Polymorph: A Model for Dynamic Level Generation


  • Martin Jennings-Teats University of California, Santa Cruz
  • Gillian Smith University of California, Santa Cruz
  • Noah Wardrip-Fruin University of California, Santa Cruz




dynamic difficulty adjustment, procedural level generation


Players begin games at different skill levels and develop their skill at different rates—so that even the best-designed games are uninterestingly easy for some players and frustratingly difficult for others. A proposed answer to this challenge is Dynamic Difficulty Adjustment (DDA), a general category of approaches that alter games during play, in response to player performance. However, nearly all these techniques are focused on basic parameter tweaking, while the difficulty of many games is connected to aspects that are more challenging to adjust dynamically, such as level design. Further, most DDA techniques are based on designer intuition, which may not reflect actual play patterns. Responding to these challenges, we have created Polymorph, which employs techniques from level generation and machine learning to understand level difficulty and player skill, dynamically constructing levels for a 2D platformer game with continually-appropriate challenge. We present the results of the user study on which Polymorph's model of level difficulty is based, as well as a discussion of the unique features of the model. We believe Polymorph creates a play experience that is unique because the changes are both personalized and structural, while also providing an example of a new application of machine learning to aid game design.




How to Cite

Jennings-Teats, M., Smith, G., & Wardrip-Fruin, N. (2010). Polymorph: A Model for Dynamic Level Generation. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 6(1), 138-143. https://doi.org/10.1609/aiide.v6i1.12417