Decision Making and Control of a Cognitive Agent’s Knowledge Process Under Time Constraints

Fominykh, Igor; Eremeev, Alexander; Alekseev, Nikolay; Gulyakina, N. A.

doi:10.1007/978-3-030-60447-9_13

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Neural networks, genetic algorithms, and fuzzy systems as applied to the assessment of knowledge and the development of learning systems were studied by Averkin and Yarushev (2020), Binh and Duy (2017), Brusilovsky, Somyürek, Guerra, Hosseini, Zadorozhny, and Durlach (2016), Fominykh, Eremeev, Alekseev, and Gulyakina (2020), Hadzhikolev, Yotov, Trankov, and Hadzhikoleva (2019, Crockett, Latham, and Whitton (2016), Chaira (2019), Kasabov (2019), Kolesnikov (2017), Kuchinskaya-Parova (2013), Kuchinskaya-Parovaya and Gulyakina (2013), Rutkovskaya, Pilinsky, and Rutkovsky (2013), Lavrov, Barchenok, Lavrova, and Savina (2019), Lisov and Bain (2012), Pokalitsina (2006), Gaaze-Rapoport and Pospelov (2011), Popov and Lazareva (2015), Dubois and Prade (2012), Petrushin (1993), Saxena, Shinghal, Misra, and Agarwal (2019), Tuktarova, Kamalova, and Daukaeva (2014), and others. The researchers have found that the accuracy and objectivity of assessment of learning outcomes depend on the dynamics and operational efficiency of use of diagnostic tools, the integrity of accounting and the nature of changes in parameters and indicators, the validity of quality criteria for the individual's mastering the content of the subject area of knowledge and the state of personal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Parameters and Structure of Neural Network Databases for Assessment of Learning Outcomes

Smirnov

Dvoryatkina

Shcherbatykh

2022

Int. J. Criminol. Sociol.

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The purpose of this study is to determine the methodology, develop a theory of construction, put into practice algorithmization and implement the functionality of a hybrid intelligent system for assessment of educational outcomes of trainees on the basis of the identified keyword parameters and structure of the artificial neural network using expert systems and fuzzy simulation; to develop a methodology for the construction of structural-logic, hierarchical, functional and fractal schemes for structuring databases of the didactic field of learning elements; to determine the content, structure of parameters and database components, selection criteria and the content of complexes of educational standards. The methodology of introducing intelligent systems into mathematical education is on the basis of the Hegelian triad: thesis (implementation of the coherence principle) – antithesis (implementation of principles of the fractality and historiogenesis) – synthesis (implementation of the principles of self-organization and reflection of the complex system inversion integrity). Requirements for the organization and construction of the artificial neural network for assessment of personal achievements on the basis of fuzzy simulation have been developed. In the direction of using elements of fractal geometry, the technological structures of clusters that constitute the basis of generalized structures have been developed. In particular, it is revealed that the didactic field of learning elements is equipped with a system of multi-level hierarchical databases of exercises, motivational-applied, research, practice-oriented tasks using expert systems and integration of mathematical, information, natural-science and humanities knowledge and procedures.

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