The case for dynamic difficulty adjustment in games
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
Rhythm-based level generation for 2D platformers
Proceedings of the 4th International Conference on Foundations of Digital Games
Modeling player experience in super mario bros
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
A semantic generation framework for enabling adaptive game worlds
Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology
Digging deeper into platform game level design: session size and sequential features
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Integrated system for automatic platform game level creation with difficulty and content adaptation
ICEC'12 Proceedings of the 11th international conference on Entertainment Computing
Procedural content generation for games: A survey
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Enhancing level difficulty and additional content in platform videogames through graph analysis
ACE'12 Proceedings of the 9th international conference on Advances in Computer Entertainment
GOALS: generator of adaptive learning scenarios
International Journal of Learning Technology
Using gameplay semantics to procedurally generate player-matching game worlds
Proceedings of the The third workshop on Procedural Content Generation in Games
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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 present Polymorph, which employs techniques from level generation and machine learning to understand game component difficulty and player skill, dynamically constructing a 2D platformer game with continually-appropriate challenge. We believe this will create a play experience that is unique because the changes are both personalized and structural, while also providing an example of a promising new research and development approach.