An Author-Centric Approach to Procedural Content Generation

  • Rui Craveirinha
  • Lucas Santos
  • Licínio Roque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8253)


This paper describes an alternative approach for videogame procedural content generation focused on providing authors direct control on what gameplay ensues from the generated content. An architecture is proposed that allows designers to define, beforehand, target gameplay indicators, and then generates content for an existing base-design that achieves those same indicators in actual gameplay sessions with human players. Besides providing a description of this architecture, a trial intent on giving evidence of the approach’s feasibility is presented. This experiment used an altered version of ‘Infinite Mario Bros.’ level generator, built to evolve design parameters so as to achieve 3 target gameplay indicators. Employing a Genetic Algorithm in generation of new parameter values, and using 25 players to test the end results, the platform was able to generate parameters that achieved, with precision, the values for those indicators. This result provides evidence of the approach’s feasibility, hinting at its potential use for real-life design processes.


Game Design Player Experience Human Player Target Indicator Game Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Drachen, A., Canossap, A.: Towards gameplay analysis via gameplay metrics. In: Proceedings of the 13th International MindTrek Conference: Everyday Life in the Ubiquitous Era, MindTrek 2009, pp. 202–209. ACM, New York (2009) ISBN 978-1-60558-633-5, doi:
  2. Gladwell, M.: Blink: The Power of Thinking Without Thinking. Little, Brown and Company (2005)Google Scholar
  3. Gombrich, E.H.: The story of art. Phaidon (2009)Google Scholar
  4. Hassenzahl, M.: User experience and experience design. In: Soegaard, M., Dam, R.F. (eds.) Encyclopedia of Human-Computer Interaction. (2011)Google Scholar
  5. Haupt, R.L., Haupt, S.E.: Practical genetic algorithms, 2nd edn. John Wiley & Sons, Inc. (2004)Google Scholar
  6. Kim, J.H., Gunn, D.V., Schuh, E., Phillips, B., Pagulayan, R.J., Wixon, D.R.: Tracking real-time user experience (true): A comprehensive instrumentation solution for complex systems. In: Computer Human Interaction, pp. 443–452 (2008), doi:10.1145/1357054.1357126Google Scholar
  7. Kujala, S.: User involvement: A review of the benefits and challenges. Behaviour & Information Technology 22(1), 1–16 (2003), CrossRefGoogle Scholar
  8. Pedersen, C., Togelius, J., Yannakakis, G.N.: Modeling player experience for content creation. IEEE Transactions on Computational Intelligence and AI in Games 2(1), 54–67 (2010) ISSN 1943-068X, doi:10.1109/TCIAIG.2010.2043950CrossRefGoogle Scholar
  9. Pedersen, C., Togelius, J., Yannakakis, G.N.: Modeling player experience in super mario bros. In: Proceedings of the 5th International Conference on Computational Intelligence and Games, CIG 2009, pp. 132–139. IEEE Press, Piscataway (2009) ISBN 978-1-4244-4814-2,
  10. Shaker, N., Yannakakis, G.N., Togelius, J.: Towards Automatic Personalized Content Generation for Platform Games. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE. AAAI Press (October 2010)Google Scholar
  11. Steen, M., Kuijt-Evers, L., Klok, J.: Early user involvement in research and design projects – A review of methods and practices. In: 23rd EGOS Colloquium (European Group for Organizational Studies) (2007)Google Scholar
  12. Yannakakis, G., Hallam, J.: A generic approach for generating interesting interactive pac-man opponents. In: Proceedings of the IEEE Symposium on Computational Intelligence and Games, pp. 94–101 (2005)Google Scholar
  13. Yannakakis, G.N., Hallam, J.: Evolving opponents for interesting interactive computer games. In: Simulation of Adaptive Behavior (2004)Google Scholar
  14. Yannakakis, G.N., Hallam, J.: Towards optimizing entertainment in computer games. Appl. Artif. Intell. 21, 933–971 (2007) ISSN 0883-9514,, doi:10.1080/08839510701527580CrossRefGoogle Scholar
  15. Yannakakis, G.N., Togelius, J.: Experience-driven procedural content generation. IEEE Transactions on Affective Computing 99(PrePrints) (2011) ISSN 1949-3045,

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Rui Craveirinha
    • 1
  • Lucas Santos
    • 2
  • Licínio Roque
    • 1
  1. 1.Department of Informatics Engineering, Faculty of Sciences and TechnologyUniversity of CoimbraCoimbraPortugal
  2. 2.Universidade Estadual da BahiaBahiaBrasil

Personalised recommendations