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Cited by 166

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Modeling player experience in Super Mario Bros

Open access
Pedersen, Chris, Togelius, Julian, Yannakakis, Georgios N.·2009·2009 IEEE Symposium on Computational Intelligence and Games

Abstract

This paper investigates the relationship between level design parameters of platform games, individual playing characteristics and player experience. The investigated design parameters relate to the placement and sizes of gaps in the level and the existence of direction changes; components of player experience include fun, frustration and challenge. A neural network model that maps between level design parameters, playing behavior characteristics and player reported emotions is trained using evolutionary preference learning and data from 480 platform game sessions. Results show that challenge and frustration can be predicted with a high accuracy (77.77% and 88.66% respectively) via a simple single-neuron model whereas model accuracy for fun (69.18%) suggests the use of more complex non-linear approximators for this emotion. The paper concludes with a discussion on how the obtained models can be utilized to automatically generate game levels which will enhance player experience.

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Metadata

Authors
Pedersen, C., Togelius, J., & Yannakakis, G. N.
Venue
2009 IEEE Symposium on Computational Intelligence and Games
Publisher
IEEE
Published
2009-09
Pages
132-139
OpenAlex
W2141104189
Cited by
166
References
0
FWCI
10.58
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Modeling player experience in Super Mario Bros. https://app.cochat.ai/papers/10.1109/cig.2009.5286482
The paper
Pedersen, C., Togelius, J., & Yannakakis, G. N. (2009). Modeling player experience in Super Mario Bros. 2009 IEEE Symposium on Computational Intelligence and Games. https://doi.org/10.1109/cig.2009.5286482

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.