‘AI chess’ tag
- See Also
-
Links
- “Estimating Cheating Rates in Titled Tuesday”, Rozovsky 2024
- “AI Search: The Bitter-Er Lesson”, McLaughlin 2024
- “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
- “Monte-Carlo Graph Search for AlphaZero”, Czech et al 2020
- “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
- “Big Blue’s Hand Of God”, Levy 1997
- “Human Window on the World”, Michie 1985
- “Time for AI to Cross the Human Performance Range in Chess”
- “Something Weird Is Happening With LLMs and Chess”, Dynomight 2024
- “Komodo 8: the Smartphone vs Desktop Challenge”
- “Leela Chess Zero: AlphaZero for the PC”
- “ChessPositionRanking/img/2389704906374985477664262349386869232706664089.png at Main · Tromp/ChessPositionRanking”
- “Google DeepMind’s Grandmaster-Level Chess Without Search”
- “Update on Playing With Piece Odds against Lc0”
- “What Are Humans Still Good For? The Turning Point in Freestyle Chess May Be Approaching”
- “Turing-Complete Chess Computation”
- “Fine-Tuning Is Not Sufficient for Capability Elicitation”
- “A Closer Look at Chess Scalings (into the Past)”
- “Evidence of Learned Look-Ahead in a Chess-Playing Neural Network”
- “Benchmarking an Old Chess Engine on New Hardware”
- “[The Addictiveness & Adversarialness of Playing against LeelaQueenOdds]”, Järviniemi 2024
- “[The Addictiveness & Adversarialness of Playing against LeelaQueenOdds]”, Järviniemi 2024
- “Sydney Can Play Chess and Kind of Keep Track of the Board State”
- “The Chess Master and the Computer”, Kasparov 2024
- “A Computer Program to Detect Possible Cheating in Chess”
- SRajdev
- Sort By Magic
- Wikipedia
- Miscellaneous
- Bibliography
See Also
Links
“Estimating Cheating Rates in Titled Tuesday”, Rozovsky 2024
“AI Search: The Bitter-Er Lesson”, McLaughlin 2024
“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
“Monte-Carlo Graph Search for AlphaZero”, Czech et al 2020
“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
“Big Blue’s Hand Of God”, Levy 1997
“Human Window on the World”, Michie 1985
“Time for AI to Cross the Human Performance Range in Chess”
“Something Weird Is Happening With LLMs and Chess”, Dynomight 2024
“Komodo 8: the Smartphone vs Desktop Challenge”
“Leela Chess Zero: AlphaZero for the PC”
“ChessPositionRanking/img/2389704906374985477664262349386869232706664089.png at Main · Tromp/ChessPositionRanking”
“Google DeepMind’s Grandmaster-Level Chess Without Search”
“Update on Playing With Piece Odds against Lc0”
“What Are Humans Still Good For? The Turning Point in Freestyle Chess May Be Approaching”
What are humans still good for? The turning point in Freestyle chess may be approaching
“Turing-Complete Chess Computation”
“Fine-Tuning Is Not Sufficient for Capability Elicitation”
“A Closer Look at Chess Scalings (into the Past)”
“Evidence of Learned Look-Ahead in a Chess-Playing Neural Network”
Evidence of Learned Look-Ahead in a Chess-Playing Neural Network:
“Benchmarking an Old Chess Engine on New Hardware”
“[The Addictiveness & Adversarialness of Playing against LeelaQueenOdds]”, Järviniemi 2024
[The addictiveness & adversarialness of playing against LeelaQueenOdds]:
“[The Addictiveness & Adversarialness of Playing against LeelaQueenOdds]”, Järviniemi 2024
[The addictiveness & adversarialness of playing against LeelaQueenOdds]:
“Sydney Can Play Chess and Kind of Keep Track of the Board State”
Sydney can play chess and kind of keep track of the board state:
“The Chess Master and the Computer”, Kasparov 2024
“A Computer Program to Detect Possible Cheating in Chess”
SRajdev
Sort By Magic
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chess-models
model-based-chess reinforcement-learning chessto-ai planning-chess language-models chess-ai
alpha-zero
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://cacm.acm.org/research/reimagining-chess-with-alphazero/:View External Link:
https://cacm.acm.org/research/reimagining-chess-with-alphazero/ -
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://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/6dn6hnFRgqqWJbwk9/deception-chess-game-1 -
https://www.lesswrong.com/posts/Q3XaZTExzDpCLr4wu/efficiency-and-resource-use-scaling-parity -
https://www.techrepublic.com/article/the-role-of-computers-in-planning-chess-strategy/
Bibliography
-
https://yellow-apartment-148.notion.site/AI-Search-The-Bitter-er-Lesson-44c11acd27294f4495c3de778cd09c8d: “AI Search: The Bitter-Er Lesson”, -
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”, -
https://jetpress.org/volume1/moravec.htm: “When Will Computer Hardware Match the Human Brain?”,