Machine learning in digital games: a survey |
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Authors: | Leo Galway Darryl Charles Michaela Black |
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Affiliation: | (1) School of Computing and Information Engineering, Faculty of Engineering, University of Ulster, Cromore Road, Coleraine, Co. Londonderry, BT52 1SA, UK |
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Abstract: | Artificial intelligence for digital games constitutes the implementation of a set of algorithms and techniques from both traditional
and modern artificial intelligence in order to provide solutions to a range of game dependent problems. However, the majority
of current approaches lead to predefined, static and predictable game agent responses, with no ability to adjust during game-play
to the behaviour or playing style of the player. Machine learning techniques provide a way to improve the behavioural dynamics
of computer controlled game agents by facilitating the automated generation and selection of behaviours, thus enhancing the
capabilities of digital game artificial intelligence and providing the opportunity to create more engaging and entertaining
game-play experiences. This paper provides a survey of the current state of academic machine learning research for digital
game environments, with respect to the use of techniques from neural networks, evolutionary computation and reinforcement
learning for game agent control. |
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Keywords: | Machine learning Computational intelligence Digital games Game AI |
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