By Lotfi A. Zadeh (auth.), Anca L. Ralescu, James G. Shanahan (eds.)
This quantity constitutes the completely refereed post-workshop complaints of a world workshop on fuzzy good judgment in synthetic Intelligence held in Negoya, Japan in the course of IJCAI '97.
The 17 revised complete papers offered have passed through rounds of reviewing and revision. 3 papers through major experts within the sector are dedicated to the final relevance of fuzzy common sense and fuzzy units to AI. the remainder papers handle a variety of proper concerns starting from idea to software in parts like wisdom illustration, induction, good judgment programming, robotics, development reputation, etc.
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Extra resources for Fuzzy Logic in Artificial Intelligence: IJCAI’97 Workshop Nagoya, Japan, August 23–24, 1997 Selected and Invited Papers
This single modification is all that is required to use probability theory with words defined by fuzzy sets as opposed to numbers or crisp intervals. Our computational algorithms will not change in nature except in this simple modification of counting. All the classical methods will still be available provided we replace the simple counting of allocating a single object to a single class with our modified counting procedure in which the single object will be distributed among several classes. The sum of the proportions we give to the various classes will, of course, sum to 1 for a single object.
Reddish is a fuzzy concept defined on a multidimensional colour space. In the predicate hair_colour(PERSON, COLOUR) COLOUR is an attribute which can be instantiated to values and these values can be words or a discrete fuzzy set on words. Reddish is a discrete fuzzy set on compound words. The words come from a three dimensional colour space. Each axis of this colour space is partitioned into a mutually exclusive and exhaustive set of fuzzy sets and each fuzzy set is a word. The three dimensional colour space is therefore represented by compound words each being a triple of one word from each of the dimensions.
Then, we select the optimum hypothesis based on the traditional statistical criterion. In our previous work, a machine learning approach to extract knowledge for improving the operation of a sintering process in a steel-making plant is 47 presented. In that work, a machine learning method is applied to generate candidates of the parameters that describe operational conditions. Then, domain scientific knowledge is used to select the best parameter set from the viewpoint of consistency with knowledge and practical usefulness.