Estimating and Predicting Human Perception on Computer-Generated Artwork Variants
Résumé
Computer assisted human creativity encodes human design decisions in algorithms allowing machines to produce artwork variants. Based on this automated production, one can leverage collective understanding of beauty to rank computer-generated artworks according to their average likability. We present the use of Software Product Line techniques for computer-generated art systems as a case study on leveraging the feedback of human perception within the boundaries of a variability model. Since it is not feasible to get feedback for all variants because of a combinatorial explosion of possible configurations, we propose an approach that is developed in two phases: 1) the creation of a data set using an interactive genetic algorithm and 2) the application of a data mining technique on this dataset to create a ranking enriched with confidence metrics.