Abstract
This paper presents the current state of the online game Iconoscope and analyzes the data collected from almost 45 months of continuous operation. Iconoscope is a freeform creation game which aims to foster the creativity of its users through diagrammatic lateral thinking, as users are required to depict abstract concepts as icons which may be misinterpreted by other users as different abstract concepts. From users’ responses collected from an online gallery of all icons drawn with Iconoscope, we collect a corpus of over 500 icons which contain annotations of visual appeal. Several machine learning algorithms are tested for their ability to predict the appeal of an icon from its visual appearance and other properties. Findings show the impact of the representation on the model’s accuracy and highlight how such a predictive model of quality can be applied to evaluate new icons (human-authored or generated).
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Liapis, A., Gravina, D., Kastbjerg, E., Yannakakis, G.N. (2019). Modelling the Quality of Visual Creations in Iconoscope. In: Liapis, A., Yannakakis, G., Gentile, M., Ninaus, M. (eds) Games and Learning Alliance. GALA 2019. Lecture Notes in Computer Science(), vol 11899. Springer, Cham. https://doi.org/10.1007/978-3-030-34350-7_13
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DOI: https://doi.org/10.1007/978-3-030-34350-7_13
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