Modelling Player Preferences in AR Mobile Games

In this paper, we use preference learning techniques to model players’ emotional preferences in an AR mobile game. This exploratory study uses player behaviour to make these preference predictions. The described techniques successfully predict players’ frustration and challenge levels with high accuracy while all other preferences tested (boredom, excitement and fun) perform better than random chance. This paper describes the AR treasure hunt game we developed, the user study conducted to collect player preference data, analysis performed, and preference learning techniques applied to model this data. This work is motivated to personalize players’ experiences by using these computational models to optimize content creation and game balancing systems in these environments. The generality of our technique, limitations, and usability as a tool for personalization of AR mobile games is discussed.

[1]  David Howard,et al.  A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.

[2]  Georgios N. Yannakakis,et al.  Entertainment modeling through physiology in physical play , 2008, Int. J. Hum. Comput. Stud..

[3]  Andrea Bonarini,et al.  Modeling enjoyment preference from physiological responses in a car racing game , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[4]  Julian Frommel,et al.  KickAR: Exploring Game Balancing Through Boosts and Handicaps in Augmented Reality Table Football , 2018, CHI.

[5]  Seth Rogers,et al.  Learning Subjective Functions with Large Margins , 2000, ICML.

[6]  Julian Togelius,et al.  Modeling Player Experience for Content Creation , 2010, IEEE Transactions on Computational Intelligence and AI in Games.

[7]  Mark O. Riedl,et al.  Mixed Reality Meets Procedural Content Generation in Video Games , 2021, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.

[8]  Regan L. Mandryk,et al.  A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies , 2007, Int. J. Hum. Comput. Stud..

[9]  Jonathan Linowes,et al.  Augmented Reality for Developers: Build practical augmented reality applications with Unity, ARCore, ARKit, and Vuforia , 2017 .

[10]  L. F. Barrett.  Solving the Emotion Paradox: Categorization and the Experience of Emotion , 2006, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[11]  Michael Garber-Barron,et al.  Using body movement and posture for emotion detection in non-acted scenarios , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[12]  Tingshao Zhu,et al.  Identifying Emotions from Non-Contact Gaits Information Based on Microsoft Kinects , 2018, IEEE Transactions on Affective Computing.

[13]  Julian Togelius,et al.  Experience-Driven Procedural Content Generation , 2011, IEEE Transactions on Affective Computing.

[14]  Anthony Steed,et al.  Automatic Recognition of Non-Acted Affective Postures , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).