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.
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References
Marcot, B.: Testing your knowledge base. In: AI Expert, August, pp. 43–47 (1987)
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 s
Dolinina, O.N.: Informacionnye tehnologii v upravlenii sovremennoj organizaciej [IT in Organisation Management]. Saratov: SSTU, (2006). 160 s. (in Russian)
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)
Gupta, A., Park, S., Lam, S.: Generalized analytic rule extraction for feedforward neural networks. IEEE Trans. Knowl. Data Eng. 11(6), 965–991 (1999)
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)
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)
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)
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Dolinina, O. (2016). Method of the Debugging of the Knowledge Bases of Intellectual Decision Making Systems. In: Silhavy, R., Senkerik, R., Oplatkova, Z.K., Silhavy, P., Prokopova, Z. (eds) Automation Control Theory Perspectives in Intelligent Systems. CSOC 2016. Advances in Intelligent Systems and Computing, vol 466. Springer, Cham. https://doi.org/10.1007/978-3-319-33389-2_29
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DOI: https://doi.org/10.1007/978-3-319-33389-2_29
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