Abstract
In our research, we aim to predict attributes of human players based on observations of their gameplay. If such predictions can be made with sufficient accuracy, games can use them to automatically adapt to the player’s needs. In previous research, however, no conventional classification techniques have been able to achieve accuracies of sufficient height for this purpose. In the present paper, we aim to find out if deep learning networks can be used to build accurate classifiers for gameplay behaviours. We compare a deep learning network with logistic regression and random forests, to predict the platform used by Battlefield 4 players, their nationality and their gaming culture. We find that deep learning networks provide significantly higher accuracies and superior generalization when compared to the more conventional techniques for some of these tasks.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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de Vries, M., Spronck, P. (2017). Deep Learning for Classifying Battlefield 4 Players. In: Poppe, R., Meyer, JJ., Veltkamp, R., Dastani, M. (eds) Intelligent Technologies for Interactive Entertainment. INTETAIN 2016 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-319-49616-0_15
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DOI: https://doi.org/10.1007/978-3-319-49616-0_15
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