Purpose: Many of the stories we are exposed to are built from small patterns of connected events involving a set of characters -- boy meets girl leads to a relationship or crime leads to revenge. The present paper studies the computational constraints that apply to the task of putting together a story by combining a set of such patterns. This approach presents three challenges: how to mix up the elements in the different patterns,how to instantiate the characters across the patterns andhow to tell acceptable combinations from the rest.
Methods: The present paper applies an evolutionary solution that relies on a genetic representation for these combinations of patterns, and applies as fitness functions a set of metrics on compatibility constraints across pattern combinations. Outputs of this procedure are evaluated by human judges in comparison with baseline solutions.
Results: The proposed solution generates a population of story drafts that resemble plot descriptions for simple stories. A comparative evaluation by human judges against baselines based on random gene assignment yields positive results.
Conclusion: The genetic representation of pattern combinations and the metrics on compatibility across pattern pairs provide a valid evolutionary solution for constructing simple plots.