TY - GEN
T1 - Evolving personalized content for super mario bros using grammatical evolution
AU - Shaker, Noor
AU - Yannakakis, Georgios N.
AU - Togelius, Julian
AU - Nicolau, Miguel
AU - O'neill, Michael
PY - 2012
Y1 - 2012
N2 - 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 gameplay 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 optimize 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.
AB - 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 gameplay 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 optimize 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.
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M3 - Conference contribution
AN - SCOPUS:84883079945
SN - 9781577355823
T3 - Proceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012
SP - 75
EP - 80
BT - Proceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012
T2 - 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2012
Y2 - 8 October 2012 through 12 October 2012
ER -