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The game of chess is the most widely-studied domain in the history of artificial intelligence.
Xxii. programming a computer for playing chess
Claude E Shannon · 1950
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Common Sense in Chess
Emanuel Lasker · 1965
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Some studies in machine learning using the game of checkers II - recent progress
A. L. Samuel · 1967
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An analysis of alpha–beta pruning
D. E. Knuth and R. W Moore · 1975
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Computer chess methods
Tony Marsland · 1987
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The Modern Chess Instructor
Wilhelm Steinitz · 1990
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Searching for Solutions in Games and Artificial Intelligence
Victor Allis · 1994
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Learning to play the game of chess
Sebastian Thrun · 1995
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Learning to play chess using temporal differences
J. Baxter, A. Tridgell, and L. Weaver · 2000
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Temporal difference learning for heuristic search and game playing
Donald F. Beal and Martin C. Smith · 2000
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Essential Chess Quotations
John Knudsen · 2000
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Temporal difference learning applied to game playing and the results of application to shogi
Donald F. Beal and Martin C. Smith · 2001
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Deep Blue
M. Campbell, A. J. Hoane, and F. Hsu · 2002
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Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Feng-hsiung Hsu · 2002
Cited alongside, same era.
Computer shogi
Hiroyuki Iida, Makoto Sakuta, and Jeff Rollason · 2002
Cited alongside, same era.
Whole-history rating: A Bayesian rating system for players of time-varying strength
R. Coulom · 2008
Cited alongside, same era.
How Computers Play Chess
David N. L. Levy and Monty Newborn · 2009
Cited alongside, same era.
Move evaluation in Go using deep convolutional neural networks
Chris J. Maddison, Aja Huang, Ilya Sutskever, and David Silver · 2015
Later among the works it cites.
Deepchess: End-to-end deep neural network for automatic learning in chess
Omid E David, Nathan S Netanyahu, and Lior Wolf · 2016
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Bootstrapping from game tree search
J. Veness, D. Silver, A. Blair, and W. Uther · 2016
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URL: https://www.365chess.com/
Online chess games database, 365chess, 2017 · 2017
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Thinking fast and slow with deep learning and tree search
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Raghuram Ramanujan, Ashish Sabharwal, and Bart Selman · 2010
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Analysis of evaluation-function learning by comparison of sibling nodes
Tomoyuki Kaneko and Kunihito Hoki · 2011
Cited alongside, same era.
Monte Carlo chess
Oleg Arenz · 2012
Cited alongside, same era.
Large-scale optimization for evaluation functions with minimax search
Kunihito Hoki and Tomoyuki Kaneko · 2014
Cited alongside, same era.
Giraffe: Using deep reinforcement learning to play chess
Matthew Lai · 2015
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Thomas Anthony, Zheng Tian, and David Barber · 2017
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Results of the 27th world computer shogi championship
Computer Shogi Association · 2017
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In-datacenter performance analysis of a tensor processing unit
Norman P. Jouppi, Cliff Young, Nishant Patil, et al · 2017
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Stockfish: A strong open source chess engine
Tord Romstad, Marco Costalba, Joona Kiiski, et al · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
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