A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Enforcing Arc Consistency on Global Constraints by Solving Subproblems on the Fly
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
A Constraint Programming Approach to the Stable Marriage Problem
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
An efficient way of breaking value symmetries
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Dual modelling of permutation and injection problems
Journal of Artificial Intelligence Research
Breaking symmetries in all different problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Constraint programming models for graceful graphs
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Symmetry and search in a network design problem
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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We present three constraint models of the problem of finding a graceful labelling of a graph, or proving that the graph is not graceful. An experimental comparison of the models applied to different classes of graph is given. The first model seems a natural way to represent the problem, but explores a much larger search tree than the other models. The second model does much less search, by making the most constrained decisions first, but is slow because the constraints are time-consuming to propagate. The third model combines the best features of the others, doing little more search than the second model while being much the fastest of the three. The comparison of the three models provides a useful case-study of modelling problems as constraint satisfaction problems. In addition, we show that constraint programming can be a useful tool for the study of graceful graphs; the models presented here have contributed many new results.