Adaptive difficulty scales for Parkour games

Mobile computer games have become increasingly popular in recent years. A major factor for successful game development is the adequate control of the game's difficulty. This paper discusses justification for dynamic difficulty adaption for Parkour games. It presents an adaptive mechanism for difficulty adjustment in response to the player's run-time performance in the single player mode. The mechanism is based on game content generation techniques, considering constrains for mobile screens. Both the functionality of the game's objects and the player's psychological and behavioral inclinations are taken into consideration. Our preliminary experiment shows that game experiences are significantly enhanced with the adaption mechanism. Parkour game design customized for various playergroups.Adaptive difficulty for game development.Experimental evaluation for various profiles of players.

[1]  Guy Hawkins,et al.  Dynamic Difficulty Balancing for Cautious Players and Risk Takers , 2012, Int. J. Comput. Games Technol..

[2]  Gavriel Salvendy,et al.  Effects of different scenarios of game difficulty on player immersion , 2010, Interact. Comput..

[3]  Zoran Popovic,et al.  A case study of expressively constrainable level design automation tools for a puzzle game , 2012, FDG.

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

[5]  Erik Andersen.  Optimizing adaptivity in educational games , 2012, FDG.

[6]  Christoph Klimmt,et al.  Explaining the enjoyment of playing video games: the role of competition , 2003, ICEC.

[7]  Matthias Rauterberg,et al.  Incongruity-Based Adaptive Game Balancing , 2009, ACG.

[8]  Edward Byrne.  Game Level Design , 2004 .

[9]  Lennart E. Nacke,et al.  Affective Ludology: Scientific Measurement of User Experience in Interactive Entertainment , 2009 .

[10]  Philip H. Mirvis.  Flow: The Psychology of Optimal Experience , 1991 .

[11]  Pieter Spronck,et al.  Player behavioural modelling for video games , 2012, Entertain. Comput..

[12]  Thomas W. Malone,et al.  What makes things fun to learn? heuristics for designing instructional computer games , 1980, SIGSMALL '80.

[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]  Julian Togelius,et al.  What is procedural content generation?: Mario on the borderline , 2011, PCGames '11.

[16]  Nicole Fruehauf.  Flow The Psychology Of Optimal Experience , 2016 .

[17]  Peta Wyeth,et al.  GameFlow: a model for evaluating player enjoyment in games , 2005, CIE.

[18]  Julian Togelius,et al.  Modeling player experience in Super Mario Bros , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[19]  Julian Togelius,et al.  Experience-Driven Procedural Content Generation , 2011, IEEE Trans. Affect. Comput..

[20]  Mark O. Riedl,et al.  Generating and Adapting Game Mechanics , 2014 .

[21]  Stéphane Natkin,et al.  Difficulty in videogames: an experimental validation of a formal definition , 2011, Advances in Computer Entertainment Technology.

[22]  Michael Nitsche.  Designing Procedural Game Spaces : A Case Study , 2006 .

[23]  Arnav Jhala,et al.  Using Data Mining to Model Player Experience , 2011 .

[24]  Michael Mateas,et al.  Towards Automated Game Design , 2007, AI*IA.

[25]  Julian Togelius,et al.  Search-Based Procedural Content Generation: A Taxonomy and Survey , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[26]  Ed Byrne,et al.  Game Level Design (Game Development Series) , 2004 .