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International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming

CPAIOR 2005: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems pp 94–109Cite as

Identifying and Exploiting Problem Structures Using Explanation-Based Constraint Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3524))

Abstract

Recent work have exhibited specific structure among combinatorial problem instances that could be used to speed up search or to help users understand the dynamic and static intimate structure of the problem being solved. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure identified within a given problem. The next step is to design algorithms that adaptatively integrate that kind of information during search. We claim in this paper, inspired by previous work on impact-based search strategies for constraint programming, that using an explanation-based constraint solver may lead to collect invaluable information on the intimate dynamic and static structure of a problem instance. We define several impact graphs to be used to design generic search guiding techniques and to identify hidden structures of instances. Finally, we discuss how dedicated OR solving strategies (such as Benders decomposition) could be adapted to constraint programming when specific relationships between variables are exhibited.

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References

  1. Achlioptas, D., Kirousis, L., Kranakis, E., Krizanc, D., Molloy, M., Stamatiou, Y.: Random constraint satisfaction: a more accurate picture. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 121–135. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  2. Benders, J.F.: Partitionning procedures for solving mixed-variables programming problems. Numerische Mathematik 4, 238–252 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  3. Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: Proceedings ECAI 2004, pp. 482–486 (2004)

    Google Scholar 

  4. Cambazard, H., Hladik, P.-E., Déplanche, A.-M., Jussien, N., Trinquet, Y.: Decomposition and learning for a real time task allocation problem. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 153–167. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Cleuziou, G., Martin, L., Vrain, C.: Disjunctive learning with a soft-clustering method. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 75–92. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  6. Ghoniem, M., Jussien, N., Fekete, J.-D.: VISEXP: visualizing constraint solver dynamics using explanations. In: Proceedings FLAIRS 2004, Miami, Florida, USA (May 2004)

    Google Scholar 

  7. Gomes, C.P., Selman, B., Crato, N.: Heavy-tailed distributions in combinatorial search. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 121–135. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  8. Haralick, R., Elliot, G.: Increasing tree search efficiency for constraint satisfaction problems. Artificial intelligence 14(9), 263–313 (1980)

    Article  Google Scholar 

  9. Hooker, J.N., Ottosson, G.: Logic-based benders decomposition. Mathematical Programming 96, 33–60 (2003)

    MATH  MathSciNet  Google Scholar 

  10. Jain, V., Grossmann, I.E.: Algorithms for hybrid milp/cp models for a class of optimization problems. INFORMS Journal on Computing 13, 258–276 (2001)

    Article  MathSciNet  Google Scholar 

  11. Jussien, N.: The versatility of using explanations within constraint programming. Habilitation thesis, Université de Nantes, France (2003); also available as RR-03-04 research report at École des Mines de Nantes

    Google Scholar 

  12. Jussien, N., Barichard, V.: The PaLM system: explanation-based constraint programming. In: Proceedings of TRICS: Techniques foR Implementing Constraint programming Systems, a post-conference workshop of CP 2000, pp. 118–133 (2000)

    Google Scholar 

  13. Jussien, N., Debruyne, R., Boizumault, P.: Maintaining arc-consistency within dynamic backtracking. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 249–261. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  14. Jussien, N., Lhomme, O.: Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence 139(1), 21–45 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  15. Prosser, P., Stergiou, K., Walsh, T.: Singleton consistencies. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 353–368. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  16. Refalo, P.: Impact-based search strategies for constraint programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  17. Williams, R., Gomes, C., Selman, B.: On the connections between backdoors and heavy-tails on combinatorial search. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  18. Williams, R., Gomes, C.P., Selman, B.: Backdoors to typical case complexity. In: Proceedings IJCAI 2003 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. École des Mines de Nantes, LINA CNRS FRE 2729, 4 rue Alfred Kastler, BP 20722, F-44307, Nantes Cedex 3, France

    Hadrien Cambazard & Narendra Jussien

Authors
  1. Hadrien Cambazard
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  2. Narendra Jussien
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Editor information

Editors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 2/25, 118 00, Praha 1, Czech Republic

    Roman Barták

  2. DEIS, University of Bologna, V.le Risorgimento 2, 40136, Bologna, Italy

    Michela Milano

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Cambazard, H., Jussien, N. (2005). Identifying and Exploiting Problem Structures Using Explanation-Based Constraint Programming. In: Barták, R., Milano, M. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2005. Lecture Notes in Computer Science, vol 3524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11493853_9

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  • DOI: https://doi.org/10.1007/11493853_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26152-0

  • Online ISBN: 978-3-540-32264-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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