The Computational Creativity Complex - Computational Creativity Research: Towards Creative Machines - page 86

Returning to our base-level discussion, we first note the potential difficulty this
apparent recursion introduces—it is not clear that there is a base case for terminating
the recursion. Perhaps there exists a level of abstraction sufficient so that no further
meta-level issues can arise. Or perhaps there will always be a point at which an “Aha”
moment must be provided (by a human?) that will serve the purpose of tipping the
process out of the recursion.
Finally, we will mention that it is very probably unrealistic to suppose that the
evaluation function E is decidable rather, it is likely more realistic to suggest that E
is at best semi-decidable —a quality artifact can be recognized, but it is not possible
to recognize an artifact that does not measure up to the aesthetic. 14
Now, the algorithm for F cannot simply consist of running E on a 15 because

to be decidable in some other way.
Unfortunately, the obvious trivial reduction from the classical Halting Problem 16
means that this is not possible. So, in the absence of a decidable aesthetic, the prob-
lem of computational creativity is not computable in the strong sense, independent
of whether the insight problem is real and independent of any difficulties (or lack
thereof) due to meta-level recursion issues.

E may not halt. In this case, we need F ( E , a )

4.4.5 Elaboration

The elaboration step is often described as the “99 % perspiration” that complements
the “1 % inspiration” of insight. The process is deliberate and intentional—the artifact
is situated relative to the background knowledge, additional variations and details are
generated and evaluated against the aesthetic, feedback from the environment may
drive additional iterations and local refinement (or, even potentially major revisions).
Herein lies all the hard work of development and polishing ideas, framing results
and selling the finished product, and these processes may themselves require addi-
tional creativity, both large and small—iterating or recursing on some or all of the
five “algorithmic” steps. Edison and his lightbulb are the perfect example here, not
only for the oft-cited methodical search for the right filament material but also for
the equally important development of the necessary infrastructure, marketing and
educational programs that facilitated his version of the technology being adopted
over all previous and contemporary competitors.
The computational challenges here are in many ways similar to those at the prepa-
ration stage, only in the reverse. Now, the system, rather than needing to acquire
knowledge must dispense it, communicating both results and their import. The hard

14 Perhaps the environment itself accepts those artifacts that everyone appreciates and rejects those
that no one appreciates but isn't sure about those with mixed reception. Any aesthetic that accurately
models such a scenario will not be decidable given the existence of all three types of artifact.
15 Unless it is acceptable to have programs that may not terminate. If the insight issue is the sticky
fourth case, this will be unavoidable, in which case F may remain a simple simulation of E without
incurring any additional computational penalty for the overall “algorithm”.
16

Actually, the most obvious reduction is from the related Acceptance Problem.