People enjoy encounters with generative software, but rarely are they encouraged to interact with, understand or engage with it. In this paper we define the term 'PCG-based game', and explain how this concept follows on from the idea of an AI-based game. We look at existing examples of games which foreground their AI, put forward a methodology for designing PCG-based games, describe some example case study designs for PCG-based games, and describe lessons learned during this process of sketching and developing ideas.
Elsewhere, work in @cite_13 or in @cite_8 show the relationship between users and generative systems, especially in the context of games or game-like applications. @cite_8 sheds light on, for example, how the presentation of generated content affects our perception of the system which generated it. Games like allow us to watch the slow creation process of a world, which may makes us feel differently about the quality and intelligence of the system than if it had appeared out of nowhere.
@cite_13 @cite_8
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In this paper, we show that personalized levels can be automatically generated for platform games. We build on previous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learning, based on questionnaires administered to players after playing different levels. The contributions of the current paper are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adaptation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players. The common misconception among non-specialists is that a computer program can only perform tasks which the programmer knows how to perform (albeit much faster). This leads to a belief that if an artificial system exhibits creative behavior, it only does so because it is leveraging the programmer’s creativity. We review past efforts to evaluate creative systems and identify the biases against them. As evidenced in our case studies, a common bias indicates that creativity requires both intelligence and autonomy. We suggest that in order to overcome this skepticism, separation of programmer and program is crucial and that the program must be the responsible party for convincing the observer of this separation.