Towards Automatic Personalized Content Generation for Platform Games


  • Noor Shaker IT University of Copenhagen
  • Georgios Yannakakis IT University of Copenhagen
  • Julian Togelius IT University of Copenhagen



Game Adaptation, Player Modeling


In this paper, we show that personalized levels can be auto- matically generated for platform games. We build on previ- ous 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 learn- ing, based on questionnaires administered to players after playing different levels. The contributions of the current pa- per 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 adap- tation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players.




How to Cite

Shaker, N., Yannakakis, G., & Togelius, J. (2010). Towards Automatic Personalized Content Generation for Platform Games. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 6(1), 63-68.