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Cluster-Specific Heuristics for Constraint Solving

  • Seda Polat ErdenizEmail author
  • Alexander Felfernig
  • Muesluem AtasEmail author
  • Thi Ngoc Trang Tran
  • Michael Jeran
  • Martin Stettinger
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10350)

Abstract

In Constraint Satisfaction Problems (CSP), variable ordering heuristics help to increase efficiency. Applying an appropriate heuristic can increase the performance of CSP solvers. On the other hand, if we apply specific heuristics for similar CSPs, CSP solver performance could be further improved. Similar CSPs can be grouped into same clusters. For each cluster, appropriate heuristics can be found by applying a local search. Thus, when a new CSP is created, the corresponding cluster can be found and the pre-calculated heuristics for the cluster can be applied. In this paper, we propose a new method for constraint solving which is called Cluster Specific Heuristic (CSH). We present and evaluate our method on the basis of example CSPs.

Keywords

Configuration Constraint satisfaction problems Variable and value ordering heuristics Clustering Performance optimization 
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References

  1. 1.
    Abeel, T., de Peer, Y., Saeys, Y.: Java-ML: a machine learning library. J. Mach. Learn. Res. 10, 931–934 (2009)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Balduccini, M.: Learning and using domain-specific heuristics in ASP solvers. AI Commun. 24(2), 147–164 (2011)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Benavides, D., Felfernig, A., Galindo, J.A., Reinfrank, F.: Automated analysis in feature modelling and product configuration. In: Favaro, J., Morisio, M. (eds.) ICSR 2013. LNCS, vol. 7925, pp. 160–175. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38977-1_11 CrossRefGoogle Scholar
  4. 4.
    Di Ciccio, C., Maggi, F.M., Montali, M., Mendling, J.: Resolving inconsistencies and redundancies in declarative process models. Inf. Sys. 64, 425–446 (2017)CrossRefGoogle Scholar
  5. 5.
    Epstein, S.L., Wallace, R.J.: Finding crucial subproblems to focus global search. In: 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), pp. 151–162 (2006)Google Scholar
  6. 6.
    Falkner, A., Felfernig, A., Haag, A.: Recommendation technologies for configurable products. AI Mag. 32(3), 99–108 (2011)Google Scholar
  7. 7.
    Felfernig, A., Hotz, L., Bagley, C., Tiihonen, J.: Knowledge-Based Configuration: From Research to Business Cases, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2014)Google Scholar
  8. 8.
    Felfernig, A., Schubert, M., Zehentner, C.: An efficient diagnosis algorithm for inconsistent constraint sets. Artif. Intell. Eng. Des. Anal. Manufact. (AIEDAM) 26(1), 53–62 (2012)CrossRefGoogle Scholar
  9. 9.
    Jannach, D.: Toward automatically learned search heuristics for CSP-encoded configuration problems - results from an initial experimental analysis. In: Proceedings of the 15th International Configuration Workshop, Vienna, Austria, pp. 9–13, 29–30 August 2013Google Scholar
  10. 10.
    Jin, X., Han, J.: K-means clustering. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 563–564. Springer, Boston (2010)Google Scholar
  11. 11.
    Jussien, N., Rochart, G., Lorca, X.: Choco: an open source Java constraint programming library. In: CPAIOR 2008 Workshop on Open-Source Software for Integer and Contraint Programming (OSSICP 2008), Paris, France, pp. 1–10 (2008)Google Scholar
  12. 12.
    Li, X., Epstein, S.L.: Learning cluster-based structure to solve constraint satisfaction problems. Ann. Math. AI 60(1–2), 91–117 (2010)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Liu, Y., Jiang, Y., Qian, H.: Topology-based variable ordering strategy for solving disjunctive temporal problems. In: 15th International Symposium on Temporal Representation and Reasoning, pp. 129–136. IEEE (2008)Google Scholar
  14. 14.
    Man, K.F., Tang, K.S., Kwong, S.: Genetic algorithms: concepts and applications. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)CrossRefGoogle Scholar
  15. 15.
    Merhej, E., Schockaert, S., De Cock, M.: Repairing inconsistent answer set programs using rules of thumb: a gene regulatory networks case study. Int. J. Approximate Reasoning 83, 243–264 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    O’Sullivan, B., Ferguson, A., Freuder, E.C.: Boosting constraint satisfaction using decision trees. In: 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), pp. 646–651 (2004)Google Scholar
  17. 17.
    Prud’homme, C., Fages, J.G., Lorca, X.: Choco Solver Documentation (2017)Google Scholar
  18. 18.
    Sadeh, N., Fox, M.S.: Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem. AI J. 86(1), 1–41 (1996)Google Scholar
  19. 19.
    Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London (1993)Google Scholar
  20. 20.
    Walter, R., Felfernig, A., Küchlin, W.: Constraint-based and SAT-based diagnosis of automotive configuration problems. J. Intell. Inf. Syst. (JIIS), 1–32 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Seda Polat Erdeniz
    • 1
    Email author
  • Alexander Felfernig
    • 1
  • Muesluem Atas
    • 1
    Email author
  • Thi Ngoc Trang Tran
    • 1
  • Michael Jeran
    • 1
  • Martin Stettinger
    • 1
  1. 1.Institute of Software TechnologyGraz University of TechnologyGrazAustria

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