- See Also
-
Links
- “Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models”, Karvonen 2024
- “Grandmaster-Level Chess Without Search”, Ruoss et al 2024
- “The Value of Chess Squares”, Gupta et al 2023
- “Evaluating Superhuman Models With Consistency Checks”, Fluri et al 2023
- “ChessGPT: Bridging Policy Learning and Language Modeling”, Feng et al 2023
- “Statistical Analysis of Chess Games: Space Control and Tipping Points”, Barthelemy 2023
- “Indoor Air Quality and Strategic Decision Making”, Künn et al 2023
- “AI, Ageing and Brain-Work Productivity: Technological Change in Professional Japanese Chess”, Yamamura & Hayashi 2022
- “Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Jacob et al 2021
- “Acquisition of Chess Knowledge in AlphaZero”, McGrath et al 2021
- “Vector Quantized Models for Planning”, Ozair et al 2021
- “Stockfish and Lc0, Test at Different Number of Nodes”, Meloni 2021
- “Learning Chess Blindfolded: Evaluating Language Models on State Tracking”, Toshniwal et al 2021
- “NNUE: The Neural Network of the Stockfish Chess Engine”, Goucher 2021
- “Assessing Game Balance With AlphaZero: Exploring Alternative Rule Sets in Chess”, Tomašev et al 2020
- “Learning Personalized Models of Human Behavior in Chess”, McIlroy-Young et al 2020
- “Measuring Hardware Overhang”, hippke 2020
- “The Chess Transformer: Mastering Play Using Generative Language Models”, Noever et al 2020
- “Aligning Superhuman AI With Human Behavior: Chess As a Model System”, McIlroy-Young et al 2020
- “Smerdon Beats Komodo 5-1 With Knight Odds”, Doggers 2020
- “Transformers Play Chess”, Cheng 2020
- “A Very Unlikely Chess Game”, Alexander 2020
- “Life Cycle Patterns of Cognitive Performance over the Long Run”, Strittmatter et al 2020
- “MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser et al 2019
- “A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go through Self-Play”, Silver et al 2018
- “Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, Sabatelli 2017 (page 3)
- “Assessing Human Error Against a Benchmark of Perfection”, Anderson et al 2016
- “Giraffe: Using Deep Reinforcement Learning to Play Chess”, Lai 2015
- “Algorithmic Progress in Six Domains”, Grace 2013
- “When Will Computer Hardware Match the Human Brain?”, Moravec 1998
- “Human Window on the World”, Michie 1985
- “Turing-Complete Chess Computation”
- Sort By Magic
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models”, Karvonen 2024
Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models
“Grandmaster-Level Chess Without Search”, Ruoss et al 2024
“The Value of Chess Squares”, Gupta et al 2023
“Evaluating Superhuman Models With Consistency Checks”, Fluri et al 2023
“ChessGPT: Bridging Policy Learning and Language Modeling”, Feng et al 2023
“Statistical Analysis of Chess Games: Space Control and Tipping Points”, Barthelemy 2023
Statistical analysis of chess games: space control and tipping points
“Indoor Air Quality and Strategic Decision Making”, Künn et al 2023
“AI, Ageing and Brain-Work Productivity: Technological Change in Professional Japanese Chess”, Yamamura & Hayashi 2022
AI, Ageing and Brain-Work Productivity: Technological Change in Professional Japanese Chess
“Modeling Strong and Human-Like Gameplay With KL-Regularized Search”, Jacob et al 2021
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
“Acquisition of Chess Knowledge in AlphaZero”, McGrath et al 2021
“Vector Quantized Models for Planning”, Ozair et al 2021
“Stockfish and Lc0, Test at Different Number of Nodes”, Meloni 2021
“Learning Chess Blindfolded: Evaluating Language Models on State Tracking”, Toshniwal et al 2021
Learning Chess Blindfolded: Evaluating Language Models on State Tracking
“NNUE: The Neural Network of the Stockfish Chess Engine”, Goucher 2021
“Assessing Game Balance With AlphaZero: Exploring Alternative Rule Sets in Chess”, Tomašev et al 2020
Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess
“Learning Personalized Models of Human Behavior in Chess”, McIlroy-Young et al 2020
“Measuring Hardware Overhang”, hippke 2020
“The Chess Transformer: Mastering Play Using Generative Language Models”, Noever et al 2020
The Chess Transformer: Mastering Play using Generative Language Models
“Aligning Superhuman AI With Human Behavior: Chess As a Model System”, McIlroy-Young et al 2020
Aligning Superhuman AI with Human Behavior: Chess as a Model System
“Smerdon Beats Komodo 5-1 With Knight Odds”, Doggers 2020
“Transformers Play Chess”, Cheng 2020
“A Very Unlikely Chess Game”, Alexander 2020
“Life Cycle Patterns of Cognitive Performance over the Long Run”, Strittmatter et al 2020
Life cycle patterns of cognitive performance over the long run
“MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser et al 2019
MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
“A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go through Self-Play”, Silver et al 2018
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
“Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”, Sabatelli 2017 (page 3)
Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks
“Assessing Human Error Against a Benchmark of Perfection”, Anderson et al 2016
“Giraffe: Using Deep Reinforcement Learning to Play Chess”, Lai 2015
“Algorithmic Progress in Six Domains”, Grace 2013
“When Will Computer Hardware Match the Human Brain?”, Moravec 1998
“Human Window on the World”, Michie 1985
“Turing-Complete Chess Computation”
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
generative-models
superhuman-chess
alphazero-chess
Wikipedia
Miscellaneous
-
http://rjlipton.wordpress.com/2014/12/28/the-new-chess-world-champion/ -
http://www.infinitychess.com/Page/Public/Article/DefaultArticle.aspx?id=118: -
https://adamkarvonen.github.io/machine_learning/2024/01/03/chess-world-models.html -
https://adamkarvonen.github.io/machine_learning/2024/03/20/chess-gpt-interventions.html: -
https://aiimpacts.org/time-for-ai-to-cross-the-human-performance-range-in-chess/ -
https://cacm.acm.org/magazines/2022/2/258230-reimagining-chess-with-alphazero/fulltext: -
https://en.chessbase.com/post/better-than-an-engine-leonardo-ljubicic-1-2 -
https://en.chessbase.com/post/better-than-an-engine-leonardo-ljubicic-2-2 -
https://en.chessbase.com/post/komodo-8-the-smartphone-vs-desktop-challenge -
https://en.chessbase.com/post/leela-chess-zero-alphazero-for-the-pc: -
https://github.com/kagisearch/llm-chess-puzzles?tab=readme-ov-file#results -
https://lichess.org/@/lichess/blog/developer-update-275-improved-game-compression/Wqa7GiAA -
https://triplehappy.wordpress.com/2015/10/26/chess-move-compression/: -
https://villekuosmanen.medium.com/i-played-chess-against-chatgpt-4-and-lost-c5798a9049ca: -
https://www.chess.com/article/view/no-castling-chess-kramnik-alphazero -
https://www.lesswrong.com/posts/4MLBK7iCW3vYd93Mn/a-closer-look-at-chess-scalings-into-the-past -
https://www.lesswrong.com/posts/6dn6hnFRgqqWJbwk9/deception-chess-game-1 -
https://www.lesswrong.com/posts/J6gktpSgYoyq5q3Au/benchmarking-an-old-chess-engine-on-new-hardware -
https://www.lesswrong.com/posts/Q3XaZTExzDpCLr4wu/efficiency-and-resource-use-scaling-parity -
https://www.lesswrong.com/posts/odtMt7zbMuuyavaZB/when-do-brains-beat-brawn-in-chess-an-experiment: -
https://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/ -
https://www.techrepublic.com/article/the-role-of-computers-in-planning-chess-strategy/
Link Bibliography
-
https://arxiv.org/abs/2402.04494#deepmind: “Grandmaster-Level Chess Without Search”, -
https://pubsonline.informs.org/doi/10.1287/mnsc.2022.4643: “Indoor Air Quality and Strategic Decision Making”, -
https://arxiv.org/abs/2111.09259#deepmind: “Acquisition of Chess Knowledge in AlphaZero”, -
https://arxiv.org/abs/2106.04615#deepmind: “Vector Quantized Models for Planning”, -
https://arxiv.org/abs/2009.04374#deepmind: “Assessing Game Balance With AlphaZero: Exploring Alternative Rule Sets in Chess”, -
https://www.chess.com/news/view/smerdon-beats-komodo-5-1-with-knight-odds: “Smerdon Beats Komodo 5-1 With Knight Odds”, -
https://github.com/ricsonc/transformers-play-chess/blob/master/README.md: “Transformers Play Chess”, -
https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-game/: “A Very Unlikely Chess Game”, -
2017-sabatelli.pdf#page=3: “Learning to Play Chess With Minimal Lookahead and Deep Value Neural Networks”,