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Title: Predicting player behavior in Tomb Raider : Underworld
Authors: Mahlmann, Tobias
Drachen, Anders
Togelius, Julian
Canossa, Alessandro
Yannakakis, Georgios N.
Keywords: Computer simulation
Human-computer interaction
Video games
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Mahlmann, T., Drachen, A., Togelius, J., Canossa, A., & Yannakakis, G. N.(2010). Predicting player behavior in Tomb Raider : Underworld. 2010 IEEE Symposium on Computational Intelligence and Games, Copenhagen. 178-185.
Abstract: This paper presents the results of an explorative study on predicting aspects of playing behavior for the major commercial title Tomb Raider: Underworld (TRU). Various supervised learning algorithms are trained on a large-scale set of in-game player behavior data, to predict when a player will stop playing the TRU game and, if the player completes the game, how long will it take to do so. Results reveal that linear regression models and other non-linear classification techniques perform well on the tasks and that decision tree learning induces small yet well-performing and informative trees. Moderate performance is achieved from the prediction models, which indicates the complexity of predicting player behavior based on a constrained set of gameplay metrics and the noise existent in the dataset examined, a generic problem in large-scale data collection from millions of remote clients.
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