• DocumentCode
    3683559
  • Title

    Player-adaptive Spelunky level generation

  • Author

    David Stammer;Hochschule Mannheim;Tobias Günther;Mike Preuss

  • Author_Institution
    Cooperative State University Baden-Wuertemberg
  • fYear
    2015
  • Firstpage
    130
  • Lastpage
    137
  • Abstract
    Procedural Content Generation (PCG) is nowadays widely applied to many different aspects of computer games. However, it can do more than to assist level designers during game creation. It can generate personalized levels according to the tastes and abilities of players online. This has already been demonstrated for (largely 1D) scrolling games and we show in this work how personalized, difficulty-adjusted levels can be generated for the more complex 2D platformer Spelunky. As direct and indirect player feedback is taken into account, the method may be filed under the Experience-Driven PCG approach. Our approach is based on a rather generic rule set that may also be transferred to similar games. We also present a user study showing that most users appreciate the online adaptation but are especially critical about making the game easier to play at any time.
  • Keywords
    "Games","Artificial intelligence","Weapons","Information services","Electronic publishing","Internet","Genetic algorithms"
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Games (CIG), 2015 IEEE Conference on
  • ISSN
    2325-4270
  • Electronic_ISBN
    2325-4289
  • Type

    conf

  • DOI
    10.1109/CIG.2015.7317948
  • Filename
    7317948