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Remarks on the generalized probability of the bifuzzy event

100%
Gerstenkorn T., Gerstenkorn J., University of Łódź
Acta Universitatis Lodziensis. Folia Oeconomica
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2008
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vol. 216
EN
The presentation is a continuation of a paper at MSA’04 (T. Gerstenkorn, J. Gerstenkorn (2007)). In 1978 Ph. Smets proposed the so-called g-probability of a fuzzy event as a generalization of the L. Zadeh’s probability of 1968. In 1980 S. Heilpem also discusscd g-probability and analysed its properties. In 1992 Ph. Smets discussed once again the same his own problem and demonstrated its axiomatic properties. In this elaboration we desire to discuss the g-probability of the bifuzzy (intuitionistic) event and its properties as consistent with Kolmogoroff axiomatics.
PL
Niniejsza prezentacja jest kontynuacją pracy pt. Probability of fuzzy event. Review of problems (Prawdopodobieństwo zdarzenia rozmytego. Przegląd zagadnień), przedstawionej na WAS'05 Acta Univ. Lodz., Folia Oeconomica 2007. W 1978 r. Philippe Smets zaproponował tzw. g-prawdopodobieństwo zdarzenia rozmytego jako pewne uogólnienie prawdopodobieństwa tegoż zdarzenia podanego Przez Lotfi Zadeha w 1968 r. W 1980 r. Stanisław Heilpem także rozważał g-prawdopodobieństwo i analizował jego własności. W 1982 r. Ph. Smets ponownie i szeroko rozpatrywał g-prawdopodobieństwo i dowodził jego aksjomatycznych własności. W przedstawianym opracowaniu pragniemy rozpatrzyć g-prawdopodobieństwo zdarzenia dwoistorozmytego (intuicjonistycznego) i jego własności jako zgodne z aksjomatyką Kołmogorowa.
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Diagnostics of the Student’s Learning Style With the Use of Modern Information Technologies

51%
Krišová Z., Pokorný M.
The New Educational Review
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2013
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vol. 34
174-187
EN
The paper deals with learning styles and their initial diagnostics in the process of the student’s learning. It is focused on a method of learning styles recognition with the support of modern information technologies. The paper analyses different methods of the learning styles diagnostics, incorporating this issue into the scientific field of artificial intelligence and presents an idea on how to diagnose a learning style by using an unconventional fuzzy logic linguistic expert system. The expert system was designed to diagnose learning styles of university students in adaptive computer aided learning systems. A significant benefit is continuous numerical evaluation of the student’s degree of affiliation to all learning categories (types of student) with a possibility of simple determination of dominant and subdominant types, the use of a linguistic rule-based decision-making model, which is completely transparent and open, and the use of a decision-making procedure corresponding to the process of human consideration. The paper is an example of an application of modern information technologies in education.
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