No abstract

How is fuzzy logic usually formalized? There are many seemingly reasonable requirements that a logic should satisfy: e.g., since A&B and B&A are the same, the corresponding and-operation should be commutative. Similarly, since A&A means the same as A, we should expect that the and-operation should also satisfy this property, etc. It turns out to be impossible to satisfy all these seemingly natural requirements, so usually, some requirements are picked as absolutely true (like commutativity or associativity), and others are ignored if they contradict to the picked ones. This idea leads to a neat mathematical theory, but the analysis of real-life expert reasoning shows that all the requirements are only approximately satisfied. we should require all of these requirements to be satisfied to some extent. In this paper, we show the preliminary results of analyzing such operations. In particular, we show that non-associative operations explain the empirical 7 AE 2 law in psychology according to which a person can normally distinguish between no more than 7 plus minus 2 classes.

How is fuzzy logic usually formalized? There are many seemingly reasonable requirements that a logic should satisfy: e.g., since ¢ ¡ ¤ £ and £ ¥ ¡ ¤ are the same, the corresponding and-operation should be commutative. Similarly, since ¦ ¡ ¤ means the same as , we should expect that the and-operation should also satisfy this property, etc. It turns out to be impossible to satisfy all these seemingly natural requirements, so usually, some requirements are picked as absolutely true (like commutativity or associativity), and others are ignored if they contradict to the picked ones. This idea leads to a neat mathematical theory, but the analysis of real-life expert reasoning shows that all the requirements are only approximately satisfied. we should require all of these requirements to be satisfied to some extent. In this paper, we show the preliminary results of analyzing such operations. In particular, we show that non-associative operations explain the empirical § © law in psychology according to which a person can normally distinguish between no more than 7 plus minus 2 classes.

This paper presents a unique point-of-view for program visualization, namely, the use of scientific visualization techniques for program visualization. This paper is exploratory in nature. Its primary contribution is to re-examine program visualization from a scientific visualization point-of-view. This paper reveals that specific visualization techniques such as animation, isolines, program slicing, dimensional reduction, glyphs and color maps may be considered for program visualization. In addition, some features of AVS/Express that may be used for program visualization are discussed. Lastly, comments regarding emotional color spaces are made.