Machine Learning in Games: A Survey

This paper provides a survey of previously published work on machine learning in game playing. The material is organized around a variety of problems that typically arise in game playing and that can be solved with machine learning methods. This approach, we believe, allows both, researchers in game playing to find appropriate learning techniques for helping to solve their problems as well as machine learning researchers to identify rewarding topics for further research in game-playing domains. The paper covers learning techniques that range from neural networks to decision tree learning in games that range from poker to chess. However, space constraints prevent us from giving detailed introductions to the used learning techniques or games. Overall, we aimed at striking a fair balance between being exhaustive and being exhausting. To appear in: J. Fürnkranz & M. Kubat: Machines that Learn to Play Games, Nova Scientific Publishers, Chapter 2, pp. 11–59, Huntington, NY, 2001.