The Computational Creativity Complex - Computational Creativity Research: Towards Creative Machines - page 79

to some degree in the model's probability distribution over motifs and weakly in the
evolutionary mechanism for generating a lead sheet but is in general less developed
than that of DARCI; musical artifacts are generated with a simple generate-and-test
mechanism at the motif level and an evolutionary mechanism at the lead sheet level.
In PIERRE's case, background knowledge is encoded in the form of a database
of ingredients and their statistical properties and neural network models of recipe
quality; learning happens by adding to the ingredient database and building the neural
networks; intentionality is effected, to some extent, by the goal of creating edible
recipes but is less developed than in CARL and especially in DARCI; an aesthetic
is encoded in the neural network models; culinary artifacts are generated with an
evolutionary mechanism.
These internal mechanisms interact with each other in multiple ways, and both
the mechanisms and their interactions are the subject of ongoing research, with
both human and computational subjects. However, we will use them here only as
instruments for grounding and guiding the discussion.
In addition to these internal mechanisms, because the agent exists in an environ-
ment, it interacts with the environment in multiple ways, including being taught ,the
presentation of artifacts, being inspired , receiving feedback and other influences .
DARCI is taught by human-labeled images, human-generated linguistic responses
and both structured and unstructured data resources on the web; presents its artifacts
as visual images, sometimes with simple titles; is weakly inspired by target con-
cepts and can autonomously discover potentially interesting concepts online to some
extent; receives some feedback in the form of negative reinforcement for poor visuo-
linguistic associations but none yet directly for its creations, though some of those
creations have been indirectly critiqued when appearing in surveys and art exhibi-
tions.
CARL is taught by structured data resources on the web; presents its artifacts
as musical motifs or lead sheets; is inspired by various types of non-musical input,
including audio, images and even sleep data recordings; doesn't really receive any
feedback at this stage.
PIERRE is taught by structured data resources on the web; presents its artifacts as
recipes, including appropriate and sometimes amusing titles; is sometimes weakly
inspired, when the set of recipes used to drive generation and evaluation differ sig-
nificantly; receives no direct feedback at this stage, though some of its creations have
been indirectly critiqued by users of online recipe repositories and by people actually
cooking the recipes and eating the result.
Figure 4.7 offers a gross visualization of a our archetypal agent. Both on the human
and computational fronts, there have been significant advances in understanding
many of the individual mechanisms shown in the figure. What is still not understood
is how these mechanisms, both internal and external, interact to realize the creativity
“algorithm”, and it is this question that we will try to say something about here.