Level Difficulty and Player Skill Prediction in Human Computation Games

@inproceedings{Sarkar2017LevelDA,
  title={Level Difficulty and Player Skill Prediction in Human Computation Games},
  author={Anurag Sarkar and Seth Cooper},
  booktitle={AIIDE},
  year={2017}
}
Human computation games (HCGs) often suffer from low player retention. This may be due to the constraints placed on level and game design from the real-world application of the game. Previous work has suggested using player rating systems (such as Elo, Glicko-2, or TrueSkill) as a basis for matchmaking between HCG levels and players, as a means to improve difficulty balancing and thus player retention. Such rating systems typically start incoming entities with a default rating. However, when… CONTINUE READING