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Automatically Categorizing Procedurally Generated Content for Collecting Games

A potentially promising application for procedural content generation (PCG) is collecting games, i.e. games in which the player strives to collect as many classes of possible artifacts as possible from a diverse set. However, the challenge for PCG in collecting games is that procedurally generated content on its own does not fall into a predefined set of classes, leaving no concrete quantifiable measure of progress for players to follow. The main idea in this paper is to remedy this shortcoming by feeding a sample of such content into a self-organizing map (SOM) that then in effect generates as many categories as there are nodes in the SOM. Once thereby organized, any new content discovered by a player can be categorized simply by identifying the node most activate after its presentation. This approach is tested in this paper in the Petalz video game, where 80 categories for user-bred flowers are generated by a SOM, allowing players to track their progress in discovering all the ”species”that are now explicitly identified. The hope is that this idea will inspire more researchers in PCG to investigate applications to collecting games in the future.