Modeling player experience in Super Mario Bros

This paper investigates the relationship between level design parameters of platform games, individual playing characteristics and player experience. The investigated design parameters relate to the placement and sizes of gaps in the level and the existence of direction changes; components of player experience include fun, frustration and challenge. A neural network model that maps between level design parameters, playing behavior characteristics and player reported emotions is trained using evolutionary preference learning and data from 480 platform game sessions. Results show that challenge and frustration can be predicted with a high accuracy (77.77% and 88.66% respectively) via a simple single-neuron model whereas model accuracy for fun (69.18%) suggests the use of more complex non-linear approximators for this emotion. The paper concludes with a discussion on how the obtained models can be utilized to automatically generate game levels which will enhance player experience.

[1]  Dev Kumar Bose,et al.  Mihalyi Csikszentmihalyi. Flow: The Psychology of Optimal Experience , 2008 .

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

[3]  Manuel Mejía-Lavalle,et al.  Power System Database Feature Selection Using a Relaxed Perceptron Paradigm , 2006, MICAI.

[4]  Michael Mateas,et al.  Authoring Interactive Narratives with Declarative Optimization-Based Drama Management , 2006, AIIDE.

[5]  Michael Mateas,et al.  Procedural Level Design for Platform Games , 2006, AIIDE.

[6]  Katherine Isbister,et al.  Game Usability: Advancing the Player Experience , 2008 .

[7]  Georgios N. Yannakakis,et al.  TOWARDS OPTIMIZING ENTERTAINMENT IN COMPUTER GAMES , 2007, Appl. Artif. Intell..

[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]  Julian Togelius,et al.  Optimization of Platform Game Levels for Player Experience , 2009, AIIDE.

[10]  David L. Roberts,et al.  Declarative optimization-based drama management in interactive fiction , 2006, IEEE Computer Graphics and Applications.

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

[12]  William V. Wright,et al.  A Theory of Fun for Game Design , 2004 .

[13]  Julian Togelius,et al.  An experiment in automatic game design , 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

[14]  Julian Togelius,et al.  Towards automatic personalised content creation for racing games , 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

[15]  Joe Marks,et al.  Automatic Design of Balanced Board Games , 2007, AIIDE.

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

[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]  Cameron Browne,et al.  Automatic generation and evaluation of recombination games , 2008 .

[19]  Chris Bateman,et al.  21st Century Game Design , 2005 .