%0 Conference Proceedings
%T Predicting Human Computation Game Scores with Player Rating Systems
%+ Northeastern University [Boston]
%A Williams, Michael
%A Sarkar, Anurag
%A Cooper, Seth
%Z Part 6: Game Understanding
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th International Conference on Entertainment Computing (ICEC)
%C Tsukuba City, Japan
%Y Nagisa Munekata
%Y Itsuki Kunita
%Y Junichi Hoshino
%I Springer International Publishing
%3 Entertainment Computing – ICEC 2017
%V LNCS-10507
%P 284-289
%8 2017-09-18
%D 2017
%R 10.1007/978-3-319-66715-7_31
%K Human computation games
%K Player rating systems
%K Prediction
%K Elo
%K Glicko-2
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 14
%2 https://inria.hal.science/hal-01771305/document
%2 https://inria.hal.science/hal-01771305/file/978-3-319-66715-7_31_Chapter.pdf
%L hal-01771305
%U https://inria.hal.science/hal-01771305
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-ICEC
%~ IFIP-TC14
%~ IFIP-LNCS-10507