can anyone give me some comments on the following problem concerning the use
of stereotypes together with fuzzy logic in user modeling?
In fuzzy logic the defuzzification of a membership function leads to a crisp
value for a property of an object (for example a new value for a valve as
part of a technical process).
I would like to adopt this method for the "defuzzification" of stereotypes in
user modeling. In this context a stereotype is a collection of typical
properties of a user. If we use fuzzy logic in the trigger rules that
control the ascription of stereotypes to specific users, we get membership
functions for each ascribed stereotype. So far this is equal to regular fuzzy
logic rules.
If we try to determine the properties of a user Ben, who is supposed to be a
"UNIX-expert", we have to perform a kind of defuzzification. We can do this
in two ways: either we state, that Ben has all properties contained in the
stereotype to a restricted extent or we give up some of these properties
and claim that he has the rest of the properties to the full extent.
A combined point of view is possible, as well.
In my opinion the second alternative mixes up imprecision and uncertainty as
the membership of a property to a stereotype is always uncertain. So the
first alternative would be eventually the clearer one. But if we consider
our own intuitive way to form conclusions out of a statement like "Ben is a
70% UNIX-Expert" we probably come to a result, that uses both alternatives
mentioned. So, what is the better solution? Does anybody have a meaning about
the theoretical aspects and the solution of the problem?
Thanks,
Hans Zimmermann.
-- ---------------------------------------------------------------- Sender: Hans Zimmermann Address (Office): Burlafingerstrasse 4, 89233 Neu-Ulm, Germany Phone (Office): +49-7308/919096 Fax (Office): +49-7308/919097 Email: Hans.Zimmermann@T-Online.de ----------------------------------------------------------------