The problem of creating an algorithm that will generate only new variations
of patterns without relying on Input is interesting even though it is not
AI.  For example, the most methodical way would be to create some endless
variety of incremental iterations on the features of the patterns to
be generated.  Interestingly enough, that could easily fail due to the
infinities of infinities thing that can be found in a non-rational real
number (or the Godel Theorem.)  If, for instance, the variation iteration
operated on one feature at a time, you could keep on going forever with
very little differentiation if the features were designed literally
to march on toward an infinite variety. The variations would presumably bog
down as the program ran until it was stopped.  (Ok no one is talking about
real infinity but it is possible to write an iteration on a feature that
could go on for years.)  So in order to ensure that the pattern generator
would create images that were clearly novel, no one would use an
infinite iteration on a single feature at a time.  If on the other hand you
were to try a single iteration (of variation) on each feature one at a
time, then if the program was able to generate novel features it could
produce tediously similar images by generating uninteresting
similar features and then iterating on them.

So while this problem does not seem to be insurmountable, my claim here is
that I believe that there are a number of interesting obstacles to the
problem and they seem to tie in with things like the infinite pathways of
an infinitely branching selection problem.  Even though I am not talking
about an AI problem it seems as if it is somehow related to AI.

To explain this a little more carefully.  I want to create a novel visual
pattern generator that will create truly novel patterns.  It is perfectly
ok if it uses iterative methods of variation (including incremental
variation) but I need a way to test the program to make sure that it is
actually capable of generating a massive number of different patterns
(whatever a pattern is.)  Then the problem includes devising a sampling
method.  But the sampling method has to be dependent on jumping around the
pattern generator since we cannot wait for years while the
program tediously jumps through various incremental iterations of
variations.  So while I want to demonstrate that the program can generate
(and would theoretically eventually generate) a massive number of similar
patterns (showing that it is capable of developing a diverse number of
similar styles) I also want it to jump around so that it would be extremely
difficult to claim that it is just doing the same kind of thing over and
over again.

And since I am not really sure what a pattern is, then I have the problem
that it might create a large number of splotches of color that are all
different and yet look like they are tediously taken from some underlying
random style.  So simple iteration and simple randomness will not work.
Without judgement that can be learned from other thinking entities it might
be very difficult to design a program that can generate an immense number
of novel patterns and avoid falling into a "style" for too long a time.

So the method of developing some simple visual analytical methods, (which
like the pattern generator would be able to generate new features) that
could be used in detection, might help me to give it some kind of innate
judgement even though it could not learn anything from the Input world.

Oh yeah.  The non-rational real number thing: Where did that come from.
Since an iterative pattern generator could fall into a sub-loop if it
started repeating, it would have to be based on a non-repeating iterative
method.  While this does not have to be done directly, it could be done by
relying on a varying number of iterators, it would have to be governed by a
non-repeating method of some kind.  Ergo: a non-rational real.

It is a little more theoretical than most people recognize.  At least, that
is my opinion.

Jim Bromer



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