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Date:         Wed, 19 Aug 1992 15:42:55 -0600
From:         "William T. Powers" <POWERS_W%FLC@VAXF.COLORADO.EDU>
Subject:      Misc catching up
X-To:         CSG-L@vmd.cso.uiuc.edu

[From Bill Powers (920819.1500)]

Bruce Nevin, Gary Csiko, recent posts.

You're right, I'm wrong. With 100 people on the net, asking for a copy of
your own posts increases the activity 1 percent. Feel free.

My mainframe at Ft. Lewis, by the way, wouold put a copy of each
transmission in my directory if I told it to. As I edit and read mail
offline, I have copies of everything anyway.
----------------------------------------------------------------------
Catching up:
Gary Cziko (couple of days ago)

"Computing output" has suffered the overgeneralization that all slogans
seem to create. It's short for "computing the exact output that will create
a predetermined outcome when it's executed."

This is different from computing an output in real time that will tend to
make the present-time error get a little smaller.

Also, "computing" an output is a modeler's way of looking at things. A
modeler would say that a transistor amplifier computes (outp[ut signal) =
k*(input signal). Of course it does no such thing; it isn't computing as a
digital computer would compute. It isn't even computing as an analog
computer would do. It's just behaving according to physical principles,
which we REPRESENT as a computation in a model. This leads to a slight
digression.

The same is true in most of the nervous system. Most of the levels of
control, below the program level, don't literally compute anything.
Perceptual signals at one level depend on sets of perceptual signals at
lower levels because of the way the signals are combined in neural nets.
Only at the level where we explictly apply analog or discrete methods of
computation is there any literal computing going on.

This is one reason that talking about lower levels as if they worked in
terms of language is a mistake. A relationship-controlling system doesn't
say "The car is too far left; I'd better turn the wheel to the right." The
perceptual signal is compared with the reference signal by inhibiting part
of it; the leftover part, the error signal, passes to lower levels via
output amplifiers and weighters, adjusting the lower reference signals and
eventually the amount and direction of effort -- all without any commentary
or reasoning.

Of course at the same time, the driver's higher-level systems may be
conducting imaginary conversations about what's being experienced at the
lower levels. "Wow, I just missed that baby carriage. I'd better give it
some more gas going around the next curve to improve the traction. Good
thing this car is so heavy; it stops faster than a light car. Good thing I
have fast reflexes." Blah, blah, blah, all sorts of opinions and
misinformation that have absolutely nothing to do with how the car is being
controlled or what skills are actually being used. The verbal obbligato is
not part of the control processes going on, unless it's specifically about
resetting reference signals: "Oops, I forgot to pick up my wife, I have to
go back."

By talking, we can make behavior seem a lot more complicated than it is.
-----------------------------------------------------------------------
Dag Forssell (920817) --

>I will grant you that everything is variable, but we have been using >the
term variable to signify that feature of the environment that we >want to
control.

What you mean we, white man? I've experimented with lots of formal ways of
using terms. For while, a variable in the environment was a quantity, not a
variable, while inside the nervous system a variable was a signal. Now I
just use variable in its mathematical sense, something that varies or can
vary. The largest category is variable, and we divide it up into smaller
classes as needed: variable, input. Variable, disturbing or independent.
Variable, controlled. Variable, output. Variable, reference. Variable,
error.

I suppose that for introductory teaching it's a good idea to stick to one
terminology until the ideas are firmly grasped. But after that, I think
it's better to vary the language so people will always have to think about
what it means, and not turn the words into slogans or just memorize them.
You should be able to explain control theory many times without ever
repeating your words. When you can do that, you know you understand what it
means.
---------------------------------------------------------------------
John van Loon (920817) --

>This term I will be building an autonomous vehicle.  I will try to
>organize it to accept PCT concepts and hopefully it will serve as a
>viable testing ground ...

Good! You'll learn more about control systems by building one than from a
hundred posts.

I suggest thinking in terms of perceptions first. Give your car some
photocells, maybe some thermistors to detect heat, whatever else is easy to
do and cheap. If you're using a digital computer, get an A-to-D board so
you can handle analog inputs. Then set reference signals, compute errors,
and use the errors to adjust direction and velocity.

One caution. There's a temptation to connect the error signal to the
steering wheel to set the angle of the front wheels. In steering, the
direction of motion changes as the integral of the wheel angle. You may
have to add some first derivative to the error signal (error + d/dt(error)
to stabilize the steering.

A tip: remember to design your system as if you're inside it looking
through its sensors, knowing only the signals from the sensors.

Good luck.
----------------------------------------------------------------------
Avery Andrews (920819.1231) --

>PCT in fact depends very much on the fact that many (in fact most, by >an
overwhelmingly large margin) features of the environment can  be >leaned
on.  A steering subsystem would for example depend on all sorts >of facts
about roads and carparts about which it knows nothing, as well >as upon the
workings of the lower-level control-systems it works >through.

I don't know what this image of "leaning on the environment" is supposed to
mean. The environment is what it is, it changes when you act on it as it
changes. It often changes all by itself, causing you great trouble. Your
goals are usually stated in terms of perceptions of the environment that
you've found pleasing or beneficial to control. But just having a goal
doesn't make the environment be that way. You have to do something to the
environment, and when you do, you find out you have to do things a little
differently each time you try to control for the same thing. Often a lot
differently. The environment isn't very cooperative; it isn't very
reliable. Right now, Mary's priming the back deck to keep it from rotting
out. I'm still trying to get a piece of video software to install right.

Most of the things in the environment that seem repeatable and reliable are
kept that way by someone's close attention and hard labor. Left to itself,
the environment would soon be overgrown with weeds and full of holes. Like
my yard, which I am allowing to have its own way, more or less. To get a
view of what nature is really like, take a walk through some undeveloped
and unmaintained woods.

It's a good thing there are some regularities in nature. If there weren't
we couldn't control at all. But there are far fewer regularities than seem
to be present.

RE: beerbugs

>What goes on here is that if the bug is hungry enough (the energy level
>is far off enough from its reference level) and over food, it will >start
eating, but being eating also excites the feeding controller, so >it keeps
eating even when its energy level is no longer low enough to >successfully
trigger eating.

This arrangement comes from thinking of eating as being "triggered" like a
flip-flop. If eating becomes possible, a control-system type model would
simply eat until the reference signal was matched, and then it would stop.
There can be two levels to this: an ingestion system with its own reference
level for "fullness", and a longer-term nutrition system that monitors a
more slowing-varying variable that is gradually changed by eating and not
eating. With the right adjustments of parameters, you get bouts of eating
separated by not eating. The system is unstable, the more so if it's very
hungry (nutritionally). I must resurrect that operant-conditioning model
before it gets lost. The "scalloping" you get from using too large a reward
for each reinforcement is a simple oscillatory phenomenon, which the model
shows without any special provision for it.

The motto is, don't look for fancy explanations and models when a simple
one will do just as well.
----------------------------------------------------------------------
Best to all,

Bill P.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU