An approach to level design using procedural content generation and difficulty curves

Level design is an art which consists of creating the combination of challenge, competition, and interaction that players call fun and involves a careful and deliberate development of the game space. When working with procedural content generation, it is necessary to review how the game designer sets the change in difficulty throughout the different levels. In this paper we present a procedural level generator that can be used for different games and is based on a genetic algorithm. We define a fitness function that does not depend on the game or content type. This function calculates the difference between the difficulty curve defined by the designer and the difficulty curve calculated for the candidate content, so the best content is the one whose difficulty curve best fits the desired curve. To design our generator, we rely on the concept of flow, theories of fun and game design.

[1]  Tracy Fullerton,et al.  Game Design Workshop: A Playcentric Approach to Creating Innovative Games, Third Edition , 2014 .

[2]  H. Jaap van den Herik,et al.  Rapid and Reliable Adaptation of Video Game AI , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[3]  Philippe Pasquier,et al.  Towards a Generic Framework for Automated Video Game Level Creation , 2010, EvoApplications.

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

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

[6]  Á. Kuri-Morales Solution of Simultaneous Non-Linear Equations using Genetic Algorithms , 2002 .

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

[8]  Rafael Bidarra,et al.  Integrating procedural generation and manual editing of virtual worlds , 2010, PCGames@FDG.

[9]  Matthias Rauterberg,et al.  Difficulty Scaling through Incongruity , 2008, AIIDE.

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

[11]  Julian Togelius,et al.  Towards multiobjective procedural map generation , 2010, PCGames@FDG.

[12]  Julian Togelius,et al.  Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution , 2012, AIIDE.

[13]  I. Sprinkhuizen-Kuyper,et al.  DIFFICULTY SCALING OF GAME AI , 2004 .

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

[15]  Georgios N. Yannakakis,et al.  Real-time challenge balance in an RTS game using rtNEAT , 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

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

[17]  Hiroyuki Iida,et al.  A metric for entertainment of boardgames: its implication for evolution of chess variants , 2002, IWEC.