Debugging procedure of the replica class errors in the production knowledge bases
DOI: 10.31673/2412-4338.2021.020414
Abstract
The problem of detecting errors in the production knowledge bases of knowledge - oriented information systems that arise at the stage of formation of the knowledge base by experts is considered. It was determined that such errors related to the conflicting opinions of experts and/or limited (imperfect) description of the subject area. There was analyzed approaches for their debugging. Ways to improve existing approaches for debugging static replica errors was shown. Possible ways of applying the obtained solutions to debug errors "contradiction", "redundancy", "incompleteness" was shown. Solutions for expanding the formalized definition of static error of the production network, considering the requirements for the accuracy of information was suggested. The issues of the influence of errors of the “replica” class on the results of derivation according to the rules of the production knowledge base was considered. The possibility of applying the methods of graph theory to solve the problem of error reduction of the "replica" class is proved. An algorithmic structure for detecting and debugging errors of this class has been developed. It allows, in contrast to existing solutions, to detect duplicate vertices at each rank of the graph to which the production knowledge base is reduced. Software implementation for detection and debugging of static errors of incomplete, partial, and complete duplication was developed, using recursion, which allows to reduce the requirements for preparing an array of input data for processing. The obtained solutions meet the requirements of DSTU ISO / IEC 9126, DSTU ISO / IEC 14598 and consider the requirements of the series of standards Software Quality Requirements and Evaluation as the values of the vertices of the graph of the event tree. During problem solving process specifics of the LMS operation are considered, first of all formalizing possibilities in the various aspects of knowledge (aletic, dissociative, causal, diontic) and ensuring a given level of efficiency in finding solutions.
Keywords: graph, model, methodology, production model, knowledge base.
References
1. Burkov V. N., Goubko M., Korgin N. and Novikov D. (2015), Introduction to Theory of Control in Organizations. Boca Raton: CRC Press. 346 p.
2. Lypaev V. (2013), "Reliability and functional safety of real-time software complexes". Moscow. P. 207.
3. Naryniany A. and Yakhno T. (1984), "Production systems. Representation of knowledge in human-machine and robotic systems". Moscow: VC AN USSR, VYNYTY. P. 136–177.
4. Zadeh L.A. (1975), "The concept of a linguistic variable and its application to approximate reasoning". Information Sciences. Vol. 8. P. 199–249, 301–357; Vol. 9. P. 43–80.
5. Tsukamoto Y. (1979), "An approach to fuzzy reasoning method". In: Gupta M.M., Ragade R.K. and Yager R.R. (eds.) Advances in fuzzy set theory and applications. P. 137–149.
6. Miller S.P., Whalen M.W. and Cofer D.D. (2010), "Software model checking takes off". Commun. ACM. № 53(2). Р. 58–64. 7. Mejnarovych J. and Kratko M. (2010), English-Ukrainian dictionary: Mathematics and cybernetics. Кyiv, Perun. 560 p. 8. Kryvulia G., Shkyl A. and Kucherenko D. (2013), "Analysis of the correctness of production rules in fuzzy inference systems using quantum models". ACS and automation devices: vseukr. mezhved. nauch.-tekhn. sb. Kharkiv: Yzd-vo KhNURE, Vol. 165. P. 42–53.
9. Dolynyna O. (2006), Information technology in the management of a modern organization. Saratov, SSTU. 160 p.
10. KES General Description Manual. (1983). Software Architecture and Engineering Inc. Arlington. Р. 33.
11. Suwa H., Scott A.C. and Shortliffe A. (1984), "An Approach to Veryfing Consistency and Completeness in a Rule-Based Expert System". Rule-Based Expert Systems. London: Addison–Wesley. P. 159–170.
12. Nguen T., Perkins W., Laffey T. and Pecora W. (1985), "Checking Expert System Knowledge Bases for consistency and completeness". Proc. оf the 9th Int. Joint Conf. on AI, Los.Ang. P. 375–378.
13. Cragun B.J. and Stendel H.J. (1987), "A decision-table-based processor for checking completeness and consistency in rule-based expert systems". Int. J. Man- Mach. Stud. №5. P. 633–648.
14. Ferrante O., Benvenuti L., Mangeruca L., Sofronis C. and Ferrari A. (2012), "Parallel NuSMV: A NuSMV Extension for the Verification of Complex Embedded Systems"; eds. Ortmeier F., Daniel P. SAFECOMPWorkshops. Vol. 7613. P. 409–416.
15. Knauf R., Gonzalez A.J. and Abel T. (2002), "A framework for validation of rule-based systems". IEEE Trans Syst Man Cybern B Cybern. №32(3). P. 281–295.
16. Godel K. (1986), "Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme, I. and On formally undecidable propositions of Principia Mathematica and related systems I in Solomon Feferman". Kurt Gödel Collected works. Oxford University Press. Р. 144–195.
17. Voitovych S., Lytvynenko M. and Pavlenko M. (2009), "Formalized description of knowledge about the process of selection of sources of fire means of the Air Force." Information processing systems. № 2(76). P. 30–35.
18. Pavlenko M., Medvediev V., Berdnik P. and Safronov R. (2016), "The method of determining the directions of impact of air attack means in the operational direction." Science and technology of the Air Force of Ukraine. № 3(24). P. 24–27.
19. Sedgewick Robert. (1998), "Algorithms in C++. Parts 1-4. Fundamentals, Data Structure, Sorting, Searching". Addison-Weasley, Р. 752.