Consistency for partially defined Constraints

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 2005, Issue , 2005, Pages 118-125

  Lallouet, Arnaud ^a   Legtchenko, Andreï ^a  

^a UNIVERSITÉ D'ORLÉANS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT PROJECTIONS; PRUNING DOMAINS;

CLASSIFICATION (OF INFORMATION); COMPUTATION THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING;

CONSTRAINT THEORY;

EID: 33845864698     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2005.49     Document Type: Conference Paper

References (20)

1. Slim Abdennadher and Christophe Rigotti. Automatic generation of rule-based constraint solvers over finite domains. Transaction on Computational Logic, 5(2), 2004.
2. M. Alberti, M. Gavanelli, E. Lamma, P. Mello, and M. Milano. A chr-based implementation of known arc-consistency. Theory and Practice of Logic Programming, to appear.
3. K.R. Apt. The essence of constraint propagation. Theoretical Computer Science, 221(1-2):179-210, 1999.
4. K.R. Apt and E. Monfroy. Automatic generation of constraint propagation algorithms for small finite domains. In Joxan Jaffar, editor, International Conference on Principles and Practice of Constraint Programming, volume 1713 of LNCS, pages 58-72, Alexandria, Virginia, USA, 1999. Springer.
5. Christian Bessière, Rémi Coletta, Eugene C. Freuder, and Barry O'Sullivan. Leveraging the learning power of examples in automated constraint acquisition. In Mark Wallace, editor, Principles and Practice of Constraint Programming, volume 3258 of LNCS, pages 123-137, Toronto, Canada, 2004. Springer.
6. Christian Bessière, Emmanuel Hebrard, Brahim Hnich, and Toby Walsh. The complexity of global constraints. In Deborah L. McGuinness and Georges Ferguson, editors, National Conference on Artificial Intelligence, pages 112-117, San Jose, CA, USA, July, 25-29 2004. AAAI Press / MIT Press.
7. Christian Bessière and Jean-Charles Régin. Arc-consistency for general constraint networks: preliminary results. In IJCAI, pages 398-404, Nagoya, Japan, 1997. Morgan Kaufmann.
8. R. Coletta, C. Bessière, B. O'Sullivan, E. C. Freuder, S. O'Connell, and J. Quinqueton. Semi-automatic modeling by constraint acquisition. In Francesca Rossi, editor, International Conference on Principles and Practice of Constraint Programming, number 2833 in LNCS, pages 812-816, Kinsale, Ireland, 2003. Springer.
9. A. Davenport, E. Tsang, C. Wang, and K. Zhu. GENET: A connectionist architecture for solving constraint satisfaction problems by iterative improvement. In National Conference on Artificial Intelligence, pages 325-330, Seattle, WA, USA, 1994. AAAI Press.
10. Boi Faltings and Santiago Macho-Gonzalez. Open constraint satisfaction. In CP, volume 2470 of LNCS, pages 356-370. Springer, 2002.
11. Boi Faltings and Santiago Macho-Gonzalez. Open constraint satisfaction. In Pascal van Hentenryck, editor, International Conference on Principles and Practice of Constraint Programming, volume 2470 of LNCS, pages 356-370, Ithaca, NY, USA, Sept. 7-13 2002. Springer.
12. Y. Freund and R. Shapire. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, 1999.
13. Arnaud Lallouet, Thi-Bich-Hanh Dao, Andreï Legtchenko, and AbdelAli Ed-Dbali. Finite domain constraint solver learning. In Georg Gottlob, editor, International Joint Conference on Artificial Intelligence, pages 1379-1380, Acapulco, Mexico, 2003. AAAI Press.
14. Arnaud Lallouet, Andreï Legtchenko, Eric Monfroy, and AbdelAli Ed-Dbali. Solver learning for predicting changes in dynamic constraint satisfaction problems. In Ken Brown Chris Beck and Gérard Verfaillie, editors, Changes'04, International Workshop on Constraint Solving under Change and Uncertainty, Toronto, CA, 2004.
15. Tom M. Mitchell. Machine Learning. McGraw-Hill, 1997.
16. Ramon E. Moore. Interval Analysis. Prentice Hall, 1966.
17. F. Rossi and A. Sperduti. Acquiring both constraint and solution preferences in interactive constraint system. Constraints, 9(4), 2004.
18. RuleQuest Research. See5: An informal tutorial, 2004. http://www.rulequest.com/see5-win.html.
19. D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning internal representations by error propagation. Parallel Distributed Processing, vol 1:318-362, 1986.
20. Neil Yorke-Smith and Carmen Gervet. Certainty closure: A framework for reliable constraint reasoning with uncertainty. In Francesca Rossi, editor, 9th International Conference on Principles and Practice of Constraint Programming, number 2833 in LNCS, pages 769-783, Cork, Ireland, 2003. Springer.