Efficient Rule Base Verification Using Binary Decision Diagrams

  • Christophe Mues
  • Jan Vanthienen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3180)


As their field of application has evolved and matured, the importance of verifying knowledge-based systems is now widely recognized. Nevertheless, some problems have remained. In this paper, we address the poor scalability to larger systems of the computation methods commonly applied to rule-chain anomaly checking. To tackle this problem, we introduce a novel anomaly checking method based on binary decision diagrams (BDDs), a technique emanating mainly from the hardware design community. In addition, we present empirical evidence of its computational efficiency, especially on rule bases with a deeper inference space.


Boolean Function Rule Base Binary Decision Diagram Label Size Rule Instance 
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  1. 1.
    Bryant, R.E.: Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Computers C-35(8), 677–691 (1986)CrossRefGoogle Scholar
  2. 2.
    Ginsberg, A.: Knowledge-base reduction: a new approach to checking knowledge bases for inconsistency and redundancy. In: Proc. of the 7th National Conf. on Artificial Intelligence (AAAI 1988), pp. 585–589 (1988)Google Scholar
  3. 3.
    Horiyama, T., Ibaraki, T.: Ordered binary decision diagrams as knowledge-bases. Artificial Intelligence 136(2), 189–213 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Levy, A., Rousset, M.-C.: Verification of knowledge bases based on containment checking. Artificial Intelligence 101(1-2), 227–250 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Mues, C.: On the Use of Decision Tables and Diagrams in Knowledge Modeling and Verification. PhD thesis, K.U.Leuven, Dept. of Applied Econ. Sciences (2002)Google Scholar
  6. 6.
    Preece, A., Shinghal, R.: Foundation and application of knowledge base verification. Intl. Journal of Intelligent Systems 9(8), 683–701 (1994)CrossRefGoogle Scholar
  7. 7.
    Preece, A., Shinghal, R., Batarekh, A.: Principles and practice in verifying rule-based systems. The Knowledge Engineering Review 7(2), 115–141 (1992)CrossRefGoogle Scholar
  8. 8.
    Rousset, M.-C.: On the consistency of knowledge bases: the Covadis system. Computational Intelligence 4, 166–170 (1988)CrossRefGoogle Scholar
  9. 9.
    Tsai, W.-T., Vishnuvajjala, R., Zhang, D.: Verification and validation of knowledge-based systems. IEEE Transactions on Knowledge and Data Engineering 11(1), 202–211 (1999)CrossRefGoogle Scholar
  10. 10.
    Vanthienen, J., Mues, C., Aerts, A.: An illustration of verification and validation in the modelling phase of KBS development. Data and Knowledge Engineering 27(3), 337–352 (1998)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Christophe Mues
    • 1
    • 2
  • Jan Vanthienen
    • 1
  1. 1.K.U.LeuvenLeuvenBelgium
  2. 2.School of ManagementUniversity of SouthamptonSouthamptonUnited Kingdom

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