Abstract
Building a strong computer Go player is a longstanding open problem. In this paper we consider the related problem of predicting the moves made by Go experts in professional games. The ability to predict experts’ moves is useful, because it can, in principle, be used to narrow the search done by a computer Go player. We applied an ensemble of convolutional neural networks to this problem. Our main result is that the ensemble learns to predict 36.9% of the moves made in test expert Go games, improving upon the state of the art, and that the best single convolutional neural network of the ensemble achieves 34% accuracy. This network has less than 104 parameters.
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Sutskever, I., Nair, V. (2008). Mimicking Go Experts with Convolutional Neural Networks. In: KĹŻrková, V., Neruda, R., KoutnĂk, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_11
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DOI: https://doi.org/10.1007/978-3-540-87559-8_11
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