Review of intrinsic motivation in simulation-based game testing

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

Researchers

Research units

  • University of London

Abstract

This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored.

Details

Original languageEnglish
Title of host publicationProceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Subtitle of host publicationEngage with CHI
Publication statusPublished - 20 Apr 2018
MoE publication typeA4 Article in a conference publication
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Palais des Congrès de Montréal, Montreal, Canada
Duration: 21 Apr 201826 Apr 2018
Conference number: 36
https://chi2018.acm.org/
https://chi2018.acm.org

Conference

ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleCHI
CountryCanada
CityMontreal
Period21/04/201826/04/2018
Internet address

    Research areas

  • Artificial intelligence, Emotion, Game testing, Intrinsic motivation, Player modeling

ID: 21505339