Add to ORKGKnowledge acquisition under uncertainty is examined. Theories proposed in deKorvin's paper 'Extracting Fuzzy Rules Under Uncertainty and Measuring Definability Using Rough Sets' are discussed as they relate to rule calculation algorithms. A data structure for holding an arbitrary number of data fields is described. Limitations of Pascal for loops in the generation of combinations are also discussed. Finally, recursive algorithms for generating all possible combination of attributes and for calculating the intersection of an arbitrary number of fuzzy sets are presented
AbstractThe use of fuzzy decision tables as a programming language for representing both the knowled...
Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic so...
[EN]Under uncertainty, traditional sets may not be sufficient to represent real-world phenomena, and...
Despite the advancements in the computer industry in the past 30 years, there is still one major def...
Although computers have come a long way since their invention, they are basically able to handle onl...
Intelligent control is a very successful way to transform the expert's knowledge of the type 'if the...
In the present work, we consider the general problem of knowledge acquisition under uncertainty. A c...
In this work we present a model for handling uncertain information. The concept of fuzzy knowledge-b...
The Dempster-Shafer theory of evidence is applied to a multiattribute decision making problem whereb...
The purpose of this research was to examine the potential of the rough sets technique for developing...
AbstractThe methodology of fuzzy reasoning has been shown to be very useful technology for modeling ...
Much of the research done in Artificial Intelligence involves investigating and developing methods o...
AbstractThe use of support pairs associated with the facts and rules of a knowledge base of an exper...
The notion of uncertainty in expert systems is dealing with vague data, incomplete information, and ...
The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that...
AbstractThe use of fuzzy decision tables as a programming language for representing both the knowled...
Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic so...
[EN]Under uncertainty, traditional sets may not be sufficient to represent real-world phenomena, and...
Despite the advancements in the computer industry in the past 30 years, there is still one major def...
Although computers have come a long way since their invention, they are basically able to handle onl...
Intelligent control is a very successful way to transform the expert's knowledge of the type 'if the...
In the present work, we consider the general problem of knowledge acquisition under uncertainty. A c...
In this work we present a model for handling uncertain information. The concept of fuzzy knowledge-b...
The Dempster-Shafer theory of evidence is applied to a multiattribute decision making problem whereb...
The purpose of this research was to examine the potential of the rough sets technique for developing...
AbstractThe methodology of fuzzy reasoning has been shown to be very useful technology for modeling ...
Much of the research done in Artificial Intelligence involves investigating and developing methods o...
AbstractThe use of support pairs associated with the facts and rules of a knowledge base of an exper...
The notion of uncertainty in expert systems is dealing with vague data, incomplete information, and ...
The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that...
AbstractThe use of fuzzy decision tables as a programming language for representing both the knowled...
Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic so...
[EN]Under uncertainty, traditional sets may not be sufficient to represent real-world phenomena, and...