A paper by Omid E. David, Nathan S. Netanyahu, and Lior Wolf:
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 learning-based method
that results in a grandmaster-level chess playing performance.