Balance Trucks: Using Crowd-Sourced Data to Procedurally-Generate Gameplay within Mobile Games

Within the field of procedural content generation (PCG) research, the use of crowd-sensing data has, until now, primarily been used as a means of collecting information and generating feedback relating to player experience within games, and game aesthetics [1], [2]. However, crowd-sensing data can offer much more, supplying a seemingly untapped font of information which may be used within the creation of unique PCG game spaces or content, whilst providing a visible outlet for the dissemination of crowd-sensed material to users. This paper examines one such use of crowd-sensed data, the creation of a game which will reside within the CROWD4ROADS (C4RS) [3] application, SmartRoadSense (SRS) [4]. The authors will open with a brief discussion of PCG. Following this, an explanation of the features and aims of the SRS application will be provided. Finally, the paper will introduce 'Balance Trucks', the SRS game, discussing the concepts behind using crowd-sensed data within its design, its development and use of PCG.

[1]  Nuno Barreto,et al.  Computational Creativity in Procedural Content Generation: A State of the Art Survey , 2014 .

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

[3]  Alberto Carini,et al.  SmartRoadSense: Collaborative Road Surface Condition Monitoring , 2014 .

[4]  Julian Togelius,et al.  Super mario evolution , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[5]  Julian Togelius,et al.  Guest Editorial: Procedural Content Generation in Games , 2011, IEEE Trans. Comput. Intell. AI Games.

[6]  Joris Dormans,et al.  Adventures in level design: generating missions and spaces for action adventure games , 2010, PCGames@FDG.

[7]  Julian Togelius,et al.  Procedural Content Generation: Goals, Challenges and Actionable Steps , 2013, Artificial and Computational Intelligence in Games.

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

[9]  Mark J. Nelson,et al.  Design metaphors for procedural content generation in games , 2013, CHI.

[10]  Julian Togelius,et al.  A procedural procedural level generator generator , 2012, 2012 IEEE Conference on Computational Intelligence and Games (CIG).

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

[12]  Kenneth O. Stanley,et al.  Automatic Content Generation in the Galactic Arms Race Video Game , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[13]  Gillian Smith,et al.  Understanding procedural content generation: a design-centric analysis of the role of PCG in games , 2014, CHI.

[14]  Marjan Kuchaki Rafsanjani,et al.  A genetic approach in procedural content generation for platformer games level creation , 2017, 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC).

[15]  Alexandru Iosup,et al.  Procedural content generation for games: A survey , 2013, TOMCCAP.

[16]  Sérgio Oliveira,et al.  Adaptive content generation for games , 2017, 2017 24º Encontro Português de Computação Gráfica e Interação (EPCGI).

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

[18]  Julian Togelius,et al.  The 2009 Mario AI Competition , 2010, IEEE Congress on Evolutionary Computation.

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

[20]  Alessandro Bogliolo,et al.  Geospatial data aggregation and reduction in vehicular sensing applications: The case of road surface monitoring , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).