Hierarchical Qualitative Inference Model with Substructures

  • Zehua Zhang
  • Duoqian Miao
  • Jin Qian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6954)


Qualitative propagation influences in qualitative inferences are unlike and interrelated on the different hierarchy of knowledge granules, and quantitative information loss easily results in reasoning conflicts. This paper presents a hierarchical qualitative inference model with substructures which to some extent can eliminate the qualitative impact of uncertainty and solve trade-off problems by metastructures with basic decomposition and coarse-grained mesoscale substructures with edge-deletion. The substructural inferences could not only reduce computational complexity, but provide an approximate strategy for modular reasoning on large-scale problems. The example respectively illustrates the two substructural methods are both effective.


qualitative inference substructures hierarchical structure granular computing 


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  1. 1.
    Choi, A., Darwiche, A.: A Variational Approach for Approximating Bayesian networks by edge deletion. In: Proceedings of the 22nd Conf. UAI, pp. 80–89 (2006)Google Scholar
  2. 2.
    Druzdzel, M.J., Henrion, M.: Efficient Reasoning in Qualitative Probabilistic Networks. In: 11st National Conference on AAAI, pp. 548–553 (1993)Google Scholar
  3. 3.
    Feng, Q., Miao, D., Cheng, Y.: Hierarchical decision rules mining. Expert Systems with Applications 37(3), 2081–2091 (2010)CrossRefGoogle Scholar
  4. 4.
    Li, X., Liao, S.: Hierarchical Reasoning in QPNs based on Network Decomposition. In: IEEE International Conference on ICIP, pp. 97–100 (2010)Google Scholar
  5. 5.
    Liu, C.L., Wellman, M.P.: Incremental Trade-off Resolution in Qualitative Probabilistic Networks. In: Proc. of Conf. UAI, pp. 338–345 (1998)Google Scholar
  6. 6.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Palo Alto (1988)zbMATHGoogle Scholar
  7. 7.
    Pedrycz, W.: Hierarchies of Architectures of Collaborative Computational Intelligence. International Journal of Software Science and Computational Intelligence, 18–31 (2009)Google Scholar
  8. 8.
    Renooij, S., van der Gaag, L.C., Parsons, S., Green, S.: Pivotal Pruning of Trade-offs in QPNs. In: Proc. of Conf. UAI, pp. 515–522 (2000)Google Scholar
  9. 9.
    Renooij, S., van der Gaag, L.C., Parsons, S.: Context-specific Sign-propagation in Qualitative Probabilistic Networks. Artificial Intelligence 144(1), 207–230 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Renooij, S., van der Gaag, L.C.: Enhanced qualitative probabilistic networks for resolving trade-offs. Artificial Intelligence 172(12-13), 1470–1494 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Renooij, S.: Bayesian network sensitivity to arc-removal. In: Proceedings of the Fifth European Workshop on Probabilistic Graphical Models, pp. 233–240 (2010)Google Scholar
  12. 12.
    Van Kouwen, F.A., Renooij, S., Schot, P.: Inference in Qualitative Probabilistic Networks revisited. International Journal of Approximate Reasoning 50(5), 708–720 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Wellman, M.P.: Fundamental Concepts of Qualitative Probabilistic Networks. Artificial Intelligence 44, 257–303 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Yao, J.T.: A ten-year review of granular computing. In: Proc. of the IEEE International Conference on Granular Computing, San Jose, USA, pp. 734–739 (2007)Google Scholar
  15. 15.
    Yao, Y.Y.: Integrative Levels of Granularity. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing Through Granular Modeling, pp. 31–47. Springer, Berlin (2009)CrossRefGoogle Scholar
  16. 16.
    Yue, K., Liu, W.: Qualitative probabilistic networks with rough-set-based weights. In: Proc. of ICMLC, vol. 3, pp. 1768–1774 (2008)Google Scholar
  17. 17.
    Yue, K., Liu, W., Yue, M.: Quantifying Influences in the Qualitative Probabilistic Network with Interval Probability Parameters. Applied Soft. Computing 11, 1135–1143 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zehua Zhang
    • 1
    • 2
  • Duoqian Miao
    • 1
  • Jin Qian
    • 1
  1. 1.Department of Computer Science and TechnologyTongji UniversityShanghaiChina
  2. 2.College of Computer Science and TechnologyTaiyuan University of TechnologyShanxiChina

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