Defining personas in games using metrics
Future Play '08 Proceedings of the 2008 Conference on Future Play: Research, Play, Share
Player modeling using self-organization in tomb raider: underworld
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Understanding individual play sequences using growing self organizing maps
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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Each player has an unique and a distinct way of interacting with a computer game due to the preconceived notion and the experience gained through playing the game title. During the game play a player adapts to the challenges posed by the game and a pattern of interaction emerge corresponding to factors such as tackling opponents, movement strategies and even decision making at certain game environments. Understanding decision making patterns provide valuable information about the players which could be exploited to enhance the total game play experience. This paper investigates the possibility of understanding the decision making patterns of a player whilst playing the 2D arcade game Pac-Man using an unsupervised approach known as the growing self organized map (GSOM). Results of this study motivated us to conjecture that player decision making patterns could be identified and explained via unsupervised learning.