Applied Games
Our mission is to leverage the methods of machine learning and game theory for addressing relevant applications both in recreational games and in abstract decision games played in the real world.
Projects
- TrueSkill™
The TrueSkill ranking system is a skill based ranking system for Xbox Live developed at Microsoft Research. The purpose of a ranking system is to both identify and track the skills of gamers in a game (mode) in order to be able to match them into competitive matches. The TrueSkill ranking system only uses the final standings of all teams in a game in order to update the skill estimates (ranks) of all gamers playing in this game. Ranking systems have been proposed for many sports but possibly the most prominent ranking system in use today is ELO. - Drivatar™
A novel form of learning Artificial Intelligence (AI) developed for Forza Motorsport. The AI driving entity called “Drivatar” is learned by monitoring the driving of the player in the game itself. The Drivatar observes the player's road positioning and choice of racing line, the speeds achieved along the course, and the utilisation of the brake and accelerator. This information is then processed and absorbed into an AI Drivatar model which is representative of the driving "style" of that player. This model may be subsequently used to dynamically generate a plausible variety of racing lines and behaviours in race. Importantly too, this model is probabilistic, and will therefore not produce the same output every time; it is not just a simple recording of the player's driving. - Computer Go
We are working on applying machine learning to revolutionize computer Go.
Publications
- Pierre Dangauthier, Ralf Herbrich, Tom Minka, and Thore Graepel, TrueSkill Through Time: Revisiting the History of Chess, in Advances in Neural Information Processing Systems 20, MIT Press, 2008
- David Stern, Ralf Herbrich, and Thore Graepel, Learning To Solve Game Trees, in Proceedings of the International Conference of Machine Learning, January 2007
- Ralf Herbrich, Tom Minka, and Thore Graepel, TrueSkill(TM): A Bayesian Skill Rating System, in Advances in Neural Information Processing Systems 20, MIT Press, January 2007
- Thore Graepel and Ralf Herbrich, Ranking and Matchmaking, in Game Developer Magazine, October 2006
- David Stern, Ralf Herbrich, and Thore Graepel, Bayesian Pattern Ranking for Move Prediction in the Game of Go, in Proceedings of the International Conference of Machine Learning, January 2006
- Ralf Herbrich and Thore Graepel, TrueSkill(TM): A Bayesian Skill Rating System, no. MSR-TR-2006-80, 2006
- Thore Graepel, Ralf Herbrich, and Julian Gold, Learning to Fight, in Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education, January 2004
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Careers
- We are looking for outstanding interns, postdoc researchers, developers and full-time researchers. Visit our career opportunities page
- Former members
