Doctoral Thesis / Dissertation from the year 2010 in the subject Computer Sciences - Artificial Intelligence, grade: PhD, Korea University, Seoul (College of Engineering - Dept of Industrial Systems and Information Engineering), course: Intelligence Control and Artificial Intelligence, language: English, abstract: Fuzzy Logic (FL) is a particular area of interest in the study of Artificial intelligence(AI) based on the idea that in fuzzy sets each element in the set can assume a value from0 to 1, not just 0 or 1, as in classic or crisp set theory. The gradation in the extent towhich an element is belonging to the relevant sets is called the degree of membership. This degree of membership is a measure of the element s belonging to the set, and thus ofthe precision with which it explains the phenomenon being evaluated. A linguisticexpression is given to each fuzzy set. The information contents of the fuzzy rules are thenused to infer the output using a suitable inference engine. The key contribution of fuzzylogic in computation of information described in natural language made it applicable to avariety of applications and problem domains; from simple control systems to humandecision support systems. Yet, despite its long-standing origins, it is a relatively new field,and as such leaves much room for development. The thesis presents two novel applications of fuzzy systems; a human decisionsupport system to help teachers to fairly evaluate students and two hybrid intelligentfuzzy systems; a type-2 fuzzy logic system and a combined type-1 fuzzy logic system andextended Kalamn filter for controlling systems operating under high levels ofuncertainties due to various sources of measurement and modeling errors. The combination of fuzzy logic and the classical student evaluation approachproduces easy to understand transparent decision model that can be easily understood bystudents and teachers alike. The developed architecture overcomes the problem ofranking students with the same score. It also incorporated different dimensions ofevaluation by considering subjective factors such as difficulty, complexity andimportance of the questions. Although we discuss this approach with an example fromthe area of student evaluation, this method evidently has wide applications in other areasof decision making including student s project evaluation, learning management systemsevaluation, as well as, other assessment applications. [...]

Doctoral Thesis / Dissertation from the year 2010 in the subject Computer Sciences - Artificial Intelligence, grade: PhD, Korea University, Seoul (College of Engineering - Dept of Industrial Systems and Information Engineering), course: Intelligence Control and Artificial Intelligence, language: English, abstract: Fuzzy Logic (FL) is a particular area of interest in the study of Artificial intelligence(AI) based on the idea that in fuzzy sets each element in the set can assume a value from0 to 1, not just 0 or 1, as in classic or crisp set theory. The gradation in the extent towhich an element is belonging to the relevant sets is called the degree of membership. This degree of membership is a measure of the element s belonging to the set, and thus ofthe precision with which it explains the phenomenon being evaluated. A linguisticexpression is given to each fuzzy set. The information contents of the fuzzy rules are thenused to infer the output using a suitable inference engine. The key contribution of fuzzylogic in computation of information described in natural language made it applicable to avariety of applications and problem domains; from simple control systems to humandecision support systems. Yet, despite its long-standing origins, it is a relatively new field,and as such leaves much room for development. The thesis presents two novel applications of fuzzy systems; a human decisionsupport system to help teachers to fairly evaluate students and two hybrid intelligentfuzzy systems; a type-2 fuzzy logic system and a combined type-1 fuzzy logic system andextended Kalamn filter for controlling systems operating under high levels ofuncertainties due to various sources of measurement and modeling errors. The combination of fuzzy logic and the classical student evaluation approachproduces easy to understand transparent decision model that can be easily understood bystudents and teachers alike. The developed architecture overcomes the problem ofranking students with the same score. It also incorporated different dimensions ofevaluation by considering subjective factors such as difficulty, complexity andimportance of the questions. Although we discuss this approach with an example fromthe area of student evaluation, this method evidently has wide applications in other areasof decision making including student s project evaluation, learning management systemsevaluation, as well as, other assessment applications. [...]