Predicting skill from gameplay input to a first-person shooter

One way to make video games more attractive to a wider audience is to make them adaptive to players. The preferences and skills of players can be determined in a variety of ways, but should be done as unobtrusively as possible to keep the player immersed. This paper explores how gameplay input recorded in a first-person shooter can predict a player's ability. As these features were able to model a player's skill with 76% accuracy, without the use of game-specific features, we believe their use would be transferable across similar games within the genre.

[1]  Georgios N. Yannakakis,et al.  Real-Time Game Adaptation for Optimizing Player Satisfaction , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[2]  Georgios N. Yannakakis,et al.  Game and Player Feature Selection for Entertainment Capture , 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

[3]  A. Elo.  The rating of chessplayers, past and present , 1978 .

[4]  Julian Togelius,et al.  Predicting player behavior in Tomb Raider: Underworld , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[5]  Rafael Bidarra,et al.  In Press: Ieee Transactions on Computational Intelligence and Ai in Games Adaptivity Challenges in Games and Simulations: a Survey , 2022 .

[6]  Julian Togelius,et al.  Feature analysis for modeling game content quality , 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

[7]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[8]  Bruce Phillips,et al.  Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems , 2008, CHI.

[9]  Vincent Corruble,et al.  Extending Reinforcement Learning to Provide Dynamic Game Balancing , 2005 .

[10]  Georgios N. Yannakakis,et al.  Ranking vs. Preference: A Comparative Study of Self-reporting , 2011, ACII.

[11]  David F. Redmiles,et al.  Extracting usability information from user interface events , 2000, CSUR.

[12]  Arthur Tay,et al.  Dynamic Game Difficulty Scaling Using Adaptive Behavior-Based AI , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[14]  Bernadette Bouchon-Meunier,et al.  Characterizing player's experience from physiological signals using fuzzy decision trees , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[15]  Noah Wardrip-Fruin,et al.  Polymorph: dynamic difficulty adjustment through level generation , 2010, PCGames@FDG.

[16]  David Laibson,et al.  Experienced Utility and Objective Happiness: A Moment-Based Approach , 2001 .

[17]  Georgios N. Yannakakis,et al.  Preference Learning for Cognitive Modeling: A Case Study on Entertainment Preferences , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[18]  H. Jaap van den Herik,et al.  Games as personality profiling tools , 2011, 2011 IEEE Conference on Computational Intelligence and Games (CIG'11).

[19]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[20]  Simon Colton,et al.  Unsupervised Modeling of Player Style With LDA , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

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