The Computational Creativity Complex - Computational Creativity Research: Towards Creative Machines - page 77

to discriminate between invalid random recipes and the valid ones, created by actual
people.
Each MLP has an input layer consisting of real-valued nodes that encode the
amount (in ounces) of each super-group (sub-group), a hidden layer consisting of 16
hidden nodes and a single real-valued output node that encodes the rating (between
0 and 1). The MLP weights are trained until there is no measurable improvement
in accuracy on a held out validation data set. The set of weights used for evaluating
generated recipes are those that performed the best on the validation data set.
4.2.3.4 Presentation
Colton has suggested that perception plays a critical role in the attribution of cre-
ativity (or, more precisely, in the attribution of un creativity) [ 38 ]. In other words, a
computationally creative system could (and possibly must) take some responsibility
to frame its work to engender a perception of creativity (or at least to avoid being
summarily labeled uncreative).
In an attempt to help facilitate such a perception of its artifacts, PIERRE contains
a module for recipe presentation. First, the module formats the recipe for human
readability. Ingredient quantities are stored internally in ounces, but when recipes
are rendered for presentation, the ingredients are sorted by amount and then formatted
using more traditional measurements, such as cups, teaspoons, dashes, and drops.
Recipes are presented in a familiar way, just as they might appear in a common
cookbook.
Second, the presentation module generates a recipe name. Standard recipes always
have a name of some sort. While this task could be a complete work by itself, PIERRE
employs a simple name generation routine that produces names in the following for-
mat: [prefix] [ingredients] [suffix] . This simple generation scheme produces names
such as “Homestyle broccoli over beef blend” or “Spicy chicken with carrots sur-
prise.” The components of the name are based on prominent recipe ingredients and
the presence of spicy or sweet ingredients. This simple approach creates names that
range from reasonable to humorous.
Recipe 1 is an example of one of PIERRE's creations and was among those served
during a computational creativity festival titled You Can't Know My Mind [ 39 ]. To
produce this recipe, a population size of 150 recipes was allowed to evolve for 50
generations with a mutation rate of 40 %.
4.3 The Blind Leading the Blind
We now make two nontrivial conceptual leaps, positing both an abstract model of
a domain-independent creative agent, and an abstract creativity “algorithm” for that
agent to run. Here we will simply present both in enough detail to facilitate the
discussion to follow, in which we consider how such an agent might or might not