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» Learning to Play Chess Using Temporal Differences
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ML
2000
ACM
126views Machine Learning» more  ML 2000»
8 years 6 months ago
Learning to Play Chess Using Temporal Differences
In this paper we present TDLEAF( ), a variation on the TD( ) algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in which our che...
Jonathan Baxter, Andrew Tridgell, Lex Weaver
NIPS
1994
8 years 7 months ago
Learning to Play the Game of Chess
This paper presents NeuroChess, a program which learns to play chess from the final outcome of games. NeuroChess learns chess board evaluation functions, represented by artificial...
Sebastian Thrun
CG
2008
Springer
8 years 8 months ago
Learning Positional Features for Annotating Chess Games: A Case Study
Abstract. By developing an intelligent computer system that will provide commentary of chess moves in a comprehensible, user-friendly and instructive way, we are trying to use the ...
Matej Guid, Martin Mozina, Jana Krivec, Aleksander...
NIPS
1993
8 years 7 months ago
Temporal Difference Learning of Position Evaluation in the Game of Go
The game of Go has a high branching factor that defeats the tree search approach used in computer chess, and long-range spatiotemporal interactions that make position evaluation e...
Nicol N. Schraudolph, Peter Dayan, Terrence J. Sej...
CG
2000
Springer
8 years 10 months ago
Chess Neighborhoods, Function Combination, and Reinforcement Learning
Abstract. Over the years, various research projects have attempted to develop a chess program that learns to play well given little prior knowledge beyond the rules of the game. Ea...
Robert Levinson, Ryan Weber
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