![]() ![]() We hope that this approach could be the basis for developing useful qualitative tools for comparing PCG systems to help designers better understand and optimize their generators. It is to this area that this paper aims to make a contribution, by exploring the utility of data compression algorithms in compressing the generative spaces of PCG systems. An area in which progress has been comparatively slow is the development of generalisable approaches for comparing alternative PCG systems, especially in terms of their generative spaces. The past decade has seen a rapid increase in the level of research interest in procedural content generation (PCG) for digital games, and there are now numerous research avenues focused on new approaches for driving and applying PCG systems. ![]()
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