Towards Leveraging Backdoors in Qualitative Constraint Networks

  • Michael SioutisEmail author
  • Tomi Janhunen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11793)


In this short paper we introduce the notions of backbones and backdoors in the context of qualitative constraint networks. As motivation for the study of those structures, we argue that they can be used to define collaborative approaches among SAT, CP, and native tools, inspire novel decomposition and parallelization techniques, and lead to the development of adaptive constraint propagators with a better insight into the particularities of real-world datasets than what is possible today.


Qualitative constraints Spatio-temporal reasoning Local consistencies Backdoors Backbones 


  1. 1.
    Alirezaie, M., Längkvist, M., Sioutis, M., Loutfi, A.: Semantic referee: a neural-symbolic framework for enhancing geospatial semantic segmentation. Semant. Web (2019, in press)Google Scholar
  2. 2.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26, 832–843 (1983)CrossRefGoogle Scholar
  3. 3.
    Amaneddine, N., Condotta, J.F., Sioutis, M.: Efficient approach to solve the minimal labeling problem of temporal and spatial qualitative constraints. In: IJCAI (2013)Google Scholar
  4. 4.
    Bhatt, M., Wallgrün, J.O.: Geospatial narratives and their spatio-temporal dynamics: commonsense reasoning for high-level analyses in geographic information systems. ISPRS Int. J. Geo-Information 3, 166–205 (2014)CrossRefGoogle Scholar
  5. 5.
    Condotta, J.F., Lecoutre, C.: A class of \(^\diamond _f\)-consistencies for qualitative constraint networks. In: KR (2010)Google Scholar
  6. 6.
    Condotta, J.-F., Ligozat, G., Saade, M.: Eligible and frozen constraints for solving temporal qualitative constraint networks. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 806–814. Springer, Heidelberg (2007). Scholar
  7. 7.
    Dylla, F., et al.: A survey of qualitative spatial and temporal calculi: algebraic and computational properties. ACM Comput. Surv. 50, 7:1–7:39 (2017)CrossRefGoogle Scholar
  8. 8.
    Dylla, F., Wallgrün, J.O.: Qualitative spatial reasoning with conceptual neighborhoods for agent control. J. Intell. Robotic Syst. 48, 55–78 (2007)CrossRefGoogle Scholar
  9. 9.
    Glorian, G., Lagniez, J.-M., Montmirail, V., Sioutis, M.: An incremental SAT-based approach to reason efficiently on qualitative constraint networks. In: Hooker, J. (ed.) CP 2018. LNCS, vol. 11008, pp. 160–178. Springer, Cham (2018). Scholar
  10. 10.
    Huang, J., Li, J.J., Renz, J.: Decomposition and tractability in qualitative spatial and temporal reasoning. Artif. Intell. 195, 140–164 (2013)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Krishnaswamy, N., Friedman, S., Pustejovsky, J.: Combining deep learning and qualitative spatial reasoning to learn complex structures from sparse examples with noise. In: AAAI (2019)Google Scholar
  12. 12.
    Ligozat, G.: Qualitative Spatial and Temporal Reasoning. Wiley, Hoboken (2013)CrossRefGoogle Scholar
  13. 13.
    Ligozat, G., Renz, J.: What is a qualitative calculus? A general framework. In: PRICAI (2004)CrossRefGoogle Scholar
  14. 14.
    Long, Z., Sioutis, M., Li, S.: Efficient path consistency algorithm for large qualitative constraint networks. In: IJCAI (2016)Google Scholar
  15. 15.
    Martins, R., Manquinho, V.M., Lynce, I.: An overview of parallel SAT solving. Constraints 17, 304–347 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Nebel, B.: Solving hard qualitative temporal reasoning problems: evaluating the efficiency of using the ORD-horn class. Constraints 1, 175–190 (1997)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Renz, J., Ligozat, G.: Weak composition for qualitative spatial and temporal reasoning. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 534–548. Springer, Heidelberg (2005). Scholar
  18. 18.
    Sioutis, M., Condotta, J., Koubarakis, M.: An efficient approach for tackling large real world qualitative spatial networks. Int. J. Artif. Intell. Tools 25, 1–33 (2016)CrossRefGoogle Scholar
  19. 19.
    Sioutis, M., Long, Z., Li, S.: Leveraging variable elimination for efficiently reasoning about qualitative constraints. Int. J. Artif. Intell. Tools 27, 1860001 (2018)CrossRefGoogle Scholar
  20. 20.
    Sioutis, M., Paparrizou, A., Condotta, J.: Collective singleton-based consistency for qualitative constraint networks: theory and practice. Theor. Comput. Sci. (2019, in press)Google Scholar
  21. 21.
    Williams, R., Gomes, C.P., Selman, B.: Backdoors to typical case complexity. In: IJCAI (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceAalto UniversityEspooFinland
  2. 2.Tampere UniversityTampereFinland

Personalised recommendations