[From Erling Jorgensen (951112.2050CST)]
[Bill Powers (951111.0630 MST)]
The problem with fuzzy logic is that (as I understand it) it tries to
approximate continuous relationships through using probability
distributions. There has to be a random variable in order to sample the
distribution on any given iteration. The result of this is to substitute
for a smooth continuous function a noise envelope that follows the same
basic relationship but with a large amount of superimposed random
variation. This superimposed noise is one of the factors that limits the
possible accuracy and stability of fuzzy-logic control systems.
What you describe here is similar to the way I think of the
_Principle_ level of the proposed PCT Hierarchy. Any given
instantiation of control of a principle, to me, seems to involve
a "judgment under uncertainty." And I guess I imagine (without
testing or modeling) that this would be similar to "a noise
envelope that follows the same basic relationship."
For instance, in walking out when I think the cashier might have
undercharged me, am I being "honest enough"? Is my perception
of my current behavior _close enough_ to my reference for acting
honestly? As Lt. Com. Data might say, am I "operating within
acceptable parameters"? In this instance, I may be somewhere
in that noise envelope, not great, but okay for now, given the
other things (like getting home) that I want to control for.
Especially if "next time (or last time if I remember correctly!)
I will (or did) give back the change." In other words, if other
iterations _average out_ to my refernce for honesty, then this
little aberration is just a "random fluctuation."
Does anyone else think of control of principles as probability
distributions?
All the best,
Erling