Semi-Automated Gameplay Analysis by Machine Learning


  • Finnegan Southey University of Alberta
  • Gang Xiao University of Alberta
  • Robert C. Holte University of Albert
  • Mark Trommelen University of Alberta
  • John Buchanan Electronic Arts



While presentation aspects like graphics and sound are important to a successful commercial game, it is likewise important that the gameplay, the non-presentational behaviour of the game, is engaging to the player. Considerable effort is invested in testing and refining gameplay throughout the development process. We present an overall view of the gameplay management problem and, more concretely, our recent research on the gameplay analysis part of this task. This consists of an active learning methodology, implemented in software tools, for largely automating the analysis of game behaviour in order to augment the abilities of game designers. The SAGA-ML (semi-automated gameplay analysis by machine learning) system is demonstrated in a real commercial context, Electronic Arts’ FIFA’99 Soccer title, where it has identified exploitable weaknesses in the game that allow easy scoring by players.




How to Cite

Southey, F., Xiao, G., Holte, R., Trommelen, M., & Buchanan, J. (2021). Semi-Automated Gameplay Analysis by Machine Learning. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 1(1), 123-128.