Artificial Intelligence Review

, Volume 29, Issue 2, pp 123–161

Machine learning in digital games: a survey

Authors

    • School of Computing and Information Engineering, Faculty of EngineeringUniversity of Ulster
  • Darryl Charles
    • School of Computing and Information Engineering, Faculty of EngineeringUniversity of Ulster
  • Michaela Black
    • School of Computing and Information Engineering, Faculty of EngineeringUniversity of Ulster
Article

DOI: 10.1007/s10462-009-9112-y

Cite this article as:
Galway, L., Charles, D. & Black, M. Artif Intell Rev (2008) 29: 123. doi:10.1007/s10462-009-9112-y

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.

Keywords

Machine learningComputational intelligenceDigital gamesGame AI

Copyright information

© Springer Science+Business Media B.V. 2009