Dual modelling of permutation and injection problems

Journal of Artificial Intelligence Research

Volumn 21, Issue , 2004, Pages 357-391

  Hnich, Brahim ^a   Smith, Barbara M ^b   Walsh, Toby ^a  

^a UNIVERSITY COLLEGE CORK   (Ireland)

^b Div Electronics and Commun Syst   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING; COMPUTER SIMULATION; CONSTRAINT THEORY; MATHEMATICAL MODELS;

CHANNELLING CONSTRAINTS; CONSTRAINT PROGRAMMING; INJECTION PROBLEMS; PERMUTATION;

PROBLEM SOLVING;

EID: 9444241384     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1351     Document Type: Review

References (27)

1. Bacchus, F., Chen, X., van Beek, P., & Walsh, T. (2002). Binary vs. Non-Binary Constraints. Artificial Intelligence, 140(1-2), 1-37.
2. Bejar, R., & Manya, F. (2000). Solving the round robin problem using propositional logic. In Proceedings of 17th National Conference on Artificial Intelligence, pp. 262-266. AAAI Press/The MIT Press.
3. Bessière, C., Meseguer, P., Freuder, E., & Larrosa, J. (1999). On forward checking for nonbinary constraint satisfaction. In Jaffar, J. (Ed.), Proceedings of Fifth International Conference on Principles and Practice of Constraint Programming (CP99), pp. 88-102. LNCS 1713. Springer.
4. Borrett, J., & Tsang, E. (1999). A context for constraint satisfaction problem formulation selection. Constraints, 6, 299-327.
5. Cheng, B., Choi, K., Lee, J., & Wu, J. (1999). Increasing constraint propagation by redundant modeling: an experience report. Constraints, 4, 167-192.
6. Choi, C., & Lee, J. (2002). On the pruning behaviour of minimal combined models for permutation CSPs. In Proceedings of CP-2002 Workshop on Reformulating Constraint Satisfaction Problems: Towards Systematisation and Automation.
7. Debruyne, R., & Bessière, C. (1997). Some practicable filtering techniques for the constraint satisfaction problem. In Proceedings of the 15th IJCAI, pp. 412-417. International Joint Conference on Artificial Intelligence.
8. Geelen, P. (1992). Dual viewpoint heuristics for binary constraint satisfaction problems. In Proceedings of the 10th ECAI, pp. 31-35. European Conference on Artificial Intelligence. Wiley.
9. Gent, I. (2002). Arc consistency in SAT. In van Harmelen, F. (Ed.), Proceedings of ECAI-2002, pp. 121-125. IOS Press.
10. Gent, I., Stergiou, K., & Walsh, T. (2000). Decomposable constraints. Artificial Intelligence, 123(1-2), 133-156.
11. Gent, I., & Walsh, T. (1999). Csplib: a benchmark library for constraints. Tech. rep., Technical report APES-09-1999. Available from http://dcs.st-and.ac.uk/~apes.
12. A shorter version appears in Jaffar, J. (Ed.), Proceedings of Fifth International Conference on Principles and Practice of Constraint Programming (CP99), pp. 480-481. LNCS 1713. Springer.
13. Haralick, R., & Elliot, G. (1980). Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14, 263-313.
14. Hentenryck, P. V., Deville, Y., & Teng, C. (1992). A Generic Arc Consistency Algorithm and its Specializations. Artificial Intelligence, 57, 291-321.
15. Jourdan, J. (1995). Concurrent Constraint Multiple Models in CLP and CC Languages: Toward a Programming Methodology by Modeling. Ph.D. thesis, Denis Diderot University, Paris VII. Available as CMU-CS-91-120.
16. Miller, J. (2002). Langford's problem.. Online description available at http://www.lcark.edu/~miller/langford.html.
17. Morris, P. (1992). On the density of solutions in equilibrium points for the queens problem. In Proceedings of the 10th National Conference on AI, pp. 428-433. American Association for Artificial Intelligence.
18. Nadel, B. (1990). Representation selection for constraint satisfaction: A case study using n-Queens. IEEE Expert, 5, 16-23.
19. Prosser, P., Stergiou, K., & Walsh, T. (2000). Singleton consistencies. In Dechter, R. (Ed.), 6th International Conference on Principles and Practices of Constraint Programming (CP-2000), pp. 353-368. LNCS 1894. Springer-Verlag.
20. Régin, J. (1994). A filtering algorithm for constraints of difference in CSPs. In Proceedings of the 12th National Conference on AI, pp. 362-367. American Association for Artificial Intelligence.
21. Régin, J., & Rueher, M. (2000). A global constraint combining a sum constraint and difference constraints. In Dechter, R. (Ed.), Proceedings of 6th International Conference on Principles and Practice of Constraint Programming (CP2000), pp. 384-395. LNCS 1894. Springer.
22. Schulte, C., & Stuckey, P. (2001). When do bounds and domain propagation lead to the same search space. In Sondergaard, H. (Ed.), Proceedings of 3rd International Conference on Principles and Practice of Declarative Programming (PPDP 2001), pp. 115-126. ACM Press.
23. Smith, B., Stergiou, K., & Walsh, T. (2000). Using auxiliary variables and implied constraints to model non-binary problems. In Proceedings of the 16th National Conference on AI, pp. 182-187. American Association for Artificial Intelligence.
24. Smith, B. (2000). Modelling a Permutation Problem. In Proceedings of ECAI'2000 Workshop on Modelling and Solving Problems with Constraints.
25. Also available as Research Report from http://scom.hud.ac.uk/stafF/ scombms/papers.html.
26. Stergiou, K., & Walsh, T. (1999). The difference all-difference makes. In Proceedings of 16th IJCAI, pp. 414-419. International Joint Conference on Artificial Intelligence.
27. Walsh, T. (2000). SAT v CSP. In Dechter, R. (Ed.), 6th International Conference on Principles and Practices of Constraint Programming (CP-2000), pp. 441-456. LNCS 1894. Springer-Verlag.