Abstract
Move prediction systems have always been part of strong Go programs. Recent research has revealed that taking interactions between features into account improves the performance of move predictions. In this paper, a factorization model is applied and a supervised learning algorithm, Latent Factor Ranking (LFR), which enables to consider these interactions, is introduced. Its superiority will be demonstrated in comparison to other state-of-the-art Go move predictors. LFR improves accuracy by 3% over current state-of-the-art Go move predictors on average and by 5% in the middle- and endgame of a game. Depending on the dimensionality of the shared, latent factor vector, an overall accuracy of over 41% is achieved.
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Wistuba, M., Schmidt-Thieme, L. (2013). Move Prediction in Go – Modelling Feature Interactions Using Latent Factors. In: Timm, I.J., Thimm, M. (eds) KI 2013: Advances in Artificial Intelligence. KI 2013. Lecture Notes in Computer Science(), vol 8077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40942-4_23
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DOI: https://doi.org/10.1007/978-3-642-40942-4_23
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