DeepChess: End-to-end deep neural network for automatic learning in chess

Omid E. David*, Nathan S. Netanyahu, Lior Wolf

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the more favorable one. The training relies entirely on datasets of several million chess games, and no further domain specific knowledge is incorporated. The experiments show that the resulting neural network (referred to as DeepChess) is on a par with state-of-the-art chess playing programs, which have been developed through many years of manual feature selection and tuning. DeepChess is the first end-to-end machine learningbased method that results in a grandmaster-level chess playing performance.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
EditorsAlessandro E.P. Villa, Paolo Masulli, Antonio Javier Pons Rivero
PublisherSpringer Verlag
Pages88-96
Number of pages9
ISBN (Print)9783319447803
DOIs
StatePublished - 2016
Event25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016 - Barcelona, Spain
Duration: 6 Sep 20169 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9887 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016
Country/TerritorySpain
CityBarcelona
Period6/09/169/09/16

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