Adaptive Horror Games

Fear is a basic human emotion that can be triggered by different situations, which vary from person to person. However, game developers usually design horror games based on a general knowledge about what most players fear, which does not guarantee a satisfying horror experience for everyone. When a horror game aims at intensifying the fear evoked in individual players, having useful information about the fears of the current player is vital to promote more frightening experiences. This project explores new methods to create adaptive horror games by using player modeling techniques to identify what individual players fear and adapt the content of the game to intensify the fear evoked in players.

 
Status:

Contributions:
  • We present a new approach to create adaptive virtual reality horror games.
  • The proposed method uses machine learning techniques to identify what players fear.
  • Our method can adapt horror games to intensify the fear evoked in players.
  • Results suggest that our method can positively improve the overall game experience.
Publications:
  • Edirlei Soares de Lima; Bruno Silva; Gabriel Galam. Adaptive Virtual Reality Horror Games Based on Machine Learning and Player Modeling. Entertainment Computing, Volume 43, August 2022, 100515, 2022. [DOI]

  • Edirlei Soares de Lima; Bruno Silva; Gabriel Galam. Towards the Design of Adaptive Virtual Reality Horror Games A Model of Players’ Fears Using Machine Learning and Player Modeling. Proceedings of the XIX Brazilian Symposium on Computer Games and Digital Entertainment (SBGames 2020), Recife, Brazil, pp. 366-372, 2020. [PDF][DOI]

  • Thainá Cristina Demarque; Edirlei Soares de Lima. Auditory Hallucination:  Audiological Perspective for Horror Games. Proceedings of the XII Brazilian Symposium on Computer Games and Digital Entertainment (SBGames 2013), São Paulo, Brazil, October 2013. [PDF]
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