Machine Learning for Semi-Automated Gameplay Analysis,
4779
Game Design, Lecture
Finnegan Southey
Research Associate, University of Alberta, Dpt of Computing Science
John Buchanan
University Research Liaison, Electronic Arts
Compelling gameplay requires constant testing and refinement during the development process, amounting to a considerable investment in time and energy. This talk presents an approach to gameplay analysis intended to support and augment the work of game designers, collecting and summarizing gameplay information from the game engine so designers can quickly evaluate the behaviour to make decisions. Using readily available machine learning technologies, a reusable tool has been constructed that can repeatedly choose scenarios to examine, run them through the game engine, and then construct concise and informative summaries of the engine's behaviour for designers. Based on the past scenarios, new scenarios are intelligently chosen to verify uncertain conclusions and refine the analysis. Game designers can examine the summaries produced by the analyzer, typically with a secondary visualization tool, providing the essential human judgement on what constitutes reasonable and entertaining behaviour. The inevitable role of the designer is why we use the term `semi-automated'. The analysis tool, SAGA-ML (Semi-Automated Gampeplay Analysis by Machine Learning), is based on machine learning research known as `active learning', and has been used to evaluate Electronic Arts' FIFA'99 soccer game, uncovering some interesting anomalies in gameplay. With only minor changes, the tool was interfaced to FIFA 2004, and plays an active, in-house role in the development and testing of FIFA 2005. While designed and developed in this context, the analysis tool is general purpose, requiring only a thin interface layer to be written to connect to different game engines. For the specific case of FIFA, a visualization tool, SoccerViz, has also been developed. SAGA-ML and SoccerViz were designed and developed by the University of Alberta GAMES group in cooperation with Electronic Arts.
Contributors/authors on this paper: John Buchanan, Electronic Arts, Robert Holte, Computing Science, University of Alberta, Finnegan Southey, Computing Science, University of Alberta, Gang Xiao, Computing Science, University of Alberta, Mark Trommelen, Alberta Ingenuity Centre for Machine Learning
Readily available methods from machine learning can be used to analyze gameplay to support designers during development. A demo of the general-purpose, reusable SAGA-ML tool shows the methodology applied to Electronic Arts' FIFA soccer game. |
