2006

Ranking and Matchmaking. Graepel, Thore; Herbrich, Ralf in Game Developer Magazine (2006). (10)
[ Abstract ] [ BibTeX ] [ URL ]
 
Online game modes are gradually becoming the standard for console games, rather than an added bonus. But how do you get the right players playing against the right people, close enough in skill to be challenging, but not so difficult as to be frustrating?
@article{graepel2006ranking, abstract = {Online game modes are gradually becoming the standard for console games, rather than an added bonus. But how do you get the right players playing against the right people, close enough in skill to be challenging, but not so difficult as to be frustrating?}, author = {Graepel, Thore and Herbrich, Ralf}, journal = {Game Developer Magazine}, keywords = {imported ralf.herbrich}, number = 10, title = {Ranking and Matchmaking}, year = 2006 }

TrueSkill(TM): A Bayesian Skill Rating System. Herbrich, Ralf; Minka, Tom; Graepel, Thore (2006). 569–576.
[ Abstract ] [ BibTeX ] [ URL ]
 
We present a new Bayesian skill rating system which can be viewed as a generalisation of the Elo system used in Chess. The new system tracks the uncertainty about player skills, explicitly models draws, can deal with any number of competing entities and can infer individual skills from team results. Inference is performed by approximate message passing on a factor graph representation of the model. We present experimental evidence on the increased accuracy and convergence speed of the system compared to Elo and report on our experience with the new rating system running in a large-scale commercial online gaming service under the name of TrueSkill.
@inproceedings{herbrich2006trueskilltm, abstract = {We present a new Bayesian skill rating system which can be viewed as a generalisation of the Elo system used in Chess. The new system tracks the uncertainty about player skills, explicitly models draws, can deal with any number of competing entities and can infer individual skills from team results. Inference is performed by approximate message passing on a factor graph representation of the model. We present experimental evidence on the increased accuracy and convergence speed of the system compared to Elo and report on our experience with the new rating system running in a large-scale commercial online gaming service under the name of TrueSkill.}, author = {Herbrich, Ralf and Minka, Tom and Graepel, Thore}, booktitle = {Advances in Neural Information Procesing Systems 19}, keywords = {imported ralf.herbrich}, pages = {569–576}, publisher = {The MIT Press}, title = {{TrueSkill(TM)}: A {Bayesian} Skill Rating System}, year = 2006 }

Bayesian Pattern Ranking for Move Prediction in the Game of Go. Stern, David; Herbrich, Ralf; Graepel, Thore (2006). 873–880.
[ Abstract ] [ BibTeX ] [ URL ]
 
We investigate the problem of learning to predict moves in the board game of Go from game records of expert players. In particular, we obtain a probability distribution over legal moves for professional play in a given position. This distribution has numerous applications in computer Go, including serving as an efficient stand-alone Go player. It would also be effective as a move selector and move sorter for game tree search and as a training tool for Go players. Our method has two major components: a) a pattern extraction scheme for efficiently harvesting patterns of given size and shape from expert game records and b) a Bayesian learning algorithm (in two variants) that learns a distribution over the values of a move given a board position based on the local pattern context. The system is trained on 181,000 expert games and shows excellent prediction performance as indicated by its ability to perfectly predict the moves made by professional Go players in 34% of test positions.
@inproceedings{stern2006bayesian, abstract = {We investigate the problem of learning to predict moves in the board game of Go from game records of expert players. In particular, we obtain a probability distribution over legal moves for professional play in a given position. This distribution has numerous applications in computer Go, including serving as an efficient stand-alone Go player. It would also be effective as a move selector and move sorter for game tree search and as a training tool for Go players. Our method has two major components: a) a pattern extraction scheme for efficiently harvesting patterns of given size and shape from expert game records and b) a Bayesian learning algorithm (in two variants) that learns a distribution over the values of a move given a board position based on the local pattern context. The system is trained on 181,000 expert games and shows excellent prediction performance as indicated by its ability to perfectly predict the moves made by professional Go players in 34\% of test positions.}, author = {Stern, David and Herbrich, Ralf and Graepel, Thore}, booktitle = {Proceedings of the 23rd International Conference on Machine Learning}, keywords = {imported ralf.herbrich}, pages = {873–880}, title = {{Bayesian} Pattern Ranking for Move Prediction in the Game of Go}, year = 2006 }