Procedural content generation (PCG) is a method of content creation entirely or partially done by computers. PCG is popularly employed in game development to produce game content, such as maps and levels. Representative examples of games using PCG are Rogue (1998), which introduced the roguelike genre, and No Man’s Sky (2016), which generated whole worlds with fauna and flora. PCG may generate final contents, ready to be added to a game, or intermediate contents, which might be polished by human designers or work as an input level sketch to be interpreted by a level translator. In this paper, we survey the current state of procedural dungeon generation (PDG) research, a PCG subarea, applied in the context of games. For each work we selected in this survey, we examined and compared how they created game features, what type of level structure and representation they propose, which content generation strategy they applied, and, finally, we classify them according to the taxonomy of procedural content generation proposed by Togelius et al. (2016). The most relevant findings of our survey are: (1) PDG for 3D levels has been little explored; (2) few works supported levels with barriers, a game mechanic which temporarily blocks the player progression, and; (3) mixed-initiative approaches, i.e., software that helps human designers by making suggestions to the levels being created, are little explored.
Adversarial Reinforcement Learning for Procedural Content Generation
