Game of Chess
7 papers with code • 0 benchmarks • 0 datasets
Chess is a two-player strategy board game played on a chessboard, a checkered gameboard with 64 squares arranged in an 8×8 grid. The idea of making a machine that could beat a Grandmaster human player was a fascination in the artificial community for decades. Famously IBM's DeepBlue beat Kasparov in the 1990s. More recently more human-like approaches such as AlphaZero have appeared.
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Chess as a Testbed for Language Model State Tracking
Motivated by this issue, we consider the task of language modeling for the game of chess.
Playing Chess with Limited Look Ahead
We have seen numerous machine learning methods tackle the game of chess over the years.
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.
Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data
This paper examines the problem of generating natural language descriptions of chess games.
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of artificial intelligence.
DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess
We present an end-to-end learning method for chess, relying on deep neural networks.
Giraffe: Using Deep Reinforcement Learning to Play Chess
This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer.