GAN-Based Content Generation of Maps for Strategy Games
This addresses the need for efficient and appealing map generation in strategy games, though it appears incremental as it applies existing GAN methods to a new domain.
The paper tackled the problem of generating realistic maps for strategy games, which are time-consuming to create manually and often look unnatural with traditional techniques, by proposing a GAN-based model that successfully produced realistic-looking maps as shown in empirical evaluations.
Maps are a very important component of strategy games, and a time-consuming task if done by hand. Maps generated by traditional PCG techniques such as Perlin noise or tile-based PCG techniques look unnatural and unappealing, thus not providing the best user experience for the players. However it is possible to have a generator that can create realistic and natural images of maps, given that it is trained how to do so. We propose a model for the generation of maps based on Generative Adversarial Networks (GAN). In our implementation we tested out different variants of GAN-based networks on a dataset of heightmaps. We conducted extensive empirical evaluation to determine the advantages and properties of each approach. The results obtained are promising, showing that it is indeed possible to generate realistic looking maps using this type of approach.