@misc{10481/87127,
year = {2020},
url = {https://hdl.handle.net/10481/87127},
abstract = {Designing a story is widely considered a crafty yet critical task that requires deep specific human
knowledge in order to reach a minimum quality and originality. This includes designing at a high
level different elements of the film; these high-level elements are called tropes when they become
patterns. The present paper proposes and evaluates a methodology to automatically synthesise
sets of tropes in a way that they maximize the potential rating of a film that conforms to them.
We use deep learning to create a surrogate model mapping film ratings from tropes, trained
with the data extracted and processed from huge film databases in Internet, and then we use a
Genetic Algorithm that uses that surrogate model as evaluator to optimize the combination of
tropes in a film. In order to evaluate the methodology, we analyse the nature of the tropes and
their distributions in existing films, the performance of the models and the quality of the sets of
tropes synthesised. The results of this proof of concept show that the methodology works and is
able to build sets of tropes that maximize the rating and that these sets are genuine. The work
has revealed that the methodology and tools developed are directly suitable for assisting in the
plots generation as an authoring tool and, ultimately, for supporting the automatic generation of
stories, for example, in massively populated videogames.},
keywords = {Content Generation},
keywords = {Tropes},
keywords = {Computational Narrative},
title = {StarTroper, a film trope rating optimizer using Deep Learning and Evolutionary Algorithms},
doi = {10.1111/exsy.12525},
author = {García-Ortega, Rubén Héctor and García Sánchez, Pablo and Merelo Guervos, Juan Julián},
}