Evaluating Go Game Records for Prediction of Player Attributes

Josef Moudřík; Petr Baudiš; Roman Neruda

doi:10.1109/cig.2015.7317909

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arXiv · 1512.08969

Evaluating Go Game Records for Prediction of Player Attributes

Josef Moudřík·Petr Baudiš·Roman Neruda

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Abstract

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.

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Josef Moudřík, Petr Baudiš, Roman Neruda. 2015-12-30. Evaluating Go Game Records for Prediction of Player Attributes. https://doi.org/10.1109/cig.2015.7317909

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