AUTOMATIC TRAINING EXPERT SYSTEM
https://doi.org/10.57070/2304-4497-2022-4(42)-19-26
Abstract
Group training in general secondary education, secondary and higher specialized educational institutions involves the transfer of knowledge and the development of skills, with an orientation to the "average" according to the abilities of the student. At the same time, achieving such a level of training as "skills" is, in principle, impossible. The level of skills requires, as a rule, individual training with the use of various kinds of teaching and training systems together with the method of iterative learning. The coronavirus pandemic (COVID-19) and the transition to distance learning have intensified the development of information and communication technologies (ICT) for teaching without the presence of a teacher. All this determines the relevance of the development of an automatic training system based on an expert system that ensures, in the absence of a teacher, the achievement of the maximum for each student, depending on his intelligence, the required level of training. In this paper, the structure of an automatic learning system is proposed, which ensures the assimilation of educational material to a given level of learning without the participation of a teacher. Using the example of using such a system for training operators of an arc steelmaking furnace, the work of the system elements is shown: the object simulator model, the trainee error analysis blocks, the synthesis of training information, as well as database and knowledge functions. The issues of obtaining and formalizing the knowledge of expert teachers are considered separately. Examples of the formation of knowledge frames for the training of chipboard steelworkers are given. For example, to teach the process of mixing an electric steelmaking process, 81 frames of information are needed, providing educational comments for all kinds of mistakes of the trainees. Conclusions were drawn based on the results of the work.
About the Authors
Vladimir BuintsevRussian Federation
Cand. Sci. (Eng.), Assist. Profes-sor of the Department of Applied Information Technologies
Inna Rybenko
Russian Federation
Dr. Sci. (Eng.), Associate Professor, Head of the Department of Applied Information Technologies and Programming
Efim Martusevich
Russian Federation
Lecturer of the Department of Applied Information Technologies
Darya Belaventseva
Russian Federation
Postgraduate Student of the Department of Applied Information Technologies
References
1. Kureichik V.V., Sorokoletov P.V., Shcheglov S.N. Analysis of the current state of automated systems for acquiring and presenting knowledge. Izvestiya YuFU. Tekhnicheskie nauki. Tematicheskii vypusk. 2008, pp. 120–124. (In Russ.).
2. Podlasyi I.P. Pedagogy: 100 questions – 100 answers. Moscow: VLADOS-press, 2004. 365 p. (In Russ.).
3. Novikov D.A. Patterns of iterative learning. Moscow: Institut problem upravleniya RAN, 1998, 77 p. (In Russ.).
4. Liora Bresler, David Cooper, Joy Palmer ed. Fifty Modern Thinkers on Education: From Piaget to the Present. 2001. 320 p.
5. Kalashnikov S.N., Buintsev V.N., Martusevich E.A. etc. Features of the use of information expert systems in metallurgy based on intelligent data processing and knowledge. Inzhenernyi vestnik Dona. 2020, no. 1, pp. 1–10. (In Russ.).
6. Borodina N.A., Podgorskaya S.V., Anisimova O.S. Information technologies in education. Persianovskii: Donskoi GAU, 2021, 168 p. (In Russ.).
7. Edward P.K. A Study of the Uses of Expert Systems in the Training. Old Dominion University, 1990. 30 p.
8. Aliev R.A., Abdikeev N.M., Shakhnazarov M.M. Production systems with artificial intelligence. Moscow: Radio i svyaz', 2016, 264 p. (In Russ.).
9. Persianov V.V., Shaidenko N.A. The use of computer technology in the educational process. Moscow: Gostekhizdat, 2017, 112 p. (In Russ.).
10. Gorenskii B.M., Kiryakova O.V., Chentsov S.V., Lapina L.A. Information technologies in the management of technological processes of non-ferrous metallurgy. Krasnoyarsk: Izd-vo «SFU», 2012, 148 p. (In Russ.).
11. Michie Donald. Knowledge-based Systems. University of IL at Urbana-Champaign, Report 80-1001. 1980, 129 p.
12. Stefik M. The Organization of Expert Systems: A Prescriptive Tutorial, XEROX, Palo Alto Research Centers, VLSI-82-1. 1982, 238 p.
13. Feigenbaum E.A. Knowledge Engineering: The Applied Side of Artificial Intelligence. Computer Science Dept., Memo HPP-80-21, Stanford University, 1980.
14. Feigenbaum E.A. Knowledge Engineering for the 1980's. Computer Science Dept., Stanford University. 1982.
15. Buchanan B.G. Research on Expert Systems. Stanford University Computer Science Department, 1981.
16. Quinlin J.R. Discovering Rules by Induction from Large Collections of Examples in the expert environment in the era of microelectronics Age. In: Edinburgh University Press. 1979, pp. 168–201.
17. Hayes-Roth F. AI the New Wave – A Technical Tutorial for R&D Management. Santa Monica, CA: Rand Corp. 1981.
18. Thomas D. McFarland. Reese Parker Expert systems in education and training, Authors Info & Claims. 1990.
19. Buintsev V.N., Rybenko I.A., Martusevich E.A., Belaventseva D.Yu. Automated training system for remote and independent training of operators of complex technological processes. In: Modeling and high-tech information technologies in technical and socio-economic systems. Proceedings of the V International Scientific and Practical Conference, Novokuznetsk, 14 aprelya 2021 goda. Novokuznetsk: ITs SibGIU, 2021, pp. 128–130. (In Russ.).
20. D'yakonov V. P., Borisov A. V. Frame model of knowledge representation. Osnovy iskusstvennogo intellekta. 2007, pp. 31. (In Russ.).
Review
For citations:
Buintsev V., Rybenko I., Martusevich E., Belaventseva D. AUTOMATIC TRAINING EXPERT SYSTEM. Bulletin of the Siberian State Industrial University. 2022;(4):19-26. (In Russ.) https://doi.org/10.57070/2304-4497-2022-4(42)-19-26