HDTenEightyP/NetNews huggingface dataset:
From: stgprao@st.unocal.COM (Richard Ottolini)
Subject: Re: What does "AND" mean?
Organization: Unocal Corporation
Date: Fri, 22 Jan 1993 15:47:05 GMT

In article <1993Jan21.225423.25301@netcom.com> gperkins@netcom.com (Glen C. Perkins) writes:
>.7 PRETTY AND .5 NICE = .5 INTERESTED

The confusion is over the meaning of AND and OR which are imprecise in colloquial English,
but precise in computerese.  AND means both conditions satisfied, OR means any condition
satisfied.  AND tends to lower membership, OR increase it.
The example used above is an incorrect translation of colloquial English into computerese
becuase you meant "either pretty or nice" in English, but used the word AND that can have either
meaning depending on usage and context.
From: cgra@btma74.nohost.nodomain (Chris Gray)
Newsgroups: comp.ai.fuzzy
Subject: Re: What does "AND" mean?
Date: 22 Jan 93 16:44:56 GMT
Reply-To: cgra@se.alcbel.be


In article <1993Jan21.225423.25301@netcom.com>, gperkins@netcom.com (Glen C. Perkins) writes:
> Suppose I'm looking for a wife "scientifically" (yeah, right) and I 
> decide that my rule for evaluating candidates is "the prettier and the 
> nicer, the more interested I am."

But this ain't a fuzzy set, it's a continuum. Now if you were to say
"If she's nice and she's pretty then she's for me", then you have a fuzzy
rule on three sets {nice}, {pretty}, and {4me}. Then I think that the
interpretation that e.g. miss (.5,.7) is only .5 4me is not so counter-
intuitive after all. Change the "and" of the rule to "or" and her 4me
rating goes up to .7 - that makes sense to me, too.

Multiplication works fine if you are allocated a wife by a lottery, and
you have a .5 chance that she's nice and a .5 chance that she's pretty.
Then the chance of getting a nice, pretty wife really is .25 (assuming
niceness and prettiness are uncorrelated).

BTW if AND is a*b, as in boolean algebra or probability theory, then OR
will presumably be a+b-a*b, which solves your problem of squeezing it into
the range (0,1). The chance of getting a wife who is  nice OR pretty is
5+.5-.25 = .75, so cheer up.

__________________________________________________________________________
Chris Gray        | Any views expressed are those of the author and not of 
cgra@se.alcbel.be | Alcatel Bell, Technology Project Services, the British
Compu$erve:       | Computer Society, Whale and Dolphin Conservation Soc.,
100065.2102       | Scottish Youth Hostels Association, etc. etc. etc.
Newsgroups: comp.ai.fuzzy
From: tob@world.std.com (Tom O Breton)
Subject: Re: Fuzzy logic and probability: entropy
Organization: The World Public Access UNIX, Brookline, MA
Date: Thu, 21 Jan 1993 23:15:21 GMT

Friedrich:

> Kosko argues somehow like this (I know I exaggerate):
>
> 1. There is something in probability theory called entropy.
> 2. There is something in fuzzy set theory called entropy which
> 2.1 has certain similarity to the probabilistic entropy,
> 2.2 can be derived immediately from first principles of max-min.
> 3. Therefore fuzzy set theory starts where Western logic ends.

I had never heard that before. Would you care to describe this "fuzzy
entropy" in a bit more detail?

Entropy as related to probability and thermodynamics is inversely
proportionate to the information in the system. How is it generalized or
extended to fuzzy logic?

			Tom

-- 
The Tom spreads its huge, scaly wings and soars into the wild sky...      
(tob@world.std.com)
Newsgroups: comp.ai.fuzzy
From: rvs1265@ritvax.isc.rit.edu
Subject: Re: Fuzzy music
Reply-To: rvs1265@ritvax.isc.rit.edu
Organization: Rochester Institute of Technology
Date: Mon, 25 Jan 1993 18:17:01 GMT

I guess, you have a good expression of concern there!!
Why do you think you need a fuzzy inspiration for FFT, you always have the Radon Transform if anything goes astray with FFT.
It's true that people *seem* to be talking about Washing Machine;s etc..,
but the way you got to look at it is in terms of the intuition they render.

Sudhakar
Newsgroups: comp.ai.fuzzy
From: pbm@mdis2.wdl.loral.com (Paul Melville)
Subject: Re: What does "AND" mean?
Organization: /users/pbm/.organization
Date: Fri, 22 Jan 1993 06:00:53 GMT

In article <1993Jan21.225423.25301@netcom.com> gperkins@netcom.com (Glen C. Perkins) writes:

>   Suppose I'm looking for a wife "scientifically" (yeah, right) and I 
>   decide that my rule for evaluating candidates is "the prettier and the 
>   nicer, the more interested I am." Assume 0.0 PRETTY is elephant 
>   (wo)man, .5 is average, 1.0 is to die for, and 0.0 NICE is intolerable, 
>   .5 is average, 1.0 is an angel. If I decide to use MIN to represent AND 
>   (the most common definition of AND in fuzzy systems), I get:

   [etc.]

A thought: You seem to imply that your membership rules for both PRETTY
and NICE are linear. i.e. if Miss X is "twice" as pretty as Miss Y,
then,

	mPRETTY(X) == 2 * mPRETTY(Y)

what about first choosing a more realistic membership function, one
that reflects how you feel about the relative importance of that LEVEL
of PRETTY-ness or NICE-ness.  Like this:

mNICE(x)^
     1.0|                                  *****
        |                          ********
        |                    ******
        |                ****
        |             ***
     0.5|           **
        |          *
        |         *
        |        *
        |       *
     0.0+--------------------------------------->  x
        0   1   2   3   4   5   6   7   8   9  10
        ^                   ^                   ^
        |                   |                   |
	downright         ave.                angel
	nasty

where "x" represents your linear rating of the marital candidate.
I still think that you are right about wanting soe kind of anD beside
MIN, but if you calculate membership in the two sets with some kind of
(easily defined) curve similar to above, then a simple average ight be
OK, or RMS or whatever.

In other words, rather than ANDing PRETTY and NICE, try ANDing
PRETTY_ENOUGH_TO_BE_MY_WIFE and NICE_ENOUGH_TO_BE_MY_WIFE.

I think that this "enough" post-fix qualifies as an HEDGE function.

	mPRETTY_ENOUGH(x) = (mPRETTY(x)) ^ (1/2)

the above example is simplistic, but demonstrates the idea.  I
understand that HEDGES can be used in situations like this, where the
logic needs to track closely with natural language....

any return thoughts?
mTIRED(Paul) = .95
--
+   +   +   +   +   +   +   +   +   +   +   +   +   +   +
Paul Melville
pbm@wdl1.wdl.loral.com
-   -   -   -   -   -   -   -   -   -   -   -   -   -   -
From: lakerb@rcwusr.bp.com
Newsgroups: comp.ai.fuzzy
Subject: Re: Fuzzy music
Date: 26 Jan 93 14:41:55 -0600
Organization: BP Research, Cleveland, OH (USA)

In article <rolfe.727699827@sfu.ca>, rolfe@fraser.sfu.ca (Christopher John Rolfe) writes:
> 	Amid all the engineering buzz, is there any interest in fuzzy
> logic applications beyond washing machines ( or those verdammten
> pendulums )?  Fuzzy spectra as an alternative to FFT's, or fuzzy
> voice-leading appeal to me as naturalistic models of music.  Any
> others involved in fuzzy sound?
>  

There is a surge of activity in Wavelet Theory/Analysis.  Some of the work
there verges on deliberately "fuzzifying" signal and image representations in
the Wavelet domain (for purposes of signal/image compression).  A preprint,
"Ideal Spatial Adaptation by Wavelet Shrinkage" by David L. Donoho and Iain M.
Johnstone, Dept. of Statistics, Stanford, is available for FTP access.
(playfair.stanford.edu)

The abstract begins:

With ideal spatial adaptation, an oracle furnishes information about how best
to adapt a spatially variable estimator - piecewise constant, piecewise
polynomial, variable knot spline, or variable bandwidth kernel - to the unknown
function. ...

In my humble opinion (no theorems allowed!), this area of work "looks"
(fuzzily) a lot like a fuzzy "spectrum".

As an aside:

Yes, I was involved in "fuzzy sound", collaborating with a composer (Ralph
Cherubini) on composition in the Walsh-Hadamard + Time domain.  Our work "Small
Changes" was presented at the (2nd?) Computer Music Conference in Urbana.

Rob Lake
BP America Research
lake@rcwcl1.dnet.bp.com

Newsgroups: comp.ai.fuzzy
From: gperkins@netcom.com (Glen C. Perkins)
Subject: Re: WHEN and WHY should I use FUZZY logic?
Organization: Netcom - Online Communication Services  (408 241-9760 guest) 
Date: Fri, 22 Jan 1993 17:57:28 GMT

chuckb@stein.u.washington.edu (Charles Bass) writes:

>gperkins@netcom.com (Glen C. Perkins) writes:

>>It's not always as easy as I make it sound, OBVIOUSLY, but
>>tweaking these rules is almost always easier than deriving
>>simultaneous differential equations in multiple variables
>>from first principles!! And a control system based on these

>If you don't know how to derive DE's from first principles this
>is probably true. However, it is not *that* difficult, espcially
>with symbolic math tools that have gained popularity over the
>last few years.

Easy for you... ;-)  I asked the very same Bart Kosko that Erik
was grumbling about a question approximately the same as yours:
"why use fuzzy logic when we have [x and y and z methods]?" and
his answer was (roughly): "because there are more problems
than experts." If solving the problem of creating a controller
that safely backs a car into a parking space can be done by a
competent driver instead of requiring a specialist in non-linear
control systems (or fill in your own example of a competent
user versus an "expert") we'll get more problems solved. And
there are quite a few problems where the first principles are
so obscure (maybe even unknown) that having the ability to
quickly build a model that reproduces observed behavior "well
enough" comes in very handy. It doesn't follow, though, that
fuzzy systems are always the best tools for a particular job.
-- 
+-----------------------+---------------------------------------------------+
|--- Glen C. Perkins ---|                                                   |
| <gperkins@netcom.com> |             "Don't forget, your mind              |
| Native Guide Software |              only *simulates* logic."             |
| Palo Alto, California |                                                   |
+-----------------------+---------------------------------------------------+

Newsgroups: comp.ai.fuzzy
From: gebhardt@gmd.de (Friedrich Gebhardt)
Subject: Fuzzy logic and probability: entropy
Followup-To: comp.ai.fuzzy
Organization: GMD
Date: Thu, 21 Jan 1993 07:22:07 GMT

Kosko argues somehow like this (I know I exaggerate):

1. There is something in probability theory called entropy.
2. There is something in fuzzy set theory called entropy which
2.1 has certain similarity to the probabilistic entropy,
2.2 can be derived immediately from first principles of max-min.
3. Therefore fuzzy set theory starts where Western logic ends.

And the same with Bayes theorem.

Incidentally, because his entropy does not work with t-norms other than
min, min is the only t-norm that makes sense (p. 277).

Friedrich Gebhardt
Gesellschaft fuer Mathematik und Datenverarbeitung mbH (GMD)
D-5205 Sankt Augustin 1
Newsgroups: comp.ai.fuzzy
From: mpp@cns.brown.edu (Michael P. Perrone)
Subject: The Axioms of Fuzzy Logic
Organization: Center for Neural Science, Brown University
Date: Thu, 21 Jan 1993 22:06:33 GMT

Is there a set of precise axioms for Fuzzy Logic?
Would someone please post them?  I think that comparing
the axioms of probability to those of fuzzy logic is the
best way to understand the purported differences between
the two.

Thanks,
Michael Perrone
Newsgroups: comp.ai.fuzzy
From: marcel@ADS.COM (Marcel Schoppers)
Subject: Re: What does "AND" mean?
Summary: normal distributions, etc
Organization: Advanced Decision Systems, Mtn. View, CA (415)960-7300
Distribution: na
Date: Fri, 22 Jan 1993 19:46:43 GMT


Let me propose a non-fuzzy way of thinking about what the fuzzy AND "should"
do.  (Get your wooden stakes and bibles out...)

The human population exhibits normal distribution on every property people
have examined, including intelligence (I mean IQ), opinions of looks, etc.
So suppose I decide to calculate person X's rating on the beauty scale as the
probability that a randomly chosen subject will think X is beautiful.  That
probability IS that person's fuzzy membership of the "beautiful people" set.
Then I collect such probabilities for a large number of X's, and I'll get a
normal distribution: probability of perceived beauty is the independent
variable, number of people having that beauty rating is the dependent variable.
OK, let's divide the vertical axis (number of people) by the total number of
people rated, so the vertical axis becomes the probability that a randomly
chosen person will have a given beauty rating.

I can do the same thing with people's nice-ness, and get a second distribution,
(presumably) orthogonal to the first, i.e. now I've got a 2-D normal
distribution with two horizontal axes; the original distributions were seen
to be "projections" of this 2-D distribution onto one of the two axes (i.e.,
integration over the other axis).

Now I want to rate someone as a possessor of both beauty and nice-ness.  The
obvious way to do that is to

	1. rate that person on the two characteristics, thus finding their
	   point on the 2-D distribution,

	2. construct an "evaluation axis" that is at 45 degrees to the original
	   axes, thus weighting beauty and nice-ness equally,

	3. perform a projection/integration of the 2-D distribution onto the
	   evaluation axis, thus getting a new 1-D distribution (which happens
	   also to be normal)

	4. along the way we will also be projecting the candidate's point onto
	   the evaluation axis, and so will get both a rating on that axis
	   (horizontal) and a corresponding probability that a random person
	   has that rating (vertical).

	5. the evaluation axis, running from [0,0] to [1,1], has length
	   sqrt(2), and the mode of the distribution will occur at its mid-
	   point, so let's re-calibrate it to run from 0 to 1.

The interesting thing is to see what rating the person gets on the evaluation
axis.  This is in fact trivial to figure out, we get

	pos-on-evaluation-axis = (X + Y) / 2

and I would propose this as being one possible "beauty + nice-ness" rating.
(If you're thinking that YOU wouldn't be interested in someone who had beauty
= 1 and nice-ness = 0, I'd argue that maybe your personal opinion doesn't
matter, many men WOULD be so silly, and I'm talking about distributions over
populations.)

marcel
From: chuckb@stein.u.washington.edu (Charles Bass)
Newsgroups: comp.ai.fuzzy
Subject: Re: WHEN and WHY should I use FUZZY logic?
Date: 23 Jan 1993 01:04:45 GMT
Organization: University of Washington
NNTP-Posting-Host: stein.u.washington.edu

 A quick remark regarding fuzzy systems.  

 If one doesn't need a plant model to use fuzzy logic how does
 one develop fuzzy rule sets/ test fuzzy rule sets and use a
 computer aid in generating the fuzzy rule sets?

 It appears that this is an important point.  If I'm going to
 have a fuzzy Missle Guidence System and I need a plant model in
 order to automatically (aid) in generating a controller am I
 better off just using that model and standard state space
 analysis methods?  You can't really do trial and error on such
 systems...
 
 If it is done purely by trial and error could similar results
 be had by "tuning" a PID controller (or several PID
 controllers).

 chuckb

 disclaimer: I hope I'm not sounding like I don't beleive fuzzy
 logic can develop controllers that work.  This point has been
 made.  I'm just trying to wade thru the marketing so I can
 understand the "big picture" a bit better.
Newsgroups: comp.ai.fuzzy
From: emily@azariah.tamu.edu (Emily Glass)
Newsgroups: comp.ai.fuzzy
Subject: Re: What does "AND" mean?
Date: 22 Jan 1993 15:26:50 GMT
Organization: Texas A&M University, Dept. of Electrical Engineering
NNTP-Posting-Host: azariah.tamu.edu

In article <1993Jan21.225423.25301@netcom.com> gperkins@netcom.com (Glen C. Perkins) writes:

>(wo)man, .5 is average, 1.0 is to die for, and 0.0 NICE is intolerable, 
  ^^^

    I'm really *glad* to see someone finally took the "sexism" out of
  this example :).  (There are a few of us women who read this 
  net too :) ) 

>.5 is average, 1.0 is an angel. If I decide to use MIN to represent AND 
>(the most common definition of AND in fuzzy systems), I get:
>
>.7 PRETTY AND .5 NICE = .5 INTERESTED
>
>Okay, fine, but this means that she could get prettier approaching 
>1.0 ("to die for") without my interest increasing at all. In other 
>words, if she were .5 NICE, I WOULDN'T EVEN CARE whether she was 
>average (.5) or gorgeous (1.0), it would be just the same to me. 
>Needless to say, that's not what I observe! At least in this case, 
>when I say "AND" I don't seem to mean MIN.

... stuff deleted ...

>Ultimately, it appears that the only way to model this deceptively 
>simple and I think typical psychological rule: "the prettier and the 
>nicer, the more interesting" would be for me to sit down and build a 
>truth table. I'd probably need to make one axis PRETTY and one axis 
>NICE in, say, increments of 0.1, and then ask myself how I'd feel 
>about each combination. Then I'd try to figure out what the function, 
>INTEREST(PRETTY, NICE) really was.

...

>+-----------------------+---------------------------------------------------+
>|--- Glen C. Perkins ---|                                                   |
>| <gperkins@netcom.com> |             "Don't forget, your mind              |
>| Native Guide Software |              only *simulates* logic."             |
>| Palo Alto, California |                                                   |
>+-----------------------+---------------------------------------------------+
>


I can't answer your question about with AND to use, but it looks
like you need to have your fuzzy sets broken down into "finer" 
distinctions:

looks:

  REAL_UGLY  SORT_OF_UGLY  PLAIN  ATTRACTIVE  GORGEOUS

personality:

  NASTY  INDIFFERENT  NICE  REALLY_SWEET

control functions:

  NO_INTEREST INDIFFERENT   SLIGHT_INTEREST  SOME_INTEREST LOTS_OF_INTEREST

Then you can make rules like:

 (If you are a heterosexual woman (or a gay man), substitute "he" for "she")

 If she is REAL_UGLY then NO_INTEREST.

 If she is NASTY then NO_INTEREST.

 If she is INDIFFERENT and GORGEOUS then SLIGHT_INTEREST.

 If she is REALLY_SWEET and GORGEOUS then LOTS_OF_INTEREST.

 If she is REALLY_SWEET and ATTRACTIVE then LOTS_OF_INTEREST.

 If she is REALLY_SWEET and PLAIN then LOTS_OF_INTEREST.

 If she is NICE and PLAIN then SOME_INTEREST.

... etc...

You may not need this many catagories, but you will proably need more 
than just "PRETTY" and "UGLY" for this example.

Emily
Newsgroups: comp.ai.fuzzy
From: erik@til.com (Erik Horstkotte)
Subject: Re: The interested bu lazy guy did some home
Reply-To: erik@til.com
Organization: Togai InfraLogic, Inc.
Date: Thu, 21 Jan 1993 19:14:11 GMT

In article 1439@se.alcbel.be, cgra@btma74.nohost.nodomain (Chris Gray) writes:
>Fuzzyists seem to think it is a feature of fuzzy logic that the rules can
>vary according to the application, and that their controllers function
>quite well even when the membership function and control functions are
>not quite right: this they call "robust", cause of much flaming from the
>classical control guys, who already assigned a meaning to this word
>(sound familiar?).

In fact, the majority of us "fuzzyists" know very well that this is not what
control theory folks call "robustness".  It appears that perhaps you've been
somewhat "poisoned" through having read Bart Kosko's book on fuzzy logic and
neural networks, where he misuses the term this way.  One thing you have to
keep in mind when reading his books, which do have some *useful* things to
say, is that Dr. Kosko has the bad habit of reinventing terminology as he
goes.  He's already somewhat infamous in the fuzzy community for discarding
all of the terminology that was built up over more than two decades about
inference methods, etc., and dealing with fuzzy systems *only* by trying to
make them look like neural networks.  This isn't to say he's a fool.  Just
a pain in the ^&*( at times, and I get tired of having to translate his
work into standard terminology!

I, for one, don't use the term to "robustness" to describe this kind of
insensitivity.  I *do* use the term for some classes of fuzzy control system
that have been empirically demonstrated to be relatively insensitive to
variations in the system being controlled - which *is* very similar to the
classical control theory definition of the term.

And yes, I do get a little annoyed at articles that make it sound like the
people in the fuzzy community are a bunch of identical sheep.  We're all
different, and just like any other group of people there are idiots and
geniuses amongst us.

Grumble mode off.
---
Erik Horstkotte, Togai InfraLogic, Inc.
The World's Source for Fuzzy Logic Solutions (The company, not me!)
erik@til.com, gordius!til!erik - (714) 975-8522
info@til.com for info, fuzzy-server@til.com for fuzzy mail-server

Newsgroups: comp.ai.fuzzy
From: gperkins@netcom.com (Glen C. Perkins)
Subject: Re: WHEN and WHY should I use FUZZY logic?
Organization: Netcom - Online Communication Services  (408 241-9760 guest) 
Date: Thu, 21 Jan 1993 17:40:51 GMT

Not all devices are on/off devices. I think it's Sugeno in Japan who
is experimenting with control of a damaged helicopter. He has a
system (I'm told) that will regain control of a helicopter after
part of its rotor has been shot off.

There are very few on/off flight controls in a helicopter, and
this system looks at the signal from several sensors simultaneously
and comes to an extremely rapid conclusion regarding how and
how much to adjust the flight controls to respond to the 
helicopter's current circumstances.

Fuzzy logic seems to work best when you want fast, continuous
response to several rapidly changing inputs and you have no
"model" of the system (in the classical algorithmic sense.)
In the case of a balancing rod, we have control equations, so
it's not a very good example of advancing the frontiers of
knowledge, but it's a good example of how I could put together
a control system WITHOUT KNOWING those equations, and without
needing to know them. I'd just tell the system principles such
as "if the rod is a little bit to the left and falling slowly,
move your hand slowly to the left" and with a half dozen or
so of these rules I'd have a first approximation of a working
control system. I could come up with these rules by just
looking at the system and saying tom myself "what would I
do in X circumstances?" and entering it as a "rule." Then
I'd watch it work an see what happened and adjust it.

It's not always as easy as I make it sound, OBVIOUSLY, but
tweaking these rules is almost always easier than deriving
simultaneous differential equations in multiple variables
from first principles!! And a control system based on these
simple fuzzy rules operates much faster than a real-time
solution of the differential equations in general.
-- 
+-----------------------+---------------------------------------------------+
|--- Glen C. Perkins ---|                                                   |
| <gperkins@netcom.com> |             "Don't forget, your mind              |
| Native Guide Software |              only *simulates* logic."             |
| Palo Alto, California |                                                   |
+-----------------------+---------------------------------------------------+

Newsgroups: comp.ai.fuzzy