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Machine Learning in Digital Games: A Survey

Galway, L., Charles, Darryl and Black, Michaela (2008) Machine Learning in Digital Games: A Survey. Artificial Intelligence Review, 29 (2). pp. 123-161. [Journal article]

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URL: http://www.springerlink.com/content/f2rr836846260201/

DOI: 10.1007/s10462-009-9112-y


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.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
ID Code:9736
Deposited By: Dr Darryl Charles
Deposited On:01 Feb 2010 11:22
Last Modified:06 Jul 2011 13:46

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