODPOP: an algorithm for open/distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
Artificial Intelligence
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Completeness and performance of the APO algorithm
Journal of Artificial Intelligence Research
Asynchronous forward bounding for distributed COPs
Journal of Artificial Intelligence Research
Taking advantage of stable sets of variables in constraint satisfaction problems
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Concurrent search for distributed CSPs
Artificial Intelligence
Min-domain ordering for asynchronous backtracking
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Deception in networks of mobile sensing agents
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
Asymmetric distributed constraint optimization problems
Journal of Artificial Intelligence Research
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A distributed search algorithm for solving Distributed Constraints Optimization Problems (DCOPs) is presented. The new algorithm scans the search space by using multiple search processes (SPs) that run on all agents concurrently. SPs search in non-intersecting parts of the global search space and perform Branch & Bound search. Each search process (SP) uses the mechanism of forward bounding (FB) to prune efficiently its part of the global search space. The Concurrent Forward-Bounding (ConcFB) algorithm enables all SPs to share their upper bound across all parts of the global search space. The number of concurrent SPs is controlled dynamically by the ConcFB algorithm, by performing dynamic splitting. Within each SP a dynamic variable ordering is employed in order to help control the balance of computational load among all agents and across different SPs. The ConcFB algorithm is evaluated experimentally and compared to all state of the art DCOP algorithms. The number of Non-Concurrent Logical Operations, Non-Concurrent Steps, the total number of messages sent and CPU time are used as performance metrics. The evaluation procedure considers different DCOP problem types with a varying number of agents and different constraint graphs. As problems become larger and denser, ConcFB is shown to outperform all other evaluated algorithms by 2-3 orders of magnitude in all performance measures. Further evaluations comparing different variants of ConcFB provide important insights into the working of the algorithm and reveals the contribution of its different components.