Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/29769
Title: Towards detecting clusters of players using visual and gameplay behavioral cues
Authors: Asteriadis, Stylianos
Karpouzis, Kostas
Shaker, Noor
Yannakakis, Georgios N.
Keywords: Computer games -- Social aspects
Computer games -- Design
Human-computer interaction
Super Mario Bros. (Game)
Issue Date: 2012
Publisher: Elsevier
Citation: Asteriadis, S., Karpouzis, K., Shaker, N., & Yannakakis, G. N. (2012). Towards detecting clusters of players using visual and gameplay behavioral cues. Procedia Computer Science, 15, 140-147.
Abstract: The issue of discriminating among players' styles and associating them with player profile characteristics, demographics and specific interests and needs is of vital importance for creating content, fine tuned and optimized in such a way that user engagement and interest are maximized. This paper attempts to address the issue of clustering players' behavior using visual features and player performance, as input parameters. Following an unsupervised scheme, in this work, we utilize data from Super Mario game recordings and explore the possibility of retrieving classes of player types along with existing correlations with certain global characteristics.
URI: https://www.um.edu.mt/library/oar//handle/123456789/29769
Appears in Collections:Scholarly Works - InsDG

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