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Communication Dans Un Congrès Année : 2017

Predicting Human Computation Game Scores with Player Rating Systems

Michael Williams
  • Fonction : Auteur
  • PersonId : 1031222
Anurag Sarkar
  • Fonction : Auteur
  • PersonId : 1031223
Seth Cooper
  • Fonction : Auteur
  • PersonId : 1031224

Résumé

Human computation games aim to apply human skill toward real-world problems through gameplay. Such games may suffer from poor retention, potentially due to the constraints that using pre-existing problems place on game design. Previous work has proposed using player rating systems and matchmaking to balance the difficulty of human computation games, and explored the use of rating systems to predict the outcomes of player attempts at levels. However, these predictions were win/loss, which required setting a score threshold to determine if a player won or lost. This may be undesirable in human computation games, where what scores are possible may be unknown. In this work, we examined the use of rating systems for predicting scores, rather than win/loss, of player attempts at levels. We found that, except in cases with a narrow range of scores and little prior information on player performance, Glicko-2 performs favorably to alternative methods.
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hal-01771305 , version 1 (19-04-2018)

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Michael Williams, Anurag Sarkar, Seth Cooper. Predicting Human Computation Game Scores with Player Rating Systems. 16th International Conference on Entertainment Computing (ICEC), Sep 2017, Tsukuba City, Japan. pp.284-289, ⟨10.1007/978-3-319-66715-7_31⟩. ⟨hal-01771305⟩
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