AAAI Publications, Eighth Artificial Intelligence and Interactive Digital Entertainment Conference

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When Players Quit (Playing Scrabble)
Brent Harrison, David Roberts

Last modified: 2012-10-07


What features contribute to player enjoyment and player retentionhas been a popular research topic in video games research;however, the question of what causes players to quit agame has received little attention by comparison. In this paper,we examine 5 quantitative features of the game Scrabblesquein order to determine what behaviors are predictors ofa player prematurely ending a game session. We identified afeature transformation that notably improves prediction accuracy.We used a naive Bayes model to determine that there areseveral transformed feature sequences that are accurate predictorsof players terminating game sessions before the endof the game.We also identify several trends that exist in thesesequences to give a more general idea as to what behaviorsare characteristic early indicators of players quitting.


Player Modeling; Artificial Intelligence

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