Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution

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@InProceedings{conf/aiide/shakerYTNO2012,
  author =       "Noor Shaker and Georgios N. Yannakakis and 
                 Julian Togelius and Miguel Nicolau and Michael O'Neill",
  title =        "Evolving Personalized Content for Super Mario Bros
                 Using Grammatical Evolution",
  booktitle =    "Eighth AAAI Conference on Artificial Intelligence and
                 Interactive Digital Entertainment (AIIDE-12)",
  year =         "2012",
  editor =       "Mark Riedl and Gita Sukthankar",
  pages =        "75--80",
  address =      "Stanford, USA",
  month =        oct # " 8-12",
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-57735-582-3",
  URL =          "http://www.aaai.org/Library/AIIDE/aiide12contents.php",
  URL =          "http://www.aaai.org/ocs/index.php/AIIDE/AIIDE12/paper/view/5448/5700.pdf",
  size =         "6 pages",
  abstract =     "Adapting game content to a particular player's needs
                 and expertise constitutes an important aspect in game
                 design. Most research in this direction has focused on
                 adapting game difficulty to keep the player engaged in
                 the game. Dynamic difficulty adjustment, however,
                 focuses on one aspect of the game play experience by
                 adjusting the content to increase or decrease perceived
                 challenge. In this paper, we introduce a method for
                 automatic level generation for the platform game Super
                 Mario Bros using grammatical evolution. The grammatical
                 evolution-based level generator is used to generate
                 player-adapted content by employing an adaptation
                 mechanism as a fitness function in grammatical
                 evolution to optimise the player experience of three
                 emotional states: engagement, frustration and
                 challenge. The fitness functions used are models of
                 player experience constructed in our previous work from
                 crowd-sourced gameplay data collected from over 1500
                 game sessions.",
}

Genetic Programming entries for Noor Shaker Georgios N Yannakakis Julian Togelius Miguel Nicolau Michael O'Neill

Citations