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Predicting Player Behavior in Tomb Raider: Underworld (2010)
| Content Provider | CiteSeerX |
|---|---|
| Author | Canossa, Alessandro Yannakakis, Georgios N. Drachen, Anders Mahlmann, Tobias Togelius, Julian |
| 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. |
| File Format | |
| Publisher Date | 2010-01-01 |
| Access Restriction | Open |
| Subject Keyword | Generic Problem Linear Regression Model Large-scale Set Noise Existent Large-scale Data Collection Moderate Performance Learning Algorithm Tru Game Constrained Set Tomb Raider Non-linear Classification Technique Player Behavior In-game Player Behavior Data Gameplay Metric Explorative Study Remote Client Major Commercial Title Tomb Raider Informative Tree |
| Content Type | Text |
| Resource Type | Article |
