[From Rick Marken (951229.0830)]
Shannon Williams (951228)---
In _The Philosophy of Artificial Intelligence_, Andy Clark briefly
describes PDP (Parallel Distributed Processing).
I see a couple of ways to change the system:
1) Since a reference level exists, the unit should continuously 'adjust the
weights on the hidden units' until the correct output is generated...
5) I can see atleast one way that the reference generator can evolve: Let
the first letters be input...
What do you think of this project?
It's hard for me to tell from your verbal description. Could you provide a
simple diagram of what you would like to do?
Here's what I think you might mean:
r
>
v
---> C --- e
p |
> adjust
> k weights
> s | |
^ |
> >
> v
[i-->|o = sum(ki)| -->o]
This control system adjusts the weights (k values) of a linear pattern
recognizer (o = sum(ki)). The weights of the pattern recognizer are to be
adjusted so that inputs (i, sound patterns) are converted into appropriate
outputs (o, phonemes ). The control system must perceive the relationship
between i and o; so the control system's perceptual function, s, is a
relationship recognizing function.
Let's say that s is designed so that when the relationship between any i and
any o produces a perceptual value of zero (0) the relationship is what a
linguist considers the "correct" mapping of sound input to phonemic output;
non-zero perceptual values measure the discrepency of the relationship from
this "correct" mapping. This way the reference input to the control system
can be fixed at 0. The output of the perceptual function, p, is quantitative
so that there is always a quantitative error signal, e = r-p. Non-zero error
values are turned into adjustments to the k weights of the pattern
recognizer.
This system would continuously adjust the k weights of the pattern recognizer
until the output of the control system's perceptual function, s, (the measure
of relationship between i and o) is at the reference value (0), if such a
solution exists.
Of course, the tough part of designing this automatic "learning" control
system is designing 1) the function, s, that perceives the relationship
between i and o and 2) the error-driven weight adjustment algorithm -- though
this could probably be an E. coli style random process.
Is this close to what you had in mind?
Best
Rick