(hmmm, I'm seeing responses to posts by Thomas, but not his posts themselves) Stephan.Lehmke@cs.uni-dortmund.de (Stephan Lehmke) writes:> In article <3B674E1D.F47314AF@verizon.net>, S. F. Thomas writes: > > * Furthermore, according to Zadeh, Fuzzyness should be used to > `compress' knowledge representation, in terms of the number of > concepts, rules, ... used. So I expect to be able to evaluate the > color of an orange entirely by comparing its membership degree in the > fuzzy sets of red, green, and blue objects, without having to employ a > plethora of auxilliary concepts like "reddish" , "somewhat red", > "orange red", etc. pp.
Right -- it seems like ``reddish orange'' ought to mean a high membership in orange, and a non-zero membership in red.>>> For empirically finding a membership degree, I'd rather have people >>> mark the `degree of tallness' on a continuous scale between `tall' and >>> `not tall at all' and take the average. >> >> I don't like it, because your subjects still have to be >> told what it is you mean by "degree of tallness". Which is a kind of >> circular question-begging in my opinion.
Actually, it seems to me that your use of calibrational propositions answers this quite nicely. If you ask, ``on a scale of 0 to 10, to what extent would you say Tom is tall'' then what you get back as an average is a notion of how people assign the membership function, without ever having to produce an actual definition of tallness for them to use. -- Joseph J. Pfeiffer, Jr., Ph.D. Phone -- (505) 646-1605 Department of Computer Science FAX -- (505) 646-1002 New Mexico State University http://www.cs.nmsu.edu/~pfeiffer SWNMRSEF: http://www.nmsu.edu/~scifair