Advertisement

Integrating machine-learning techniques in knowledge-based systems verification

  • Hakim Lounis
Learning and Adaptive Systems I
Part of the Lecture Notes in Computer Science book series (LNCS, volume 689)

Abstract

A significant problem in the development of Knowledge-Based Systems (KBS) is its verification step. This paper describes an expert system verification approach that considers system specifications, and consequently, Knowledge Bases (KB) to be partially described when development starts. This partial description is not necessarily perfect and our work aims at using Machine Learning techniques to progressively improve the quality of expert system Knowledge Bases. by coping with two major KB anomalies: incompleteness and incorrectness. In agreement with the current tendency, KBs considered in our approach are expressed in different formalisms. Results obtained with two different learning algorithms, confirm the hypothesis that integrating machine learning techniques in the verification step of a Knowledge-Based System life cycle, is a promising approach.

Keywords

Verification Formal Specifications Machine Learning Revision Process Production Rules Semantic-Net Integrity Constraint 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Laurent: “Vers une terminologie valide pour le domaine de la validation”, actes des JFVAV, pp 1–15, Dourdan, Avril 1992.Google Scholar
  2. 2.
    Quinlan: “Learning Efficient Classification Procedures and their Application to Chess End Games”, in Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G.Carbonell & T.M.Mitchell (Eds.), Morgan Kaufmann 1983, pp 463–482.Google Scholar
  3. 3.
    Michalski: “A Theory and a Methodology of Inductive Learning”, in Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G.Carbonell & T.M.Mitchell (Eds.), Morgan Kaufmann 1983, pp 83–134.Google Scholar
  4. 4.
    Quinlan: “Learning Logical Definitions from Relations”, in Machine Learning Journal, 5, pp 239–266, 1990.Google Scholar
  5. 5.
    Bisson: “Conceptual Clustering in a First-Order Logic Representation”, Proceeding of 10th ECAI, Vienna 1992.Google Scholar
  6. 6.
    Nguyen & al.: “Checking an Expert System Knoweldge Base for consistency and completeness”, IJCAI 1985, pp 375–379.Google Scholar
  7. 7.
    Rousset: “On the Consistency of Knowledge Bases: The COVADIS System”, ECAI 1988, pp 79–84.Google Scholar
  8. 8.
    Loiseau: “Validation, acquisition et mise au point interactive des BC: le système COCO-X fondé sur la cohérence”, thèse de doctorat, univeristé de Paris-Sud, 1990.Google Scholar
  9. 9.
    Ayel: “Détection d'incohérences dans les bases de connaissances: SACCO”, thèse d'état, Chambery, 1987.Google Scholar
  10. 10.
    Politakis & et. al.: “Using Empirical Analysis to Refine E.S Knowledge Bases”, Artificial Intelligence 22, pp 23–48, 1984.Google Scholar
  11. 11.
    Wilkins: “Knowledge base refinement using apprenticeship learning techniques. In Proceedings of the 7th National Conference on Artificial Intelligence, pp 646–651, St. Paul, MN, August 1988.Google Scholar
  12. 12.
    Danyluk: “Finding new rules for incomplete theories: explicit biases for induction with contextual information”. In proceedings of the 6th International Workshop on Machine Learning, pp 34–36, Ithaca, NY, June 1989.Google Scholar
  13. 13.
    Whitehall: “Knowledge-Based Learning: An Integration of Deductive and Inductive Learning for Knowledge Base Completion”, PhD thesis, University of Illinois, Urbana, IL, October 1990.Google Scholar
  14. 14.
    Flann & Dietterich: “A study of explanation-based methods for inductive learning”. Machine Learning, 4 (2), pp 187–226, 1989.Google Scholar
  15. 15.
    Mooney & Ourston: “Induction over the unexplained: Integrated learning of concepts with box explainable and conventional aspects”. In proceedings of the 6th International Workshop on Machine Learning, pp 5–7, Ithaca, NY, June 1989.Google Scholar
  16. 16.
    Cohen: “Learning from textbook knowledge: A case study. In proceedings of the 8th National Conference on Artificial Intelligence, pp 743–748, Boston, MA, July 1990.Google Scholar
  17. 17.
    Mitchell & al.: “EBL: An Unifying View”, ML Journal, vol 1, number 1, Kluwer Academic Publishers, 1986, pp 47–80.Google Scholar
  18. 18.
    Cohen: “Abductive Explanation-Based Learning: A Solution to the multiple Inconsistent Explanation Problem”, in Machine Learning Journal, Vol 8, number 2, March 1992.Google Scholar
  19. 19.
    Rajamoney & DeJong: “The Classification, Detection and Handling of Imperfect Theory Problems”, IJCAI 1987, pp 205–207.Google Scholar
  20. 20.
    Matwin & Plante: “A Deductive-Inductive Method For Theory Revision”, IWML 1991, pp 160–174.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Hakim Lounis
    • 1
  1. 1.Laboratoire de Recherche en InformatiqueUniversité de Paris-SudOrsay CedexFrance

Personalised recommendations