From Tom Bourbon (930622.1231)
[Martin Taylor 930621 18:00]
(Tom Bourbon 930621.1323)
And please delineate how your ideas in the remark to Hans differ from, say,
a plan driven system that relies on information in the form of
programs for action, thereby freeing itself from a need to rely on
information about the CEV obtained through the pereptual apparatus. As you
stated it, I see no difference.
The difference is in those words "relies on" and "freeing itself from." I
have no concept of either. Change them to "uses" and "reduces its
need for" respectively, and I have less of a problem.
Fine. Change the words. Now, please because I still do not understand, tell
me how the model implied in your remarks to Hans differ from, say, a plan
driven system that "uses" information in the form of programs for action,
thereby "reducing its need for" information about the CEV obtained through
the perceptual apparatus. As you stated it, I see no difference.
Then show me that those results generalize, with no further tinkering with
the model, to new conditions, with unpredictably different disturbances and
targets.
What I assume would be in the model doesn't have much to do with the
disturbances and targets, but with what Bill has labelled f(e)--the
effect of a particular output change on the CEV. Reorganization is
one way of building a f(e) that conforms to a predetermined model, which
has the characteristic of being monotonic, as steep as can be constructed,
and leads to negative feedback. That "model" needs no explicit form.
It works with little information about the environment (which, Bill,
incorporates all the lower-level ECSs, not just the part of the world
outside the skin envelope) other than that the sign of the feedback is
constant and the environmental gain stays adequately high. Bats, on
the other hand, seem to adjust their perceptual input filters according
to the expected time and frequency of the (doppler-shifted) echo. They
need the model to distinguish the very low-power but precisely determined
echo from whatever else is going on in their acoustic world.
How could such a model work? In the neural-net world, one rather
powerful form of node is called a sigma-pi node. It does summation
and multiplication, and can be used as a variable filter. It would
be quite reasonable, I think, for a perceptual input function to contain
the pi part of the sigma-pi, in addition to the sigma that is generally
acknowledged to be there. The input to the pi could come from the
output signal, changing the relative sensitivity of different elements
of the PIF, and thereby changing its prior uncertainty about the expected
signal. That's just one way it could work.
I'm not committed (yet) to internal models in general. I can see their
potential usefulness, but they add a complexity to the ECS with which I am
not happy. In the syntax predictor that Allan is developing for me, we do
not include (yet) any internal model. We hope we will not need to include
one to achieve good prediction. We are starting by relying on perceptual
input functions that include differentiation. Nevertheless, when we
get to noisy, smoothly changing representations of the syntax, I am
at least open to the idea that we will have to incorporate models.
As I said, it's a question of the required information rate from
perceptual signals. If you are among those who consider it an
uninteresting quantity, you will not be interested in the possible
value of an internal model as a component of an ECS.
Please, all I asked was:
Then show me that those results generalize, with no further tinkering with
the model, to new conditions, with unpredictably different disturbances and
targets.
Of course, in the original I asked to see a generalization of the
results of simulations by the model you suggested. That is all I need to
see, for you to convince me that what you say about information theory
*does* translate into imnprovements in the performance of the PCT model.
In the demonstration, you are free (encouraged) to assume the model in its
fully developed and informed state. You need not simulate evolution,
conception, birth, maturation, learning, social control proceses, or
enlightenment. Simply take an extant PCT model, add to it the features or
measures you believe must be there for it to be an information theoretic PCT (ITPCT)
model, and let it run. I described my criteria for improvement in other
posts long ago, and in one addressed to Hans Bloom a few minutes ago. A
demonstration like that would clear the air of gigabytes of "I said," "You
said," "We said," and the like. And it would focus the discussion on the
real issue -- does the PCT model work and, if so, can it be improved?
Here's a counter-challenge to the skilled modellers. I think it is fair,
because we have not yet developed our own model, so we can see whether
anyone, ourselves included, can solve the problem.
This is another kind of "challenge" entirely. In fact, my offer is not a
challenge. I am merely saying that we know the PCT model works for certain
instances of control by humans. We know the model can be and should be
improved. We are eager to enlist the support of anyone who wishes to join
in that endeavour. The criteria for demonstrating improvement in the model
are simple and direct. Have at it. We have even published and posted the
PCT model (all two lines of it, if you include the environment) many times,
so you can avoid the need to develop your own model. Please, use ours as a
testbed for your ideas. (I am completely serious -- no attempt by me to be
cute, clever or condescending.)
This is not a contest in which we try to prove prowess and skill -- not for
me it isn't -- I have neither of those "attributes." My skills are limited.
I would like to see people with skills and resources superior to my own
devote some of their time and creativity to working on our project.
Define a formal grammar (say a BNF grammar) with 3 levels between the
root and the leaves. Assert for each leaf symbol a description consisting
of a location in an arbitrary 3-space (by analogy, think of phonetic
feature values for phonemes). Let a control system "see" the succession
of locations defined by the successive symbols output by executing the
grammar with predefined probabilities of taking the different branches.
The ouput of the control system is a location in 3-space. The three
"intrinsic variables" that the control system must maintain are the
difference between the locations of the output symbols and its own
three dimensional output. The control system may be designed or it
may learn (ours will learn).
Obviously, if the grammar output moves very slowly, any 3-D control
system will work. Our problem is to get the control system to move
to the right place as early as possible, preferably in synchrony with
the motion of the grammar output point, which is moving quickly.
So far, we have not defined a challenge grammar or specified its rate
of output, but we assume that the output point will have to stay stable
for at least two compute cycles for the control system to have any chance
of learning. We think that our control system will learn to have about
as many levels as there are in the grammar, but that remains to be seen
(it will grow by inserting ECSs between the "intrinsic variable" control
ECSs and the top perceptual layer, as discussed last week).
You got me there, Martin. Congratulations. I sure can't do that, but then
I never claimed to be a skilled modeler. Now, can I interest you in
helping us figure out how to improve the PCT model for something as mundane
and trivial as stick wiggling?
Until later,
Tom Bourbon