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4.4 Out of the Frying Pan
We will treat each of the steps of the “algorithm” in turn, positing something about
the salient agent mechanisms and their interactions and what the prospects are for
its implementation in a computational setting.
4.4.1 Preparation
Preparation is the initial process of learning about the domain in which an agent
will attempt creativity. It entails significant interaction with the environment for
the acquisition of background knowledge and understanding accepted practices and
approaches as well as open problems. In addition, an agent must acquire or develop
some aesthetic sense of the domain, where we use aesthetic in the sense of some
abstract notion of quality. Initially this sense could be taught to the agent by the
environment in just the same way that the background knowledge is. Of course,
agents that develop new aesthetic sensibilities (a meta-level creative act?) are likely
to be considered more creative in their output. Eventually, an agent may use its
acquired background information to learn/develop such novel aesthetics. It is some-
times argued that too much preparation can result in the repression of creative pos-
sibility as old, set ideas are assimilated too thoroughly. However, it is certainly the
case that a good deal of preparation is necessary to facilitate downstream processes,
particularly those of evaluation and elaboration.
Computational challenges inherent in this step include the acquiring, encoding,
and understanding of knowledge, ontologies, formalization, etc. as well as methods
for learning/developing evaluation strategies. These are nontrivial tasks, to be sure,
but many proof-of-concept structured, semi-structured and unstructured projects
put the knowledge acquisition aspects squarely in the category of difficult-but-
manageable engineering tasks (cf., Wikipedia,
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WordNet [
18
], ConceptNet [
42
], the
semantic web
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and even the World-Wide-Web itself). As for learning/developing an
aesthetic, general purpose machine learning techniques exist for inferring structural
relations from data. In many respects, this preparation step is not unlike develop-
ing pedagogy for human students, and many AI approaches to the problem, from
ontologies to machine learning would be recognized to some extent by educational
practitioners.
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