Arc and path consistence revisited
Artificial Intelligence
Local and global relational consistency
Theoretical Computer Science - Special issue: principles and practice of constraint programming
A Strong Local Consistency for Constraint Satisfaction
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Inverse Consistencies for Non-binary Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Domain filtering consistencies
Journal of Artificial Intelligence Research
A study of residual supports in arc consistency
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Optimal and suboptimal singleton arc consistency algorithms
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Optimization of Simple Tabular Reduction for Table Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Path consistency by dual consistency
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Journal of Artificial Intelligence Research
A framework for decision-based consistencies
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Revisiting neighborhood inverse consistency on binary CSPs
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
| Hi-index | 0.00 |
Consistencies are properties of Constraint Networks (CNs) that can be exploited in order to make inferences. When a significant amount of such inferences can be performed, CNs are much easier to solve. In this paper, we interest ourselves in relation filtering consistencies for binary constraints, i.e. consistencies that allow to identify inconsistent pairs of values. We propose a new consistency called Dual Consistency (DC) and relate it to Path Consistency (PC). We show that Conservative DC (CDC, i.e. DC with only relations associated with the constraints of the network considered) is more powerful, in terms of filtering, than Conservative PC (CPC). Following the approach of Mac Gregor, we introduce an algorithm to establish (strong) CDC with a very low worst-case space complexity. Even if the relative efficiency of the algorithm introduced to establish (strong) CDC partly depends on the density of the constraint graph, the experiments we have conducted show that, on many series of CSP instances, CDC is largely faster than CPC (up to more than one order of magnitude). Besides, we have observed that enforcing CDC in a preprocessing stage can significantly speed up the resolution of hard structured instances.