Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution
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gp-bibliography.bib Revision:1.8051
- @InProceedings{conf/aiide/shakerYTNO2012,
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author = "Noor Shaker and Georgios N. Yannakakis and
Julian Togelius and Miguel Nicolau and Michael O'Neill",
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title = "Evolving Personalized Content for {Super Mario Bros}
Using Grammatical Evolution",
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booktitle = "Eighth AAAI Conference on Artificial Intelligence and
Interactive Digital Entertainment (AIIDE-12)",
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year = "2012",
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editor = "Mark Riedl and Gita Sukthankar",
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pages = "75--80",
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address = "Stanford, USA",
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month = oct # " 8-12",
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publisher = "AAAI",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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isbn13 = "978-1-57735-582-3",
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URL = "http://www.aaai.org/Library/AIIDE/aiide12contents.php",
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URL = "http://www.aaai.org/ocs/index.php/AIIDE/AIIDE12/paper/view/5448/5700.pdf",
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size = "6 pages",
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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