Towards explainable prediction of player frustration in video games.

Max Wolterink, Sander Bakkes

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Frustration is a key concept in retaining a player interest in both commercial and applied games. In a HCI context, frustration is often seen as a purely negative phenomenon. However, for games to be interesting some amount of frustrating has to be present. As such, dynamically adjusting game elements to ensure optimal frustration levels can be a valuable way to increase player retention. A first step towards such a system is an accurate classifier of frustration. To date, most attempts at frustration classification use models that are relatively hard for a human to understand. In this paper an attempt will be made at creating an explainable predictor of player frustration. To accomplish this, the frustration-aggression theory was used to identify a number of key components that determine the severity of a frustrated response. 135 participants were asked to play a series of Pac-Man levels while being asked about the frustration components. Gameplay features, participant behaviour and participant responses were gathered and used as a dataset to train a number of random forest classifiers. The classifiers were trained to predict player frustration, with accuracy ranging from 66.3% to 83.1% depending on the amount of frustration classes used. Accuracy dropped significantly when excluding participant responses on frustration component questions from the dataset. Furthermore, feature importance analysis revealed the overwhelming importance of the Repeated Failures component, as well as the relatively low importance of all in-game variables. These results suggest that the currently used variable set might not accurately represent the components of frustration. A possible avenue for future research could be the discovery of accurate metrics for these internal component perceptions.

Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on the Foundations of Digital Games, FDG 2021
EditorsAllan Fowler, Johanna Pirker, Alesandro Alessandro Canossa, Ali Ali Arya, Casper Harteveld
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450384223
DOIs
Publication statusPublished - 3 Aug 2021
Event16th International Conference on the Foundations of Digital Games, FDG 2021 - Virtual, Online, Canada
Duration: 2 Aug 20216 Aug 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference16th International Conference on the Foundations of Digital Games, FDG 2021
Country/TerritoryCanada
CityVirtual, Online
Period2/08/216/08/21

Keywords

  • Frustration
  • Player modelling
  • Random Forest Classifier
  • User Experience

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