Advertisement

Method of the Debugging of the Knowledge Bases of Intellectual Decision Making Systems

  • Olga Dolinina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)

Abstract

The paper describes the method of the debugging of the intellectual decision making systems. It combines the detecting of the structural errors and the testing of the knowledge base. Static analysis allows to detect so called structural errors such as incomplete knowledge, inconsistency, extra rules. Static debugging allows to build the static correct knowledge base. But even the static correct knowledge base can have errors connected with the inconsistency of the subject area which can be detected with the dynamic debugging (testing). The paper shows that the most difficult for the detection is the “forgetting about the exception” type of the errors. There is described the method of the generation of the full test set which allows to detect such types of the errors in the knowledge base. The method is based on the building of the tests for the logic schemes. The method was successfully approved for the testing of the rule-based expert systems and for the artificial network based on the 3-level perceptron.

Keywords

Debugging of the intellectual decision making systems Static analysis Testing Generation of the full test set Rule-based systems 

References

  1. 1.
    Marcot, B.: Testing your knowledge base. In: AI Expert, August, pp. 43–47 (1987)Google Scholar
  2. 2.
    Dolinina, O.N.: Razrabotka metoda testirovanija produkcionnyh baz znanij jekspertnyh sistem s uchetom oshibok tipa “zabyvanija ob iskljuchenii”: dis…kand. tehn. nauk. Saratov (1999). 171 sGoogle Scholar
  3. 3.
    Dolinina, O.N.: Informacionnye tehnologii v upravlenii sovremennoj organizaciej [IT in Organisation Management]. Saratov: SSTU, (2006). 160 s. (in Russian)Google Scholar
  4. 4.
    Goel, P., Rosales, B.: PODEM—X: an automatic test generation system for VLSI logic structures. In: Proceedings of 18th IEEE Design Automation Conference, pp. 260–268. IEEE Press Piscataway, NJ, USA. (1981)Google Scholar
  5. 5.
    Gupta, A., Park, S., Lam, S.: Generalized analytic rule extraction for feedforward neural networks. IEEE Trans. Knowl. Data Eng. 11(6), 965–991 (1999)CrossRefGoogle Scholar
  6. 6.
    Dolinina, O.N.: Otladka iskusstvennoj nejroseti, osnovannoj na trjohslojnom perseptrone, na primere jekspertnoj sistemy dlja oftalmologii [Debugging artificial neural network based on the 3-level perceptron: a study of the expert system in ophthalmology]/Kuzmin, A.K., Dolinina, O.N.//Vestnik Astrahanskogo gosudarstvennogo tehnicheskogo universiteta. Ser.: Upravlenie, vychislitel’naja tehnika i informatika. 80–90 (2011). (in Russian)Google Scholar
  7. 7.
    Dolinina, O.N.: Otladka nejrosetevoj ekspertnoj sistemy dlja oftalmologii [Debugging neural network expert system for ophthalmology]/Dolinina, O.N., Kuzmin, A.K.//Vestnik Saratovskogo gosudarstvennogo tehnicheskogo universiteta. 4(62). V. 4. S. 248–253(2011). (in Russian)Google Scholar
  8. 8.
    Dolinina, O.N., Antropov, P.G., Kuzmin, A.K., Shvarts, A.Ju.: Ispolzovanie intellektualnyh sistem dlja diagnostiki neispravnostej gazoperekachivajushhih agregatov [Using intellectual systems for compressors troubleshooting ]//Sovremennye problemy nauki i obrazovanija 6 (2013). http://www.science-education.ru/113-11252 (data obrashhenija: 23.12.2013). (in Russian)

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Yury Gagarin State Technical University of SaratovSaratovRussia

Personalised recommendations