Node and arc consistency in weighted CSP
Eighteenth national conference on Artificial intelligence
Constraint Processing
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
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
| Hi-index | 0.00 |
Distributed constraint optimization problems with finite domains can be solved by asynchronous procedures. ADOPT is the reference algorithm for this kind of problems. Several versions of this algorithm have been proposed, one of them is BnB-ADOPT which changes the nature of the original algorithm from best-first to depth-first search. With BnB-ADOPT, we can assure in some cases that the value of a variable will not be used in the optimal solution. Then, this value can be deleted unconditionally. The contribution of this work consists in propagating these unconditionally deleted values using soft arc consistency techniques, in such a way that they can be known by other variables that share cost functions. When we propagate these unconditional deletions we may generate some new deletions that will also be propagated. The global effect is that we search in a smaller space, causing performance improvements. The effect of the propagation is evaluated on several benchmarks.