Explaining Creative Artifacts
This provides a method for interpreting computational creativity outputs, which is incremental as it adapts existing graph and embedding techniques to a new application.
The paper tackles the problem of explaining computational creativity by developing an inverse problem formulation that deconstructs creative artifacts into associative chains, matching human creative processes; they demonstrate this approach on culinary and language generation examples, proposing the length of an optimal traveling salesman path as a novelty measure.
Human creativity is often described as the mental process of combining associative elements into a new form, but emerging computational creativity algorithms may not operate in this manner. Here we develop an inverse problem formulation to deconstruct the products of combinatorial and compositional creativity into associative chains as a form of post-hoc interpretation that matches the human creative process. In particular, our formulation is structured as solving a traveling salesman problem through a knowledge graph of associative elements. We demonstrate our approach using an example in explaining culinary computational creativity where there is an explicit semantic structure, and two examples in language generation where we either extract explicit concepts that map to a knowledge graph or we consider distances in a word embedding space. We close by casting the length of an optimal traveling salesman path as a measure of novelty in creativity.