Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1814256.1814267 | Proceedings of the 2010 Workshop on Procedural Content Generation in Games |
Keywords | DocType | Citations |
games,level design,best-designed game,level generation,different skill level,player skill,dynamic difficulty adjustment,dda technique,play experience,continually-appropriate challenge,different rate,procedural content generation,actual play pattern,game component difficulty,polymorphism,machine learning | Conference | 39 |
PageRank | References | Authors |
1.83 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Jennings-Teats | 1 | 55 | 3.08 |
| Gillian Smith | 2 | 369 | 28.59 |
| Noah Wardrip-Fruin | 3 | 298 | 52.31 |