Data structures and network algorithms
Data structures and network algorithms
A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
An O(v|v| c |E|) algoithm for finding maximum matching in general graphs
SFCS '80 Proceedings of the 21st Annual Symposium on Foundations of Computer Science
Constraint (Logic) Programming: A Survey on Research and Applications
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
Edge Matching Puzzles as Hard SAT/CSP Benchmarks
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Graph properties based filtering
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
WG'06 Proceedings of the 32nd international conference on Graph-Theoretic Concepts in Computer Science
Consistency of the matching predicate
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Counting-based search: branching heuristics for constraint satisfaction problems
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
The symmetric alldiff constraint is a particular case of the all diff constraint, a case in which variables and values are defined from the same set 5. That is, every variable represents an element c of S and its values represent the elements of S that are compatible with c. This constraint requires that all the values taken by the variables are different (similar to the classical all diff constraint) and that if the variable representing the element i is assigned to the value representing the element j, then the variable representing the element j is assigned to the value representing the element?. This constraint is present in many real-world problems, such sports scheduling where it expresses matches between teams. In this paper, we show how to compute the arc consistency of this constraint in O(n,m) (m = Σi|D(i)|), where n is the number of involved variables and D(i) the domain of the variable i. We also propose a filtering algorithm of less complexity (O(m)).