Procedural Content Generation (PCG) is widely used in the game development industry to randomly generate various types of content. However, ensuring quality PCG is very hard, because of its inherent subjectivity; defining exactly what makes for enjoyable gameplay is difficult and depends on a wide range of different factors. We have therefore explored the concept of PCG, specifically procedural level generation, to gather observations and insights, and we present these in the report. The exploration has been performed by creating a simple game called Fluky and developing six different level generation algorithms for it. Among these observations are the importance of identifying levels of abstraction for an algorithm, and several aspects that may influence how enjoyable the generated content can be. Our resulting conclusions are very subjective, but not without merit, due to the extensive exploration and evaluation that has taken place.
AB Procedural Content Generation (PCG) is widely used in the game development industry to randomly generate various types of content. However, ensuring quality PCG is very hard, because of its inherent subjectivity; defining exactly what makes for enjoyable gameplay is difficult and depends on a wide range of different factors. We have therefore explored the concept of PCG, specifically procedural level generation, to gather observations and insights, and we present these in the report. The exploration has been performed by creating a simple game called Fluky and developing six different level generation algorithms for it. Among these observations are the importance of identifying levels of abstraction for an algorithm, and several aspects that may influence how enjoyable the generated content can be. Our resulting conclusions are very subjective, but not without merit, due to the extensive exploration and evaluation that has taken place.