Geometrical vs topological measures for the evolution of aesthetic maps in a RTS game

•We present a procedural content generation (PCG) method to generate aesthetic maps.•Maps characterized based on either their geometrical or topological measures.•Topological measures taken from the sphere-of-influence graph induced by each map.•Comparison between the solutions obtained from both characterization approaches.

This paper presents a procedural content generation (PCG) method that is able to generate aesthetic maps for a real-time strategy game. The maps were characterized based on either their geometrical properties or their topological measures (obtained in this latter case from the sphere-of-influence graph induced by each map). Using these features, a distance function between maps can be defined. This function is used in turn to determine how close/far each map generated by the PCG method (a self-adaptive evolutionary algorithm) is to a collection of maps which were taken initially to be aesthetic or non-aesthetic. This correspondence guided a multi-objective evolutionary approach whereby maps close to aesthetic maps and far to non-aesthetic maps are sought. Self-organizing maps are used to ascertain whether the so-generated maps naturally cluster together with aesthetic maps, as well as to provide a qualitative assessment of the ability of each set of features to characterize the latter.