  For an artificial creative agent, an essential driver of the search for
novelty is a value function which is often provided by the system designer or
users. We argue that an important barrier for progress in creativity research
is the inability of these systems to develop their own notion of value for
novelty. We propose a notion of knowledge-driven creativity that circumvent the
need for an externally imposed value function, allowing the system to explore
based on what it has learned from a set of referential objects. The concept is
illustrated by a specific knowledge model provided by a deep generative
autoencoder. Using the described system, we train a knowledge model on a set of
digit images and we use the same model to build coherent sets of new digits
that do not belong to known digit types.
