Context-Aware Petri Net for Dynamic Procedural Content Generation in Role-Playing Game

In most cases, the story or plot of popular role-playing games is constructed by professional designers as a main content. However, manual design of game content has a limitation in the quantitative aspect; it requires a large amount of time and effort. As game consumers want more diverse and rich contents, it is not easy to satisfy these needs with manual design, so procedural content generation is actively exploited to automatically generate game contents. In this paper, we propose a quest generation method using Petri net modules. A quest depending on the player's involvement or type determined by Bayesian network is generated by Petri net. Never Winter Night is used as a game platform to show the feasibility of the proposed method. In future works, we will collect players' playing history and evaluate the performance of Bayesian network inference for a player's type. Also, we will apply the proposed method to an open-source platform for a complete automatic quest generation system.