Evaluating Go Game Records for Prediction of Player Attributes
This work addresses the need for automated analysis in Go for applications like player study and program tuning, but it is incremental as it applies existing machine learning methods to a new domain.
The authors tackled the problem of predicting player attributes from Go game records by extracting and aggregating per-move evaluations, achieving good accuracy in predicting strength and playing style.
We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. The evaluations capture different aspects of the games such as played patterns or statistic of sente/gote sequences. Using machine learning algorithms, the evaluations can be utilized to predict different relevant target variables. We apply this methodology to predict the strength and playing style of the player (e.g. territoriality or aggressivity) with good accuracy. We propose a number of possible applications including aiding in Go study, seeding real-work ranks of internet players or tuning of Go-playing programs.