Methods for Assessing Human–Machine Performance under Fuzzy Conditions
AbstractThe assessment of a system’s performance is a very important task, enabling its designer/user to correct its weaknesses and make it more effective. Frequently, in practice, a system’s assessment is performed under fuzzy conditions, e.g., using qualitative instead of numerical grades, incomplete information about its function, etc. The present review summarizes the author’s research on building assessment models for use in a fuzzy environment. Those models include the measurement of a fuzzy system’s uncertainty, the application of the center of gravity defuzzification technique, the use of triangular fuzzy or grey numbers as assessment tools, and the application of the fuzzy relation equations. Examples are provided of assessing human (students and athletes) and machine (case-based reasoning systems in computers) capacities, illustrating our results. The outcomes of those examples are compared to the outcomes of the traditional methods of calculating the mean value of scores assigned to the system’s components (system’s mean performance) and of the grade point average index (quality performance) and useful conclusions are obtained concerning their advantages and disadvantages. The present review forms a new basis for further research on systems’ assessment in a fuzzy environment. View Full-Text
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Voskoglou, M.G. Methods for Assessing Human–Machine Performance under Fuzzy Conditions. Mathematics 2019, 7, 230.
Voskoglou MG. Methods for Assessing Human–Machine Performance under Fuzzy Conditions. Mathematics. 2019; 7(3):230.Chicago/Turabian Style
Voskoglou, Michael G. 2019. "Methods for Assessing Human–Machine Performance under Fuzzy Conditions." Mathematics 7, no. 3: 230.
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