When players quit (playing scrabble)

Brent Harrison, David L. Roberts

Producción científica: Conference contributionrevisión exhaustiva

6 Citas (Scopus)

Resumen

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

Idioma originalEnglish
Título de la publicación alojadaProceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012
Páginas154-159
Número de páginas6
DOI
EstadoPublished - 2012
Evento8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012 - Stanford, CA, United States
Duración: oct 8 2012oct 12 2012

Serie de la publicación

NombreProceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012

Conference

Conference8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012
País/TerritorioUnited States
CiudadStanford, CA
Período10/8/1210/12/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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