DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess
By Omid David, Nathan S. Netanyahu, Lior Wolf. 2016
90 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Economics and Econometrics, Evaluation & Benchmarks, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Yuxi Li
- Yoshua Bengio
- Timothy P. Lillicrap
- David Silver
- Chelsea Finn
- Ruslan Salakhutdinov
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Sergey Levine
- Trevor Darrell
- Dacheng Tao
- Jitendra Malik
- Abhinav Kumar Gupta
- Andrea Vedaldi
- Andrew Zisserman
In Economics and Econometrics
- Arthur Guez
- Timothy P. Lillicrap
- David Silver
- Marc Lanctot
- Thore Graepel
- Demis Hassabis
- Thomas Hubert
- Laurent Sifre
In Evaluation & Benchmarks
- David Silver
- Thore Graepel
- Marc Lanctot
- Dharshan Kumaran
- Ioannis Antonoglou
- Timothy P. Lillicrap
- Karen Simonyan
- Arthur Guez
In Signal Processing
- David Silver
- Thore Graepel
- Dharshan Kumaran
- Timothy P. Lillicrap
- Karen Simonyan
- Matthew Lai
- Demis Hassabis
- Junichi Yamagishi
Citations in the corpus
- VAIN: Attentional Multi-agent Predictive Modeling (2017, neutral): “Approaches include [24, 25] for Chess, [26, 27, 28] for Backgammons [29] for Go.”
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017, background): “The high performance of AlphaZero, using MCTS, calls into question the widely held belief (4, 11) that alpha-beta search is inherently superior in these domains.”