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In this study, we present an innovative fusion of language models and query analysis techniques to unlock cognition in artificial intelligence. The introduced open-source AI system seamlessly integrates a Chess engine with a language model, enabling it to predict moves and provide strategic explanations. Leveraging a v... | AI cognition, Chess, large language models, query analysis, retrievable answer generation | https://huggingface.co/OpenSI/cognitive_AI_chess | https://github.com/TheOpenSI/CoSMIC | https://aisel.aisnet.org/acis2024/31 | Australasian Conference on Information Systems, {ACIS} 2024, Canberra, Australia, December 4-6, 2024 | 2024 | Muntasir Adnan and Buddhi Gamage and Zhiwei Xu and Damith Chandana Herath and Carlos C. N. Kuhn | Unleashing Artificial Cognition: Integrating Multiple {AI} Systems | inproceedings | adnan:2024:unleashing-artificial-cognition-integrating-multiple-ai-systems | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess has long been a benchmark for artificial intelligence (AI) research due to its complexity and well-defined rules. Recent advances, such as AlphaZero, introduced self-learning AI through reinforcement learning and self-play, achieving superhuman performance without prior strategic knowledge, relying solely on the ... | null | null | null | http://hdl.handle.net/20.500.12380/310683 | null | 2025 | Adolfsson, Hannes and Lewis, David and Rahmn, Anton and Rajam{\"a}e, Sigge and Rungardt, Edvin and Tafani, Marco | A self-trained engine for a chess variant | thesis | adolfsson:2025:self-trained-engine-chess-variant | Bachelor's thesis | Abel, Andreas | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The board game market has experienced significant growth worldwide over the past decade, and Hungary is no exception. The aim of this study is to analyse the state of the Hungarian board game market from both financial and macroeconomic perspectives, and to compare it with international trends. The analysis is based on... | board games, pricing, product markets | null | null | https://www.bankszovetseg.hu/gep-reszlet.cshtml?gepId=53&lang=eng | null | 2025 | Adorj\'{a}n, Bal\'{a}zs and Bedn\'{a}rik, \'{E}va | Statistical Analysis of Hungarian Board Game Sales | article | adorjan:2025:statistical-analysis-hungarian-board-games | null | null | https://bankszovetseg.hu/Public/gep/2025/2025_4_angol/453-484%20E%20Adorjan%20B%20Tarsasjatek%20eladasi%20statisztikak.pdf | 10.33908/EF.2025.4.1 | 453--484 | 4 | 12 | Economy and Finance | December | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Modern chess engines, such as Stockfish, have grown increasingly powerful at analyzing and calculating evaluations of given chess positions. Most chess engines are capable of not only providing several sequences of optimal moves which are most likely to yield the highest advantage, but also quantifying the perceived ad... | Python; Multi-threaded; Research; Chess | null | https://github.com/paulxro/cse592_chess | https://paulaldea.com/project_pictures/chess_elo_file.pdf | null | 2024 | Aldea, Paul-Andrei and Bangarbale, Pranav and Li, Jerry | Advancing Chess Engine Design with Elo-Integrated Evaluation (EIE) | misc | aldea:2024:advancing-chess-engine-design-elo-integrated-evaluation-eie | null | null | null | null | null | null | null | null | null | Student project paper, CSE 592, University of Michigan | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The FIDE Laws of Chess establish that if a player runs out of time during a game, they lose unless there exists no sequence of legal moves that ends in a checkmate by their opponent, in which case the game is drawn. The problem of determining whether or not a given chess position is unwinnable for a certain player has ... | null | null | https://github.com/miguel-ambrona/D3-Chess | https://doi.org/10.4230/LIPIcs.FUN.2022.2 | 11th International Conference on Fun with Algorithms, {FUN} 2022, May 30 to June 3, 2022, Island of Favignana, Sicily, Italy | 2022 | Miguel Ambrona | A Practical Algorithm for Chess Unwinnability | inproceedings | ambrona:2022:practical-algorithm-chess-unwinnability | null | null | https://chasolver.org/FUN22-full.pdf | 10.4230/LIPICS.FUN.2022.2 | 2:1--2:20 | null | 226 | null | null | null | Pierre Fraigniaud and Yushi Uno | https://chasolver.org/ | LIPIcs | Schloss Dagstuhl - Leibniz-Zentrum f{\"{u}}r Informatik | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This report encompasses the implementation of two state-of-the-art machine learning algorithms for evaluating chess positions. The first algorithm makes use of artificial neural networks and manual feature representation thus closely following the implementation and architecture of Matthew Lai's Giraffe. Giraffe learn... | null | null | null | https://dr.ntu.edu.sg/handle/10356/157572?mode=full | null | 2022 | Manav Arora | Deep learning for computer chess (part 1) | misc | arora:2022:deep-learning-computer-chess | null | null | null | null | null | null | null | null | null | Final Year Project (FYP) | null | null | null | Nanyang Technological University | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
WebAssembly (Wasm for short) brings a new, powerful capability to the web as well as Edge, IoT, and embedded systems. Wasm is a portable, compact binary code format with high performance and robust sandboxing properties. As Wasm applications grow in size and importance, the complex performance characteristics of divers... | Benchmarking, WebAssembly, record and replay | null | null | https://doi.org/10.1145/3689787 | null | 2024 | Baek, Doehyun and Getz, Jakob and Sim, Yusung and Lehmann, Daniel and Titzer, Ben L. and Ryu, Sukyoung and Pradel, Michael | Wasm-R3: Record-Reduce-Replay for Realistic and Standalone WebAssembly Benchmarks | article | baek:2024:wasm-r3-webassembly-benchmarks | null | null | null | 10.1145/3689787 | null | OOPSLA2 | 8 | Proc. ACM Program. Lang. | October | null | null | null | null | Association for Computing Machinery | 27 | 347 | October 2024 | New York, NY, USA | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Generative sequence models are typically trained on sample sequences from natural or formal languages. It is a crucial question whether -- or to what extent -- sample-based training is able to capture the true structure of these languages, often referred to as the "world model". Theoretical results indicate that we can... | generative sequence model, implicit world model, adversarial sequences, chess | null | https://github.com/szegedai/world-model-verification | https://openreview.net/forum?id=BLOIB8CwBI | The Fourteenth International Conference on Learning Representations | 2026 | Andr\'{a}s Balogh and M\'{a}rk Jelasity | Verification of the Implicit World Model in a Generative Model via Adversarial Sequences | inproceedings | balogh:2026:verification-implicit-world-model-generative-model-adversarial-sequences | null | null | https://arxiv.org/pdf/2602.05903 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This paper presents a data-driven statistical framework to quantify the role of skill in games, addressing the long-standing question of whether success in a game is predominantly driven by skill or chance. We analyze player level data from four popular games Chess, Rummy, Ludo, and Teen Patti, using empirical win stat... | Chance, Chess, Ludo, Rummy, Skill, Statistical Analysis, Teen Patti | null | null | https://doi.org/10.48550/arXiv.2410.14363 | null | 2024 | Tathagata Banerjee and Anushka De and Subhamoy Maitra and Diganta Mukherjee | Skill vs. Chance Quantification for Popular Card & Board Games | article | banerjee:2024:skill-vs-chance-quantification-popular-card-board-games | null | null | null | 10.48550/ARXIV.2410.14363 | null | null | abs/2410.14363 | CoRR | null | null | null | null | null | null | null | null | null | null | 2410.14363 | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Computer chess research has traditionally focused on creating the strongest possible chess engine. Recently, however, attempts have been made to create engines that mimic the playing strength and style of human players. Our research proposes enhancements of models developed in this vein that more accurately imitate mas... | Artificial intelligence, Chess, Action prediction | null | null | https://doi.org/10.1007/978-3-031-54968-7_1 | Advances in Computer Games - 18th International Conference, {ACG} 2023, Virtual Event, November 28-30, 2023, Revised Selected Papers | 2023 | Daniel Barrish and Steve Kroon and Brink van der Merwe | Making Superhuman {AI} More Human in Chess | inproceedings | barrish:2023:making-superhuman-ai-more-human | null | null | null | 10.1007/978-3-031-54968-7_1 | 3--14 | null | 14528 | null | null | null | Michael Hartisch and Chu{-}Hsuan Hsueh and Jonathan Schaeffer | null | Lecture Notes in Computer Science | Springer | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This article reports an investigation of the extent to which a chess program with an artificial intelligence component (i.e., Stockfish with NNUE) can identify 10 chess moves that are recognized as outstanding chess moves. Stockfish with NNUE was able to identify seven of the ten moves. Although Stockfish with NNUE is ... | Chess, Creative move, Creativity, Stockfish 15, Artificial intelligence | null | null | https://www.sciencedirect.com/science/article/pii/S271337452300016X | null | 2023 | William Bart | Can artificial intelligence identify creativity?: An empirical study | article | bart:2023:can-artificial-intelligence-identify-creativity | null | null | null | 10.1016/j.yjoc.2023.100057 | 100057 | 2 | 33 | Journal of Creativity | null | null | null | null | null | null | null | null | null | null | null | null | 2713-3745 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Recent progress in machine learning has been fueled by increasing scale, enabling breakthroughs in domains such as image generation, natural language understanding, and decision-making. While tremendous improvements have been realized for low-risk applications like chat completion and recommendation, there are fundame... | null | null | null | null | null | 2024 | Beliaev, Mark | Towards Robust and Cooperative Learning Algorithms | thesis | beliaev:2024:robust-cooperative-learning-algorithms | Doctoral Thesis | null | null | null | null | null | null | null | null | https://escholarship.org/uc/item/6mc2d7q3 | null | null | null | null | null | null | null | null | null | null | null | UC Santa Barbara | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess is widely played on computers, yet over-theboard (OTB) chess remains the official and preferred format for many players due to its tactile and immersive nature. Bridging digital and physical play requires accurate recognition of OTB positions. Prior research has explored modular pipelines for board localization, ... | YOLO;Accuracy;Pipelines;Games;Robustness;Calibration;Time factors;Synchronization;Image reconstruction;Engines;Chess recognition;YOLO;Stockfish;Lichess | null | null | null | 2025 40th International Conference on Image and Vision Computing New Zealand (IVCNZ) | 2025 | Benitez-Garcia, Gibran and Takahashi, Hiroki | Y-LIChess: Live and Interactive Over-The-Board Chess Recognition and Play with Yolo | inproceedings | benitez-garcia:2025:ylichess-live-interactive-over-the-board-chess-recognition-play-yolo | null | null | null | 10.1109/IVCNZ67716.2025.11281842 | 1--6 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The Elo score has been extensively used to rank players by their skill or strength in competitive games such as chess, go, or StarCraft II. The Elo score implicitly assumes games have a strong additive--hence transitive--component. In this paper, we investigate the challenge of identifying transitive components in game... | null | null | https://github.com/QB3/discrating | https://proceedings.mlr.press/v206/bertrand23a.html | International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain | 2023 | Quentin Bertrand and Wojciech Marian Czarnecki and Gauthier Gidel | On the Limitations of the Elo, Real-World Games are Transitive, not Additive | inproceedings | bertrand:2023:limitations-elo-real-world-games-transitive-not-additive | null | null | https://proceedings.mlr.press/v206/bertrand23a/bertrand23a.pdf | null | 2905--2921 | null | 206 | null | null | null | Francisco J. R. Ruiz and Jennifer G. Dy and Jan{-}Willem van de Meent | null | Proceedings of Machine Learning Research | {PMLR} | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
When generating levels, algorithmically evaluating the results is essential. In this paper, we looked at predicting a level's difficulty and enjoyment. Past work has approached this problem for puzzle games like Sudoku by analyzing the characteristics of the initial level, the solved level, and the process that led to ... | difficulty, player study, procedural content generation | null | null | https://doi.org/10.1145/3649921.3659846 | Proceedings of the 19th International Conference on the Foundations of Digital Games | 2024 | Biemer, Colan and Cooper, Seth | Solution Path Heuristics for Predicting Difficulty and Enjoyment Ratings of Roguelike Level Segments | inproceedings | biemer:2024:solution-path-heuristics-predicting-difficulty-enjoyment-ratings-roguelike-level-segments | null | null | null | 10.1145/3649921.3659846 | null | null | null | null | null | null | null | null | FDG '24 | Association for Computing Machinery | 8 | 69 | null | New York, NY, USA | null | null | null | null | 9798400709555 | Worcester, MA, USA | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
We present a novel method to find chess positions similar to a given query position from a collection of chess games. We consider not only the static similarity resulting from the arrangement of chess pieces, but also the dynamic similarity involving the recognition of chess motifs and the tactical, dynamic aspects of ... | Problem solving, Chess motifs, Automatic similarity recognition | null | null | https://doi.org/10.1007/978-3-031-11488-5_12 | Advances in Computer Games: 17th International Conference, ACG 2021, Virtual Event, November 23–25, 2021, Revised Selected Papers | 2021 | Bizjak, Miha and Guid, Matej | Automatic Recognition of Similar Chess Motifs | inproceedings | bizjak:2021:automatic-recognition-similar-chess-motifs | null | null | null | 10.1007/978-3-031-11488-5_12 | 131--141 | null | null | null | null | null | null | null | null | Springer-Verlag | 11 | null | null | Berlin, Heidelberg | null | null | null | null | 978-3-031-11487-8 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
A common way for chess players to practice tactical awareness is to solve chess puzzles, consisting of an initial position and a sequence of moves to achieve a winning position. This practice is more effective when puzzles are matched to the player's skill level. In this work, we present an approach for estimating the ... | null | null | null | http://dx.doi.org/10.15439/2025F6497 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Sebastian Bj\"{o}rkqvist | Estimating the Difficulty of Chess Puzzles by Combining Fine-Tuned Maia-2 with Hand-Crafted and Engine Features | inproceedings | bjorkqvist:2025:estimating-difficulty-chess-puzzles-combining-fine-tuned-maia-2-hand-crafted-engine-features | null | null | null | 10.15439/2025F6497 | 801--806 | null | 43 | null | null | null | Marek Bolanowski and Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik \'{S}l\k{e}zak | null | Annals of Computer Science and Information Systems | IEEE | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Over the last decade, the amount of data generated by software applications e.g. information systems, websites, mobile applications etc. has increased tremendously. Process mining, a subdiscipline of data science, uses this data to analyse and improve processes. In this research, the possibilities of process mining on ... | null | null | null | http://essay.utwente.nl/88571/ | null | 2021 | Niels Bos | Improving the Chess Elo System With Process Mining | thesis | bos:2021:improving-chess-elo-system-process-mining | Bachelor's thesis | Faiza Allah Bukhsh | null | null | null | null | null | null | July | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
We detail the bread emoji team's submission to the FedCSIS 2025 Predicting Chess Puzzle Difficulty Challenge. Our solution revolved around improving our submission from the previous competition by incorporating a new puzzle metadata feature and optimizing our implementation to allow for larger model ensembles and more ... | null | null | null | http://dx.doi.org/10.15439/2025F6771 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Tyler Woodruff and Luke Imbing and Marco Cognetta | The bread emoji Team's Submission to the 2025 FedCSIS Predicting Chess Puzzle Difficulty Challenge | inproceedings | bread-emoji-team-submission-2025-fedcsis-predicting-chess-puzzle-difficulty-challenge | null | null | null | 10.15439/2025F6771 | 837--842 | null | 43 | null | null | null | Marek Bolanowski and Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik \'{S}l\k{e}zak | null | Annals of Computer Science and Information Systems | IEEE | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Accurately estimating the difficulty of the chess puzzle is important for adaptive training systems, personalized recommendations, and large-scale content curation. Unlike engine evaluations optimized for perfect play, this task involves modeling human-perceived solving difficulty, typically expressed by Glicko-2 ratin... | null | null | null | http://dx.doi.org/10.15439/2025F4532 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Ling Cen and Jiahao Cen and Malin Song and Zhuliang Yu | A Multi-Stage Framework for Chess Puzzle Difficulty Prediction | inproceedings | cen:2025:multi-stage-framework-chess-puzzle-difficulty-prediction | null | null | null | 10.15439/2025F4532 | 807--812 | null | 43 | null | null | null | Marek Bolanowski and Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik \'{S}l\k{e}zak | null | Annals of Computer Science and Information Systems | IEEE | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Open-source software (OSS) projects, characterized by distributed development and volunteer contributions, face challenges in prioritizing user-centered design and usability. This difficulty arises because these projects are primarily driven by developers who focus on technical contributions. As a result, usability and... | open-source software, user-centered design, usability, User persona, UX design | null | https://github.com/ChellyAhmed/personas-os-resources | https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1457563 | null | 2025 | Chelly, Ahmed and Hamza, Salma and Khan, Javed Ali | How Relevant Are Personas in Open-Source Software Development? | article | chelly:2025:how-relevant-personas-open-source-software-development | null | null | null | 10.3389/fcomp.2025.1457563 | null | null | Volume 7 - 2025 | Frontiers in Computer Science | null | null | null | null | null | null | null | null | null | null | null | null | 2624-9898 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Several recently introduced deep learning optimizers inspired by second-order methods have shown promising speedups relative to the current dominant optimizer AdamW, particularly in relatively small-scale experiments. However, efforts to validate and replicate their successes have reported mixed results, with some find... | second order optimization; scaling laws; maximum update paramterization; batch size scaling; depth scaling; critical batch size; compute optimal scaling | null | null | https://openreview.net/forum?id=Ei6IsmxYrb | The Thirty-ninth Annual Conference on Neural Information Processing Systems | 2025 | Zixi Chen and Shikai Qiu and Hoang Phan and Qi Lei and Andrew Gordon Wilson | How to Scale Second-Order Optimization | inproceedings | chen:2025:how-to-scale-second-order-optimization | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | We investigate how to scale second-order optimizers effectively, showing they outperform Adam and reduce data needs in compute-optimal transformer training. | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The goal of FedCSIS 2025 Challenge is to build a model to predict the difficulty (measured as Lichess rating) of given chess puzzles. To address this task, we propose a three-stage joint visual–statistical framework for predicting Glicko-based difficulty ratings. In the first stage, a convolutional model based on Mobil... | null | null | null | http://dx.doi.org/10.15439/2025F3227 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Junlin Chen and Cenru Liu and Yujie Gao | Multi-Modal Deep Learning with Residual and Structure-Guided Refinement for Chess Puzzle Difficulty Prediction | inproceedings | chen:2025:multi-model-deep-learning-residual-structure-guided-refinement-chess-puzzle-difficulty-prediction | null | null | null | 10.15439/2025F3227 | 813--818 | null | 43 | null | null | null | Marek Bolanowski and Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik \'{S}l\k{e}zak | null | Annals of Computer Science and Information Systems | IEEE | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Strength estimation and adjustment are crucial in designing human-AI interactions, particularly in games where AI surpasses human players. This paper introduces a novel strength system, including a strength estimator (SE) and an SE-based Monte Carlo tree search, denoted as SE-MCTS, which predicts strengths from games a... | Bradley-Terry Model, Strength Estimation, Strength Adjustment, Human-like Playing Style, Monte-Carlo Tree Search, Go, Chess | null | https://github.com/rlglab/strength-estimator/ | https://openreview.net/forum?id=CvjXlsBLCX | The Thirteenth International Conference on Learning Representations | 2025 | Chun Jung Chen and Chung-Chin Shih and Ti-Rong Wu | Strength Estimation and Human-Like Strength Adjustment in Games | inproceedings | chen:2025:strength-estimation-human-like-strength-adjustment-games | null | null | null | null | null | null | null | null | null | null | null | https://rlg.iis.sinica.edu.tw/papers/strength-estimator/ | null | null | null | null | null | null | null | null | null | null | null | null | This paper proposes a strength system that can estimate the strength from games and provide various playing strengths while simultaneously offer a human-like behavior in both Go and chess. | SEtheChessBot8, SEtheChessBot7, SEtheChessBot6, SEtheChessBot5, SEtheChessBot4, SEtheChessBot3, SEtheChessBot2, SEtheChessBot1, SEtheChessGod | https://rlg.iis.sinica.edu.tw/papers/strength-estimator/assets/Strength%20Estimation%20and%20Human-Like%20Strength%20Adjustment%20in%20Games%20Slides.pdf | https://rlg.iis.sinica.edu.tw/papers/strength-estimator/assets/Strength%20Estimation%20and%20Human-Like%20Strength%20Adjustment%20in%20Games%20Poster.pdf | https://rlg.iis.sinica.edu.tw/papers/strength-estimator/assets/models/chess.tar.gz | https://lichess.org/@/Dr_Kiwi/blog/a-humanlike-playstyle-chess-bot-for-every-level-player/pU3M8ya4 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This paper presents the design, implementation, and evaluation of an innovative electronic chess board leveraging Hall-effect sensors for chess piece identification. Current electronic chess boards employ diverse technologies such as RFID, resistive switches, optical sensors, and computer vision, each with varying comp... | Magnetic flux density;Magnetic sensors;Printed circuits;Interference;Sensor phenomena and characterization;Robot sensing systems;Sensor systems;Sensors;Reliability;Magnets;electronic chessboard;analogue object identification;Hall-effect sensors;micro-controller | null | null | null | 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) | 2025 | Cheong, Justin Julius Chin and Bhatia, Praneel and Krauledat, Matthias and Hartanto, Ronny | Design of Electronic Chess Board Using Analogue Hall-effect Sensors for Piece Identification | inproceedings | cheong:2025:design-electronic-chess-board-analogue-hall-effect-sensors-piece-identification | null | null | null | 10.1109/ICHORA65333.2025.11016842 | 1--5 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Traditional chess engines face a compelling dual challenge that significantly limits their practical utility for human chess education and training. First, engines like Stockfish, AlphaZero, and LeelaChess require computationally intensive tree-search algorithms, evaluating millions of positions per second to determine... | null | null | null | https://cs224r.stanford.edu/projects/pdfs/CS224R_Final_Report__1_%20(3).pdf | null | 2025 | Choudhary, Prerit and Vagadia, Rikhil and Dhawan, Ankush | Human Chess: A Novel Searchless RL-based Chess Agent Capable of Multi-ELO Human-Like Play | misc | choudhary:2025:human-chess-novel-searchless-rl-chess-agent-capable-multi-elo-human-like-play | null | null | null | null | null | null | null | null | null | Final project for CS 224R Deep Reinforcement Learning, Spring 2025 https://cs224r.stanford.edu/projects/cs224r_final_projects.html | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
From sports to science, the recent availability of large-scale data has allowed to gain insights on the drivers of human innovation and success in a variety of domains. Here we quantify human performance in the popular game of chess by leveraging a very large dataset comprising of over 120 million games between almost ... | null | null | null | https://doi.org/10.1038/s41598-023-27735-9 | null | 2023 | Chowdhary, Sandeep and Iacopini, Iacopo and Battiston, Federico | Quantifying human performance in chess | article | chowdhary:2023:quantifying-human-performance-chess | null | null | null | 10.1038/s41598-023-27735-9 | 2113 | 1 | 13 | Scientific Reports | February | null | null | null | null | null | null | null | null | null | null | null | 2045-2322 | null | null | null | null | null | null | null | null | null | 06 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
A chess opening is the preliminary stage of a chess game which typically consists of moves from formerly analysed openings. Opening strategy plays a crucial role in the entire game and decides the destiny of the middlegame and endgame. Here in this article, we attempted to introduce a method to predict the opening move... | Chess Opening, Opponent, middlegame, endgame. | null | null | null | null | 2021 | Chowdhury, Debarpan Bose and Sen, Banashree | Predicting Chess Opening Through Modelling Of Chess Opponents | article | chowdhury:2021:predicting-chess-openings-modelling-opponents | null | null | https://www.webology.org/data-cms/articles/20220815062132pmwebology%2018%20(6)%20-%20579.pdf | null | null | 6 | 18 | Webology (ISSN: 1735-188X) | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess is becoming more popular and accessible by the day. For ins- tance, online chess enables matches between players from different parts of the world, bringing new ways of learning the game and interacting with other Web users. With this growth in popularity, there is a possibility to empower amateur players with ri... | null | null | null | https://sol.sbc.org.br/index.php/eniac/article/view/18296 | Anais do XVIII Encontro Nacional de Intelig\^{e}ncia Artificial e Computacional | 2021 | Giovanni Comarela and Davi Silva | A lightweight approach for predicting errors in chess matches | inproceedings | comarela:2021:lightweight-approach-prediction-errors-chess | null | null | https://sol.sbc.org.br/index.php/eniac/article/view/18296/18130 | 10.5753/eniac.2021.18296 | 703--714 | null | null | null | null | null | null | null | null | SBC | null | null | null | Porto Alegre, RS, Brasil | null | null | 2763-9061 | null | null | Evento Online | null | null | null | null | null | null | null | PT | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
There is a long-held belief in the chess community that the player with the white pieces has an advantage in making the first move. This phenomenon has been observed repeatedly in over-the-board games between high-level players and professionals. However, less is known about the prevalence of white's advantage in games... | null | null | null | https://doi.org/10.1177/13896911251315903 | null | 2025 | Tyler Cook | The Advantage of Moving First in Amateur Online Chess | article | cook:2025:advantage-moving-first-amateur-online-chess | null | null | null | 10.1177/13896911251315903 | null | null | null | ICGA Journal | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
We investigate the look-ahead capabilities of chess-playing neural networks, specifically focusing on the Leela Chess Zero policy network. We build on the work of Jenner et al. (2024) by analyzing the model's ability to consider future moves and alternative sequences beyond the immediate next move. Our findings reveal ... | model behavior attribution, look-ahead planning, mechanistic interpretability | null | null | https://doi.org/10.48550/arXiv.2505.21552 | null | 2025 | Diogo Cruz | Understanding the learned look-ahead behavior of chess neural networks | article | cruz:2025:understanding-learned-look-ahead-behavior-chess-neural-networks | null | null | null | 10.48550/ARXIV.2505.21552 | null | null | abs/2505.21552 | CoRR | null | keywords from rejected openreview submission: https://openreview.net/forum?id=OcBAd0JPxv¬eId=ZcMfmDpMpn | null | null | null | null | null | null | null | null | 2505.21552 | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Deep neural networks have been successfully applied in learning the board games Go, chess, and shogi without prior knowledge by making use of reinforcement learning. Although starting from zero knowledge has been shown to yield impressive results, it is associated with high computationally costs especially for complex ... | deep learning, chess, crazyhouse, supervised learning, Monte-Carlo tree search | null | https://github.com/QueensGambit/CrazyAra | https://doi.org/10.3389/frai.2020.00024 | null | 2020 | Johannes Czech and Moritz Willig and Alena Beyer and Kristian Kersting and Johannes F{\"{u}}rnkranz | Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data | article | czech:2020:learning-chess-variant-crazyhouse-above-world-champion-level-deep-neural-networks-human-data | null | null | https://public-pages-files-2025.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2020.00024/pdf | 10.3389/FRAI.2020.00024 | 24 | null | 3 | Frontiers Artif. Intell. | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://github.com/QueensGambit/CrazyAra/wiki/Stockfish-10:-Crazyhouse-Self-Play | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network that learns a value and policy function to guide the exploration in a Monte-Carlo Tree Search. Although many search improvements such as graph search have been proposed for Monte-... | Classical Planning Techniques And Analysis, Applications And Case Studies Of Planning And Scheduling Techniques, Learning For Planning And Scheduling, Multi-agent And Distributed Planning | null | null | https://ojs.aaai.org/index.php/ICAPS/article/view/15952 | Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, {ICAPS} 2021, Guangzhou, China (virtual), August 2-13, 2021 | 2021 | Johannes Czech and Patrick Korus and Kristian Kersting | Improving AlphaZero Using Monte-Carlo Graph Search | inproceedings | czech:2021:improving-alphazero-monte-carlo-graph-search | null | null | https://ojs.aaai.org/index.php/ICAPS/article/view/15952/15763 | null | 103--111 | null | null | null | null | null | Susanne Biundo and Minh Do and Robert Goldman and Michael Katz and Qiang Yang and Hankz Hankui Zhuo | null | null | {AAAI} Press | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans. In response to the proliferation of such models, the field of Explainable AI (XAI) has sought to develop techniques that enhance the transparency and interpretability of machine learning... | explainable AI, machine learning | null | null | https://doi.org/10.1145/3377325.3377512 | Proceedings of the 25th International Conference on Intelligent User Interfaces | 2020 | Das, Devleena and Chernova, Sonia | Leveraging rationales to improve human task performance | inproceedings | das:2020:leveraging-rationales-human-task-performance | null | null | null | 10.1145/3377325.3377512 | 510–518 | null | null | null | null | null | null | null | IUI '20 | Association for Computing Machinery | 9 | null | null | New York, NY, USA | null | null | null | null | 9781450371186 | Cagliari, Italy | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Mechanistic interpretability (MI) studies aim to identify the specific neural pathways that underlie decision-making in neural networks. Here we analyze both the horizontal and vertical information flows of a chess-playing transformer. This paper introduces a new taxonomy of chessboard attention patterns that synchroni... | chess cognition, mechanistic interpretability, transformers | null | null | https://doi.org/10.1007/978-3-031-65572-2_7 | Artificial General Intelligence: 17th International Conference, AGI 2024, Seattle, WA, USA, August 13–16, 2024, Proceedings | 2024 | Davis, Austin L. and Sukthankar, Gita | Decoding Chess Mastery: A Mechanistic Analysis of a Chess Language Transformer Model | inproceedings | davis:2024:decoding-chess-mastery-mechanistic-analysis-chess-language-transformer-model | null | null | null | 10.1007/978-3-031-65572-2_7 | 63--72 | null | null | null | null | null | null | null | null | Springer-Verlag | 10 | null | null | Berlin, Heidelberg | null | null | null | null | 978-3-031-65571-5 | SEATTLE, WA, USA | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Probing classifiers are a technique for understanding and modifying the operation of neural networks in which a smaller classifier is trained to use the model's internal representation to learn a probing task. Similar to a neural electrode array, probing classifiers help both discern and edit the internal representatio... | Representation Engineering; Probing Classifiers; Chess-playing Language Models, GPT | null | https://github.com/austinleedavis/icmla-2024 | null | 2024 International Conference on Machine Learning and Applications (ICMLA) | 2024 | Davis, Austin L and Sukthankar, Gita | Hidden Pieces: An Analysis of Linear Probes for GPT Representation Edits | inproceedings | davis:2024:hidden-pieces-analysis-linear-probes-gpt-representation-edits | null | null | https://ial.eecs.ucf.edu/pdf/Sukthankar-Austin-ICMLA2024.pdf | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This dissertation investigates the structures and mechanisms underpinning the latent space representations that emerge within Generative Pretrained Transformer (GPT) models. Addressing the broader goal of enhancing AI trustworthiness through transparency, accountability, and controllability, we focus on techniques to ... | Latent Representation Editing; Mechanistic Interpretability; Linear Probes; Chess Language Models; Artificial Intelligence | null | null | https://stars.library.ucf.edu/etd2024/285 | null | 2025 | Davis, Austin | Interpretation and Control of AI Model Behavior Through Direct Adjustment of Latent Representations | thesis | davis:2025:interpretation-control-ai-model-behavior-direct-adjustment-latent-representations | PhD thesis | Sukthankar, Gita | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | University of Central Florida | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Probing classifiers are a technique for understanding and modifying the operation of neural networks in which a smaller classifier is trained to use the model's internal representation to learn a probing task. Similar to a neural electrode array, probing classifiers help both discern and edit the internal representatio... | representation engineering; probing classifiers; chess; language models; GPT; sparse autoencoders | null | https://github.com/austinleedavis/icmla-2024 | https://doi.org/10.20944/preprints202601.2229.v1 | null | 2026 | Austin L. Davis and Robinson Vasquez Ferrer and Gita Sukthankar | Exploring the Limits of Probes for Latent Representation Edits in GPT Models | article | davis:2026:exploring-limits-probes-latent-representation-edits-gpt-models | null | null | null | 10.20944/preprints202601.2229.v1 | null | null | null | Preprints | January | null | null | null | null | Preprints | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess is a centuries-old game that continues to be widely played worldwide. Opening Theory is one of the pillars of chess and requires years of study to be mastered. In this paper, we use the games played in an online chess platform to exploit the ``wisdom of the crowd'' and answer questions traditionally tackled only ... | null | null | null | https://doi.org/10.1038/s41598-023-31658-w | null | 2023 | De Marzo, Giordano and Servedio, Vito D. P. | Quantifying the complexity and similarity of chess openings using online chess community data | article | de-marzo:2023:complexity-similarity-chess-openings-community-data | null | null | null | 10.1038/s41598-023-31658-w | 5327 | 1 | 13 | Scientific Reports | April | null | null | null | null | null | null | null | null | null | null | null | 2045-2322 | null | null | null | null | null | null | null | null | null | 01 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess pieces recognition using computer vision is a problem generally approached in various ways, with different kinds of results and complexity. Deep learning is a state of the art approach to solve problems on image recognition although facing necessity of huge data sets. This paper discusses a method to identify syn... | Histograms, Image color analysis, Games, Tracking, Image edge detection, Machine learning, Task analysis, chess, neural networks, piece recognition, synthetic data generation | null | https://github.com/rafaelmcam/cChess | null | 2019 21st Symposium on Virtual and Augmented Reality (SVR) | 2019 | de S\'{a} Delgado Neto, Afonso and Mendes Campello, Rafael | Chess Position Identification using Pieces Classification Based on Synthetic Images Generation and Deep Neural Network Fine-Tuning | inproceedings | de-sa-delgado-neto:2019:chess-position-identification | null | null | null | 10.1109/SVR.2019.00038 | 152--160 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://github.com/rafaelmcam/cChess/tree/master/Jogos | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
In reinforcement learning, Transformers have been shown to be powerful models for multi-task policy distillation and, to a lesser extent, policy improvement via return interventions within frameworks such as Decision Transformers. These recent results are somewhat atypical for reinforcement learning, as they do not rel... | reinforcement learning, transformers, policy evaluation, policy improvement, sequence modeling, compression | null | null | https://openreview.net/forum?id=6qtDu7hVPF | null | 2024 | Gregoire Deletang and Anian Ruoss and Li Kevin Wenliang and Elliot Catt and Tim Genewein and Jordi Grau-Moya and Marcus Hutter and Joel Veness | Generative Reinforcement Learning with Transformers | misc | deletang:2024:generative-reinforcement-learning-with-transformers | null | null | https://openreview.net/pdf?id=6qtDu7hVPF | null | null | null | null | null | null | Submitted to ICLR 2024 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://openreview.net/attachment?id=6qtDu7hVPF&name=supplementary_material | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The capabilities of today{'}s natural language processing systems are typically evaluated using large datasets of curated questions and answers. While these are critical benchmarks of progress, they also suffer from weakness due to artificial distributions and incomplete knowledge. Artifacts arising from artificial dis... | null | null | null | https://aclanthology.org/2021.conll-1.16 | Proceedings of the 25th Conference on Computational Natural Language Learning | 2021 | Demeter, David and Downey, Doug | Who{'}s on First?: Probing the Learning and Representation Capabilities of Language Models on Deterministic Closed Domains | inproceedings | demeter:2021:probing-learning-representation-language-models-closed-domains | null | null | null | 10.18653/v1/2021.conll-1.16 | 210--222 | null | null | null | November | null | Bisazza, Arianna and Abend, Omri | null | null | Association for Computational Linguistics | null | null | null | Online | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Creating a chess engine using language models is challenging due to the complexity of understanding the logic and depth of moves. To solve this problem, we propose several approaches with different types of training of different LLMs that already have some knowledge of tasks and not necessarily on chess, and adjust the... | Training, Deep learning, Social networking (online), Large language models, Retrieval augmented generation, Force, Transformers, Logic, Engines, Tuning, Deep learning, Transformers ,LLMs, RAG, FAISS, GPT, Similarity Search, Chess engine, Fine tuning | null | null | null | SoutheastCon 2025 | 2025 | Diallo, Kassim B. and Akhloufi, Moulay A. | ChessMoveLLM: Large Language Models for Chess Next Move Prediction | inproceedings | diallo:2025:chessmovellm-large-language-models-chess-next-move-prediction | null | null | null | 10.1109/SoutheastCon56624.2025.10971611 | 475--480 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
While generalization over tasks from easy to hard is crucial to profile language models (LLMs), the datasets with fine-grained difficulty annotations for each problem across a broad range of complexity are still blank. Aiming to address this limitation, we present Easy2Hard-Bench, a consistently formatted collection of... | null | null | null | https://www.microsoft.com/en-us/research/publication/easy2hard-bench-standardized-difficulty-labels-for-profiling-llm-performance-and-generalization/ | NeurIPS 2024 | 2024 | Ding, Mucong and Deng, Chenghao and Choo, Jocelyn and Wu, Zichu and Agrawal, Aakriti and Schwarzschild, Avi and Zhou, Tianyi and Goldstein, Tom and Langford, John and Anandkumar, A. and Huang, Furong | Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization | inproceedings | ding:2024:easy2hard-bench | null | null | null | null | null | null | null | null | September | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
In the previous chapter, we explored asynchronous programming with asyncio, learning how to handle multiple I/O-bound operations efficiently within a single thread. | null | null | null | https://doi.org/10.1007/979-8-8688-1261-3_17 | Deep Dive Python: Techniques and Best Practices for Developers | 2025 | Divakaran, Adarsh | Data Serialization and Persistence | inbook | divakaran:2025:data-serialization-persistence-deep-dive-python-techniques-best-practices-developers | null | null | null | 10.1007/979-8-8688-1261-3_17 | 531--588 | null | null | null | null | null | null | null | null | Apress | null | null | null | Berkeley, CA | null | null | null | null | 979-8-8688-1261-3 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess is a complex game that requires deep strategic thinking, pattern recognition, calculations, and creative problem-solving. Modeling strategic decision-making in chess endgames poses a unique challenge due to the game's high complexity and the uncertainty of human play. In this paper, we propose a hybrid analytical... | combinatorial game theory; CGT; optimal strategy; probabilistic modeling; graph-theoretic game tree analysis; chess | null | null | https://nhsjs.com/2025/beyond-perfect-play-a-combinatorial-and-probabilistic-approach-to-chess-endgame-strategy/ | null | 2025 | Divij Dogra | Beyond Perfect Play: A Combinatorial and Probabilistic Approach to Chess Endgame Strategy | article | dogra:2025:beyond-perfect-play-combinatorial-probabilistic-approach-chess-endgame-strategy | null | null | null | null | null | null | null | The National High School Journal of Science | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Large language models often possess latent capabilities that lie dormant unless explicitly elicited, or surfaced, through fine-tuning or prompt engineering. Predicting, assessing, and understanding these latent capabilities pose significant challenges in the development of effective, safe AI systems. In this work, we r... | elicitation, large language models, LLMs, latent capabilities, minimum description length | null | https://github.com/edonoway/quantifying-elicitation-neurips25 | https://openreview.net/forum?id=Dkgx2pS4Ww | The Thirty-ninth Annual Conference on Neural Information Processing Systems | 2025 | Elizabeth Donoway and Hailey Joren and Arushi Somani and Henry Sleight and Julian Michael and Michael R DeWeese and John Schulman and Ethan Perez and Fabien Roger and Jan Leike | Quantifying Elicitation of Latent Capabilities in Language Models | inproceedings | donoway:2025:quantifying-elicitation-latent-capabilities-language-models | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
With the growing capabilities of AI, technology is increasingly able to match or even surpass human performance. In the current study, focused on the game of chess, we investigated whether chess players could distinguish if they were playing against a human or a computer, and how they achieved this. A total of 24 chess... | null | null | null | https://www.sciencedirect.com/science/article/pii/S2451958824001295 | null | 2024 | Yke Bauke Eisma and Robin Koerts and Joost {de Winter} | Turing Tests in Chess: An Experiment Revealing the Role of Human Subjectivity | article | eisma:2024:turing-tests-chess-human-subjectivity | null | null | null | 10.1016/j.chbr.2024.100496 | 100496 | null | null | Computers in Human Behavior Reports | null | null | null | null | null | null | null | null | null | null | null | null | 2451-9588 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment, and (2) Analytical insights in natural language form, exposing the invaluable thought process or strategic considerations. Despite this, th... | null | https://huggingface.co/Waterhorse/ChessCLIP,https://huggingface.co/Waterhorse/chessgpt-base-v1,https://huggingface.co/Waterhorse/chessgpt-chat-v1 | https://github.com/waterhorse1/ChessGPT | null | Proceedings of the 37th International Conference on Neural Information Processing Systems | 2023 | Feng, Xidong and Luo, Yicheng and Wang, Ziyan and Tang, Hongrui and Yang, Mengyue and Shao, Kun and Mguni, David and Du, Yali and Wang, Jun | ChessGPT: bridging policy learning and language modeling | inproceedings | feng:2023:chessgpt-bridging-policy-learning-language-modeling | null | null | https://proceedings.neurips.cc/paper_files/paper/2023/file/16b14e3f288f076e0ca73bdad6405f77-Paper-Datasets_and_Benchmarks.pdf | null | null | null | null | null | null | null | null | null | NeurIPS '23 | Curran Associates Inc. | 47 | 316 | null | Red Hook, NY, USA | null | null | null | null | null | New Orleans, LA, USA | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to tackle these difficulties in the domain of chess puzzles. We start by benchmarking Generative AI architectures, and then introduce an RL fr... | null | null | null | https://arxiv.org/abs/2510.23881 | null | 2025 | Xidong Feng and Vivek Veeriah and Marcus Chiam and Michael Dennis and Ryan Pachauri and Thomas Tumiel and Federico Barbero and Johan Obando-Ceron and Jiaxin Shi and Satinder Singh and Shaobo Hou and Nenad Toma\v{s}ev and Tom Zahavy | Generating Creative Chess Puzzles | misc | feng:2025:generating-creative-chess-puzzles | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2510.23881 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | cs.AI | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to existing data? Can the learnable content in data be evaluated without considering a downstream task? On these questions, Shannon information and ... | null | null | null | https://arxiv.org/abs/2601.03220 | null | 2026 | Marc Finzi and Shikai Qiu and Yiding Jiang and Pavel Izmailov and J. Zico Kolter and Andrew Gordon Wilson | From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence | misc | finzi:2026:entropy-epiplexity-rethinking-information-computationally-bounded-intelligence | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2601.03220 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | cs.LG | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Modern chess language models are dense transformers trained on millions of games played by thousands of high-rated individuals. However, these monolithic networks tend to collapse into mode-averaged behavior, where stylistic boundaries are blurred, and rare but effective strategies are suppressed. To counteract homogen... | chess language modeling, mixture of experts, reinforcement learning, behavioral stylometry | null | https://anonymous.4open.science/r/mixture-of-masters | https://arxiv.org/abs/2602.04447 | null | 2026 | Giacomo Frisoni and Lorenzo Molfetta and Davide Freddi and Gianluca Moro | Mixture of Masters: Sparse Chess Language Models with Player Routing | misc | frisoni:2026:mixture-masters-sparse-chess-language-models-player-routing | null | null | null | null | null | null | null | null | null | Submitted to ICLR 2026 https://openreview.net/forum?id=lnIlH0hfek | null | null | null | null | null | null | null | null | 2602.04447 | null | null | null | null | null | Sparse Chess Language Models with Player Routing | mixture-of-masters | null | null | null | null | null | null | null | null | cs.LG | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess enhances problem-solving and decisionmaking skills. Traditional chess often features static difficulty, which can frustrate novices or bore masters. Dynamic Difficulty Adaptation (DDA) addresses this by adjusting the game's challenge based on player performance, enabling personalized learning and engagement. DDA ... | Accuracy;Games;Machine learning;Ubiquitous computing;Timing;Problem-solving;Engines;Game Analytics;Dynamic Difficulty Adaption;Chess;Machine Learning | null | null | null | 2025 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) | 2025 | Gamal, Mohamed and Aboulhassan, Amal and Hassan, Yomna M.I. | Machine Learning Based Dynamic Difficulty Adaptation for Chess | inproceedings | gamal:2025:machine-learning-based-dynamic-difficulty-adaptation-chess | null | null | null | 10.1109/MIUCC66482.2025.11196851 | 9--14 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The presence or absence of winner-loser effects is a widely discussed phenomenon across both sports and psychology research. Investigation of such effects is often hampered by the limited availability of data. Online chess has exploded in popularity in recent years and provides vast amounts of data which can be used to... | Winner-loser Effects, Chess, Hierarchical Bayesian Modeling, online competitions | null | https://github.com/OwenWard/Chess_Winner | https://doi.org/10.1515/jqas-2025-0035 | null | 2025 | Adam Gee and Sydney O. Seese and James P. Curley and Owen G. Ward | Investigating experiential effects in online chess using a hierarchical Bayesian analysis | article | gee:2025:investigating-experiential-effects-online-chess-hierarchical-bayesian-analysis | null | null | https://www.degruyterbrill.com/document/doi/10.1515/jqas-2025-0035/pdf | doi:10.1515/jqas-2025-0035 | null | null | null | Journal of Quantitative Analysis in Sports | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://zenodo.org/records/17247312 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The aim of this research was to investigate the effects of transcranial direct current stimulation (tDCS) on risky decision-making in student chess players, taking into account their personality traits. In this study, 28 high school students who were active in chess and participated in provincial and national chess lea... | Extroverted,Brain stimulation,Risky Decision-making,Introverted,Chess | null | null | https://spsyj.ssrc.ac.ir/article_4394_55f2b660d8300f25206eb52a483e0bb4.pdf | null | 2025 | Ghayebzadeh, Shahrouz and Moharramzadeh, Mehrdad and Zoghi, Maryam | The Effect of Transcranial Direct Current Stimulation on Risky Decision-Making of Student Chess Players Based on their Introverted and Extroverted Personality Traits | article | ghayebzadeh:2025:effect-transcranial-current-decision-making-chess-personality | null | null | null | 10.22089/spsyj.2025.17731.2546 | null | null | null | Sport Psychology Studies | null | null | null | null | null | Sport Sciences Research institute | null | null | null | null | null | null | 2345-2978 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2538-1504 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Puzzle-solving has long served as a benchmark for evaluating artificial intelligence, testing a model's ability to reason, infer, and strategize across complex problem spaces. Traditional AI and machine learning methods, such as symbolic reasoning and reinforcement learning, have made notable strides in structured doma... | Large Language Models; Reasoning; Puzzle Solving; Prompting; Neurosymbolic Methods | null | null | https://dspace.lib.ntua.gr/xmlui/handle/123456789/61469 | null | 2025 | Giadikiaroglou, Panagiotis | Investigating the capabilities of language models in puzzle reasoning: A survey and experimental analysis | thesis | giadikiaroglou:2025:investgiating-capabilities-language-models-puzzles | Bachelor's thesis | Giorgos Stamou | null | http://dx.doi.org/10.26240/heal.ntua.29165 | null | null | null | null | March | null | null | null | null | National Technological University of Athens | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
We extend the Stainless deductive verifier with floating-point support, providing the first automated verification support for floating-point numbers for a subset of Scala that includes polymorphism, recursion and higher-order functions. We follow the recent approach in the KeY verifier to axiomatise reasoning about ma... | null | null | null | https://arxiv.org/abs/2601.14059 | null | 2026 | Andrea Gilot and Axel Bergstr\"{o}m and Eva Darulova | Verifying Floating-Point Programs in Stainless | misc | gilot:2026:verifying-floating-point-programs-stainless | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2601.14059 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | cs.PL | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess engines have played a fundamental role in the advancement of articial intelligence applied to the game since the mid-20th century. Today, Stocksh, the most powerful and open source chess engine, still relies on alpha-beta pruning, but also incorporates machine learning techniques. The goal of this project is to d... | Articial Intelligence, Chess Engine, Alpha-beta pruning, Iterative deepening, Quiescence search, Move ordering, Transposition table, Zobrist hashing, Magic bitboards | null | https://github.com/LauraWangQiu/AlphaDeepChess | https://hdl.handle.net/20.500.14352/123857 | null | 2025 | Gir{\'o}n Herranz, Juan and Wang Qiu, Yi | AlphaDeepChess: chess engine based on alpha-beta pruning | thesis | giron:2025:alpha-deep-chess-chess-engine-alpha-beta-pruning | Grado en Ingenier\'{\i}a de Computadores y Grado en Desarrollo de Videojuegos | F\'{a}bregas Alfaro, Ignacio and Rubio Cu\'{e}llar, Rub\'{e}n Rafael | null | 20.500.14352/123857 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Universidad Complutense de Madrid | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Language Models have not acquired their popularity based only on their text-generation capabilities, but also for the ability of learning they do have. An exploration of these capabilities over chess is carried out. With chess, it allows to process the game as a Natural Language problem. Analysing its capabilities of r... | GPT-4, GPT-3, Modelos ling\"{u}\'{\i}sticos GPT, Modelos ling\"{u}\'{\i}sticos (LM), Machine Learning (ML), Artificial Intelligence (AI), GPT Language models, Language models (LMs) | null | null | https://riunet.upv.es/handle/10251/197801 | null | 2023 | Albert Gramaje, Borja | Exploring GPT's Capabilities in Chess-Puzzles | thesis | gramaje:2023:exploring-gpt-capabilities-chess-puzzles | mathesis | Ferri Ram\'{i}rez, C\'{e}sar | https://riunet.upv.es/server/api/core/bitstreams/8dc80122-7f5c-40d2-9844-cd40d5a943d7/content | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Valencia, Spain | null | null | null | null | null | null | null | null | null | Universitat Polit\`{e}cnica de Val\`{e}ncia | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
In this paper we present results from recent experiments that suggest that chess players associate emotions to game situations and reactively use these associations to guide search for planning and problem solving. We report on a pilot experiment with multi-modal observation of human experts engaged in solving challeng... | chess, chunking, cognitive models, multimodal observation of gaze and emotion, situation models, working memory | null | null | https://doi.org/10.1145/3279810.3279846 | Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data | 2018 | Guntz, Thomas and Crowley, James L. and Vaufreydaz, Dominique and Balzarini, Raffaella and Dessus, Philippe | The role of emotion in problem solving: first results from observing chess | inproceedings | guntz:2018:role-emotion-problem-solving-first-results-observing-chess | null | null | https://dl.acm.org/doi/pdf/10.1145/3279810.3279846 | 10.1145/3279810.3279846 | null | null | null | null | null | null | null | null | MCPMD '18 | Association for Computing Machinery | 8 | 12 | null | New York, NY, USA | null | null | null | null | 9781450360722 | Boulder, Colorado | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This paper attempts to generate point values for chess pieces, as alternatives to the commonly accepted chess piece values. We use a database of over a million online chess games to heuristically determine the value of a chess piece, by using material imbalances to predict game results. We then explore how piece values... | null | null | null | https://www.jsr.org/hs/index.php/path/article/view/4356 | null | 2023 | Gupta, Aditya and Grattoni, Christopher and Gupta, Arnav | Determining Chess Piece Values Using Machine Learning | article | gupta:2023:determining-chess-piece-values-machine-learning | null | null | null | 10.47611/jsrhs.v12i1.4356 | null | 1 | 12 | Journal of Student Research | February | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Houston, USA | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
With large chess-playing neural network models like AlphaZero contesting the state of the art within the world of computerised chess, two challenges present themselves: the question of how to explain the domain knowledge internalised by such models, and the problem that such models are not made openly available. This w... | null | null | https://github.com/patrik-ha/ii-map | https://doi.org/10.1038/s41598-024-70701-2 | null | 2024 | Hammersborg, Patrik and Str{\"u}mke, Inga | Information based explanation methods for deep learning agents---with applications on large open-source chess models | article | hammersborg:2024:information-based-explanation-methods-deep-learning-agents-applications-large-open-source-chess-models | null | null | null | 10.1038/s41598-024-70701-2 | 20174 | 1 | 14 | Scientific Reports | August | null | null | https://patrik-ha.github.io/ii-map/ | null | null | null | null | null | null | null | null | 2045-2322 | null | null | null | null | null | null | null | null | null | 30 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
In games like chess, strategy evolves dramatically across distinct phases - the opening, middlegame, and endgame each demand different forms of reasoning and decision-making. Yet, many modern chess engines rely on a single neural network to play the entire game uniformly, often missing opportunities to specialize. In t... | null | null | https://github.com/QueensGambit/CrazyAra | https://arxiv.org/abs/2401.16852 | null | 2025 | Felix Helfenstein and Johannes Czech and Jannis Bl\"{u}ml and Max Eisel and Kristian Kersting | Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess | misc | helfenstein:2025:checkmating-one-using-many-combining-mixture-experts-mcts-improve-chess | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2401.16852 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | cs.LG | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Humans are social beings, and most of our decisions are influenced by considerations of how others will respond. Whether in poker or political negotiations, the riskiness of a decision is often determined by the variance of the other party's possible responses. Such socially-contingent decisions can be framed in terms ... | risk taking; adversarial games; chess | null | https://github.com/choldawa/Chess | https://escholarship.org/uc/item/403764rd | Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, CogSci 2021, virtual, July 26-29, 2021 | 2021 | Cameron Holdaway and Ed Vul | Risk-taking in adversarial games: What can 1 billion online chess games tell us? | inproceedings | holdaway:2021:risk-taking-adversarial-games-what-billion-chess-games-tell-us | null | null | null | null | null | null | null | null | null | null | W. Tecumseh Fitch and Claus Lamm and Helmut Leder and Kristin Te{\ss}mar{-}Raible | null | null | cognitivesciencesociety.org | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://www.linkedin.com/pulse/what-can-1-billion-chess-games-tell-us-risk-taking-cameron-holdaway/ | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Chess is a strategy board game with its inception dating back to the 15th century. The Covid-19 pandemic has led to a chess boom online with 95,853,038 chess games being played during January, 2021 on lichess.com. Along with the chess boom, instances of cheating have also become more rampant. Classifications have been ... | null | null | null | null | null | 2021 | Hoque, Masudul | Classification of Chess Games: An Exploration of Classifiers for Anomaly Detection in Chess | thesis | hoque:2022:classification-chess-games-exploration-classifiers-anomaly-detection-chess | mathesis | Premarathna, Galkande Iresha | https://cornerstone.lib.mnsu.edu/cgi/viewcontent.cgi?article=2118&context=etds | null | null | null | null | null | null | https://cornerstone.lib.mnsu.edu/etds/1119/ | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Minnesota State University, Mankato | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This paper introduces ChessLM, a novel Transformer-based model designed to learn rich, contextual vector representations (embeddings) of chess positions. Moving beyond traditional chess engines focused on move evaluation, our approach is inspired by the success of self-supervised learning in Natural Language Processing... | Chess, Transformers, Machine Learning, Embeddings | https://huggingface.co/odestorm1/chesslm | https://github.com/bluehood/Encoder-ChessLM | https://bluehood.github.io/research/benh_Beyond_Evaluation__Learning_Contextual_Chess_Position_Representations_2025.pdf | null | 2025 | Ben Hull | Beyond Evaluation: Learning Contextual Chess Position Representations | misc | hull:2025:beyond-evaluation-learning-contextual-chess-position-representations | null | null | https://bluehood.github.io/research/benh_Beyond_Evaluation__Learning_Contextual_Chess_Position_Representations_2025.pdf | null | null | null | null | null | null | Technical report | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Accessed via \url{[https://bluehood.github.io/](https://bluehood.github.io/)} | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This paper examines pedagogical approaches and instructional tools for teaching chess in higher education. Chess instruction in universities can serve disciplinary goals (e.g., sport sciences, cognitive psychology), cross-curricular goals (critical thinking, problem solving), and extra-curricular objectives (wellness, ... | chess education, higher education, pedagogy, constructivism, blended learning, assessment, chess engines,digital boards, transferable skills | null | null | https://egarp.lt/index.php/JPURM/article/view/460 | null | 2025 | Huseynova, Kifayet and Novruzova, Aide | Methods and Tools for Teaching Chess in Higher Education | article | huseynova:2025:methods-tools-teaching-chess-higher-education | null | null | null | 10.69760/portuni.0110018 | 176--186 | 10 | 1 | Porta Universorum | December | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | 2025 | Dongyoon Hwang and Hojoon Lee and Jaegul Choo and Dongmin Park and Jongho Park | Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess | article | hwang:2025:can-large-language-models-develop-strategic-reasoning-post-training-insights-learning-chess | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This study addresses the challenge of distinguishing between human and computer-generated play in chess, crucial for ensuring the integrity and fairness of both onlineand tournament play. As unauthorized computer assistance becomes increasinglysophisticated, we utilize sequential neural networks to analyze a vast datas... | Chess, Cheating, AI, Neural Networks | null | null | https://ceur-ws.org/Vol-3885/paper13.pdf | Proceedings of 29th International Conference Information Society and University Studies | 2024 | Iavich, Maksim and Kevanishvili, Zura | Detecting Fair Play Violations in Chess Using Neural Networks | inproceedings | iavich:2024:detecting-fair-play-violations-chess-neural-networks | null | null | null | null | 121--127 | null | 3341 | null | null | null | null | null | {CEUR} Workshop Proceedings | CEUR-WS.org | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://www.youtube.com/watch?v=hJ7POry_q6U | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
ChessFormer introduces a novel searchless chess engine leveraging transformer architecture to approximate human decision-making in chess. Trained on a vast dataset of 3 billion chess positions, our model learns its entire decision-making process directly from training data. Evaluations show an improvement in human move... | null | null | null | null | Modeling Decisions for Artificial Intelligence | 2026 | Zeman, Jakub and {\v{C}}epek, Miroslav | ChessFormer - Modeling Human Decision Making in Chess | inproceedings | jakub:2026:chessformer-modeling-human-decision-making-chess | null | null | null | null | 42--53 | null | null | null | null | null | Torra, Vicen{\c{c}} and Narukawa, Yasuo and Domingo-Ferrer, Josep | null | null | Springer Nature Switzerland | null | null | null | Cham | null | null | null | null | 978-3-032-00891-6 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Do neural networks learn to implement algorithms such as look-ahead or search "in the wild"? Or do they rely purely on collections of simple heuristics? We present evidence of learned look-ahead in the policy and value network of Leela Chess Zero, the currently strongest deep neural chess engine. We find that Leela int... | null | null | https://github.com/HumanCompatibleAI/leela-interp | https://proceedings.neurips.cc/paper_files/paper/2024/file/37d9f19150fce07bced2a81fc87d47a6-Paper-Conference.pdf | Advances in Neural Information Processing Systems | 2024 | Jenner, Erik and Kapur, Shreyas and Georgiev, Vasil and Allen, Cameron and Emmons, Scott and Russell, Stuart | Evidence of Learned Look-Ahead in a Chess-Playing Neural Network | inproceedings | jenner:2024:evidence-lookahead-chess-neural-network | null | null | https://proceedings.neurips.cc/paper_files/paper/2024/file/37d9f19150fce07bced2a81fc87d47a6-Paper-Conference.pdf | 10.52202/079017-0987 | 31410--31437 | null | 37 | null | null | null | A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang | https://leela-interp.github.io/ | null | Curran Associates, Inc. | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Although pretrained large language models (LLMs) can generate convincing natural language about games like chess, they lack positional and contextual knowledge and as such are poor game-playing agents. In this project, I utilize language pretaining; instruction fine-tuning, an additional training regimen with chess-spe... | null | null | null | https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1234/final-reports/final-report-169466939.pdf | null | 2023 | Bowen Jiang | Building a Natural Language Chess Engine with Pretraining and Instruction Fine-Tuning | misc | jiang:2023:building-natural-language-chess-engine-pretraining-instruction-finetunine | null | null | null | null | null | null | null | null | null | Stanford CS224N Custom Project, Winter 2023 (https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1234/project.html) | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
We consider supervised learning (regression/classification) problems with tensor-valued input. We derive multi-linear sufficient reductions for the regression or classification problem by modeling the conditional distribution of the predictors given the response as a member of the quadratic exponential family. We devel... | null | null | null | https://arxiv.org/abs/2502.20216 | null | 2025 | Daniel Kapla and Efstathia Bura | Generalized Multi-Linear Models for Sufficient Dimension Reduction on Tensor Valued Predictors | misc | kapla:2025:generalized-multi-linear-models-dimension-reduction-tensor-valued-predictors | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2502.20216 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | stat.ME | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
In this paper, we address the problem of finding similar chess puzzles to a given query puzzle using a dataset of one million puzzles. We approach this problem through an information retrieval (IR) perspective. Chess positions can be compared in mainly two aspects, positional similarity and dynamic similarity. We exper... | null | null | null | null | Computer Science Engineering: Proceedings of the 1st International Conference on Computing and Intelligent Information Systems (ICCIIS 2024), Bangalore, India, 19-20th April, 2024 Volume 1 | 2024 | Karn, Aryan and Biradar, Chinmay Anil and Puranik, Aryan and Kireeti, Attili Krishna and Jayashree, R | Personalized recommendation of chess puzzles | inproceedings | karn:2024:personalized-recommendation-chess-puzzles | null | null | null | null | 29 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | CRC Press | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The increasing demand for robust and scalable encryption algorithms is driven by rapid advancements in computing technology, and emerging technologies like quantum cryptanalysis pose significant threats to information security. This paper presents a novel trinary-based multistage encryption algorithm designed for scala... | Encryption;Steganography;Security;Tensors;Table lookup;Encoding;Music;Heuristic algorithms;Ciphers;Transforms;Cryptography;Encryption;Generative Steganography;Transforms;Trinary Encoding | null | null | null | null | 2026 | Kaushal Karthik, K M and Ramesh, R | GenSTEG: A Light and Scalable Trinary-Based Encryption with Multimodal Generative Steganography | article | karthik:2026:gensteg-light-scalable-trinary-based-encryption-multiomodal-generative-steganography | null | null | null | 10.1109/ACCESS.2026.3665790 | null | null | null | IEEE Access | null | null | null | null | null | null | null | null | null | null | null | null | 2169-3536 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Language models have shown unprecedented capabilities, sparking debate over the source of their performance. Is it merely the outcome of learning syntactic patterns and surface level statistics, or do they extract semantics and a world model from the text? Prior work by Li et al. investigated this by training a GPT mod... | GPT, large language model, interpretability, world model | https://huggingface.co/adamkarvonen/chess_llms | https://github.com/adamkarvonen/chess_llm_interpretability | https://openreview.net/forum?id=PPTrmvEnpW | First Conference on Language Modeling | 2024 | Adam Karvonen | Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models | inproceedings | karvonen:2024:emergent-world-models-latent-variable-estimation-chess-playing | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | We train a GPT model from scratch to play chess and find that it learns to compute board state and estimate player Elo. We use these representations to edit the GPT's internal board state and increase or decrease its chess-playing ability. | null | null | null | null | null | null | null | https://huggingface.co/datasets/adamkarvonen/chess_games | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
What latent features are encoded in language model (LM) representations? Recent work on training sparse autoencoders (SAEs) to disentangle interpretable features in LM representations has shown significant promise. However, evaluating the quality of these SAEs is difficult because we lack a ground-truth collection of i... | Language models, interpretability, dictionary learning | null | https://github.com/adamkarvonen/SAE_BoardGameEval | https://proceedings.neurips.cc/paper_files/paper/2024/file/9736acf007760cc2b47948ae3cf06274-Paper-Conference.pdf | Advances in Neural Information Processing Systems | 2024 | Karvonen, Adam and Wright, Benjamin and Rager, Can and Angell, Rico and Brinkmann, Jannik and Smith, Logan and Mayrink Verdun, Claudio and Bau, David and Marks, Samuel | Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models | inproceedings | karvonen:2024:measuring-progress-dictionary-learning-language-model-interpretability-board-games-models | null | null | null | 10.52202/079017-2644 | 83091--83118 | null | 37 | null | null | An older version of this paper was previously published at the ICML 2024 Workshop on Mechanistic Interpretability: https://openreview.net/forum?id=qzsDKwGJyB | A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang | https://nips.cc/virtual/2024/poster/95121 | null | Curran Associates, Inc. | null | null | null | null | null | null | null | null | null | null | We measure progress in training sparse autoencoders for LM interpretability by working in the setting of LMs trained on chess and Othello. | null | https://nips.cc/media/neurips-2024/Slides/95121_AxaqEUR.pdf | null | null | null | null | null | null | null | null | null | null | null | null | null | https://slideslive.com/39025524/measuring-progress-in-dictionary-learning-for-language-model-interpretability-with-board-game-models | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Android and iOS are the two dominant mobile operating systems in the rapidly expanding smartphone market, serving billions of users worldwide. Both platforms feature extensive app stores with millions of applications available for download. While security measures are in place to prevent the distribution of malicious o... | Mobile Security; Android; iOS; Binary Analysis; Cross-Platform Analysis | null | null | http://hdl.handle.net/20.500.12708/217584 | null | 2025 | Keusch, Alexander | Binary Matching of Android and iOS Apps | thesis | keusch:2025:binary-matching-android-ios-apps | Diploma Thesis | Lindorfer, Martina and Bleier, Jakob | null | 10.34726/hss.2025.128603 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Technische Universit\"{a}t Wien | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The rapid growth of online chess has intensified the challenge of distinguishing engine-assisted from authentic human play, exposing the limitations of existing approaches that rely solely on deterministic evaluation metrics. This study introduces a proof-of-concept hybrid framework for discriminating between engine-li... | null | null | null | https://www.mdpi.com/2571-5577/9/1/11 | null | 2026 | Kevanishvili, Zura and Iavich, Maksim | A Hybrid Human-Centric Framework for Discriminating Engine-like from Human-like Chess Play: A Proof-of-Concept Study | article | Kevanishvili:2026:hybrid-human-centric-framework-discriminating-engine-like-human-like-chess-play-proof-concept-study | null | null | null | 10.3390/asi9010011 | null | 1 | 9 | Applied System Innovation | null | null | null | null | null | null | null | null | null | null | null | null | 2571-5577 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | online chess; gameplay integrity analysis; hybrid system design; stylometric modeling; centipawn loss; move match probability; convolutional neural networks; explainable AI; human-AI interaction; applied system innovation | 11 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The growing adoption of the Internet of Things (IoT) highlights the need for intuitive, accessible, and screenless modes of interaction. Voice interfaces, combining speech-to-text (STT) and text-to-speech (TTS) processing, provide a natural mechanism for controlling IoT systems while enabling inclusive user experiences... | Training;Pipelines;Prototypes;Process control;Computer architecture;Real-time systems;Text to speech;Systems support;Internet of Things;Speech to text;Internet of Things (IoT);voice interface;speech-to-text (STT);text-to-speech (TTS);embedded systems;accessibility;blindfold chess;Lichess API | null | null | null | 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) | 2025 | Khamele, Ojas and Lambe, Amruta and Pawar, Praveen | A Modular Voice-Controlled IoT Architecture for Screenless Real-Time Interaction | inproceedings | khamele:2025:modular-voice-controlled-iot-architecture-screenless-real-time-interaction | null | null | null | 10.1109/CICN67655.2025.11367883 | 1686--1690 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The 2024 Chessable Research Awards had five student winners, including Alex Knopps, the author of this guest blog post. Knopps explores whether solving chess puzzles alone or with a partner leads to better outcomes. His research also accounted for the difficulty of puzzles. The results indicate that there wasn't much d... | null | null | null | https://www.chessable.com/blog/collaborative-versus-individual-chess-puzzle-solving/ | null | 2025 | Knopps, Alex | Collaborative versus Individual Chess Puzzle Solving | online | knopps:collaborative-vs-individual-chess-puzzle-solving | null | null | null | null | null | null | null | null | February | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2025-03-14 | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
We introduce LLM CHESS, an evaluation framework designed to probe the generalization of reasoning and instruction-following abilities in large language models (LLMs) through extended agentic interaction in the domain of chess. We rank over 50 open and closed source models by playing against a random opponent using a ra... | null | null | https://github.com/LLM-CHESS/llm_chess_minimal, https://github.com/maxim-saplin/llm_chess/ | null | Workshop on Foundations of Reasoning in Language Models at NeurIPS 2025 | 2025 | Kolasani, Sai and Saplin, Maxim and Crispino, Nicholas and Montgomery, Kyle and Davis, Jared and Zaharia, Matei and Wang, Chi and Wang, Chenguang | LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess | inproceedings | kolasani:2025:llm-chess-benchmarking-reasoning-instruction-following-llms-through-chess | null | null | null | null | null | null | null | null | null | null | null | https://maxim-saplin.github.io/llm_chess/ | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
State-of-the-art reinforcement learning agents are capable of outperforming human experts at games like chess, Go, and StarCraft II. These agents do not simply take advantage of their digital hardware in being able to react and calculate faster than humans, but employ better strategies that lead to more victories. Inte... | null | null | null | https://doi.org/10.1201/9781003355281-6 | Proceedings of the Explainable Agency in Artificial Intelligence Workshop, 36th AAAI Conference on Artificial Intelligence | 2022 | Krishnan, Abhijeet and Martens, Chris | Towards the automatic synthesis of interpretable chess tactics | inproceedings | krishnan:2022:automatic-synthesis-interpretable-chess-tactics | null | null | https://abhijeetkrishnan.me/publications/eaai-22/Interpretable_Chess_Tactics.pdf | 10.1201/9781003355281-6 | 91--97 | null | null | null | March | null | null | null | null | American Association of Artificial Intelligence | null | null | null | null | null | null | null | null | null | null | null | null | https://abhijeetkrishnan.me/publications/eaai-22/EAAI_22_Presentation.pdf | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Competitive games admit a wide variety of player strategies and emergent, domain-specific concepts that are not obvious from an examination of their rules. Expert agents trained on these games demonstrate many useful strategies, but these are difficult for human players to understand and adopt. Algorithmically revealin... | null | null | https://github.com/AbhijeetKrishnan/interpretable-chess-tactics | null | Proceedings of the Workshop on Artificial Intelligence for Strategy Games (SG) and Esports Analytics (EA), 18th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment | 2022 | Krishnan, Abhijeet and Martens, Chris | Synthesizing interpretable chess tactics from player games | inproceedings | krishnan:2022:synthesizing-interpretable-chess-tactics-player-games | null | null | https://www.convivial.tools/PapersPublic/aiide22-synthesizing-tactics.pdf | null | null | null | null | null | October | The title in the paper from the proceedings is "Synthesizing chess tactics from player games" | null | null | null | American Association for Artificial Intelligence | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Smartphone usage data can provide valuable insights for understanding interaction with technology and human behavior. However, collecting large-scale, in-the-wild smartphone usage logs is challenging due to high costs, privacy concerns, under representative user samples and biases like non-response that can skew result... | null | null | null | https://arxiv.org/abs/2509.13892 | null | 2025 | Gustavo Kruger and Nikhil Sachdeva and Michael Sobolev | Synthetic Data Generation for Screen Time and App Usage | misc | kruger:2025:synthetic-data-generation-screen-time-app-usage | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2509.13892 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | cs.HC | arXiv | null | null | null | null | null | null | null | null | null | https://osf.io/u2h3d/ | null | null | null | null | null | null | null | null | null | null | null | null | null |
Accurately estimating human skill levels is crucial for designing effective human-AI interactions so that AI can provide appropriate challenges or guidance. In games where AI players have beaten top human professionals, strength estimation plays a key role in adapting AI behavior to match human skill levels. In a previ... | null | null | null | https://doi.org/10.48550/arXiv.2505.00279 | null | 2025 | Kyota Kuboki and Tatsuyoshi Ogawa and Chu{-}Hsuan Hsueh and Shi{-}Jim Yen and Kokolo Ikeda | Policies of Multiple Skill Levels for Better Strength Estimation in Games | article | kuboki:2025:policies-multiple-skill-levels-better-strength-estimation-games | null | null | null | 10.48550/ARXIV.2505.00279 | null | null | abs/2505.00279 | CoRR | null | null | null | null | null | null | null | null | null | null | 2505.00279 | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This paper presents a novel AI-driven chess engine that integrates a lightweight deep learning architecture with an uncertainty-aware Monte Carlo Tree Search (MCTS) framework. Unlike traditional engines that rely on brute-force search, our model utilizes reinforcement learning with a transformer-based neural network to... | Accuracy;Monte Carlo methods;Computational modeling;Decision making;Reinforcement learning;Transformers;Computational efficiency;Artificial intelligence;Optimization;Engines;AI chess engine;reinforcement learning;monte carlo tree search (MCTS);deep learning;transformer-based models;proximal policy optimization (PPO) | null | null | null | 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) | 2025 | D, Girish Kumar and Shiva Kumar, K S and Rama Prasad, P Pani and Jalade, Sangamesh C and Praveen Kumar, C T M and D C, Subhashree | Optimizing AI-Driven Chess Bots: Strategies for Balancing Performance, Accuracy, and Computational Efficiency | inproceedings | kumar:2025:optimizing-ai-driven-chess-bots-strategies-balancing-performance-accuracy-computational-efficiency | null | null | null | 10.1109/ICDCECE65353.2025.11035271 | 1--5 | null | null | null | April | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This study expands on previous surveys of computational theory of mind (ToM) focusing on four key areas. Data: We attempt to characterize data needed for this research and propose creating procedurally generated, multi-modal synthetic data for training and testing ToM systems, addressing the lack of open-source data of... | theory of mind, game theory, multi-agent, machine-learning, artificial intelligence, intention, adversarial dynamics, computational, automation | null | null | null | HCI International 2025 -- Late Breaking Papers | 2026 | Kumar, Prabhat and Zaroukian, Erin and Summers-Stay, Douglas and Raglin, Adrienne | Directions for Computational Theory of~Mind: Data, Metrics, Models and~Mathematical Formalization | inproceedings | kumar:2026:directions-computational-theory-of-mind-data-metrics-models-mathematical-formalization | null | null | null | null | 53--70 | null | null | null | null | null | Degen, Helmut and Ntoa, Stavroula | null | null | Springer Nature Switzerland | null | null | null | Cham | null | null | null | null | 978-3-032-13184-3 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Acting intelligently in complex environments poses a challenging learning problem: faced with many different situations and possible actions, how do people learn which action to take in each situation? While traditional laboratory-based experiments have been used to study specific learning mechanisms, these experiments... | decision-making, learning, reinforcement learning, social learning | null | https://github.com/ionatankuperwajs/learning-openings | osf.io/preprints/psyarxiv/d8zje | null | 2024 | Kuperwajs, Ionatan and van Opheusden, Bas and Russek, Evan and Griffiths, Tom | Learning from rewards and social information in naturalistic strategic behavior | article | kuperwajs:2024:learning-from-rewards-social-information-strategic-behavior | null | null | null | 10.31234/osf.io/d8zje | null | null | null | null | August | null | null | null | null | PsyArXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Human planning is incredibly efficient. Even in complex situations with many possible courses of action, people are able to make good decisions. Recent proposals suggest that a primary contributor to this efficiency is the intelligent use of cognitive resources, but how people allocate these resources under time constr... | null | null | null | https://escholarship.org/uc/item/75b4m9c2 | null | 2025 | Kuperwajs, Ionatan and Russek, Evan and Schut, Lisa and Sagiv, Yotam and Mattar, Marcelo G and Ma, Wei Ji and Griffiths, Tom | Exploring resource-rational planning under time pressure in online chess | article | kuperwajs:2025:exploring-resource-rational-planning-time-pressure-online-chess | null | null | null | null | null | null | 47 | Proceedings of the Annual Meeting of the Cognitive Science Society | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Online game providers face the challenge of preventing malicious users (cheaters) from breaking the rules and winning games through illegal means. This issue in particular plagues the online chess scene, where the strongest algorithms have long surpassed the world's best players - any cheater can beat the best human p... | null | null | null | https://doi.org/10.1007/978-3-031-34017-8_14 | Computers and Games - International Conference, {CG} 2022, Virtual Event, November 22-24, 2022, Revised Selected Papers | 2022 | Thijs Laarhoven and Aditya Ponukumati | Towards Transparent Cheat Detection in Online Chess: An Application of Human and Computer Decision-Making Preferences | inproceedings | laarhoven:2022:transparent-cheat-detection-online-chess | null | null | null | 10.1007/978-3-031-34017-8_14 | 163--180 | null | 13865 | null | null | null | Cameron Browne and Akihiro Kishimoto and Jonathan Schaeffer | null | Lecture Notes in Computer Science | Springer | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the pro... | Deep neural network, Computer vision, Visual attention, Chess | null | null | https://doi.org/10.1145/3314111.3319827 | Proceedings of the 11th {ACM} Symposium on Eye Tracking Research & Applications, {ETRA} 2019, Denver , CO, USA, June 25-28, 2019 | 2019 | Justin Le Louedec and Thomas Guntz and James L. Crowley and Dominique Vaufreydaz | Deep learning investigation for chess player attention prediction using eye-tracking and game data | inproceedings | le-louedec:2019:chess-player-attention-prediction | null | null | https://dl.acm.org/doi/pdf/10.1145/3314111.3319827 | 10.1145/3314111.3319827 | 1:1--1:9 | null | null | null | null | null | Krzysztof Krejtz and Bonita Sharif | null | null | {ACM} | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
In the modern age of computing and technology, computer vision has become a key aspect of numerous innovations and solutions that make everyday life easier. Object recognition in images is one area where computer vision can contribute to significant improvements. Playing chess, one of the oldest and most challenging in... | null | null | null | null | New Technologies, Development and Application VIII | 2025 | Leme{\v{s}}, Samir and Koli{\'{c}}, Mirhad and Tabak, Edin | Computer Vision for Chess Game Automation | inproceedings | lemes:2025:computer-vision-chess-game-automation | null | null | null | null | 21--30 | null | null | null | null | null | Karabegovi{\'{c}}, Isak and Kova{\v{c}}evi{\'{c}}, Ahmed and Mand{\v{z}}uka, Sadko | null | null | Springer Nature Switzerland | null | null | null | Cham | null | null | null | null | 978-3-031-95197-8 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This study presents a few-shot embedding learning approach to predict the behavior of individual chess players, based on only 100 games. Traditional models have relied on extensive datasets, often requiring thousands of games to achieve accurate move predictions. In contrast, our method leverages a limited number of ga... | null | null | null | https://fse.studenttheses.ub.rug.nl/id/eprint/34065 | null | 2024 | August, Lennart | A Few-Shot Embedding Learning Approach for Predicting the Behavior of Individual Chess Players | thesis | lennart:2024:few-shot-embedding-learning-approach-predicting-behvaior-individual-chess-players | Bachelor's thesis | Abreu, Steven and Jaeger, Herbert | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | University of Groningen | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
This thesis investigates how neural networks can be used to analyze and compare chess player styles. Using the last layer of Stockfish's neural network, we process positions from historical World Chess Championships (1886–2024), as well as the 2024 World Blitz and Rapid Championships. We apply dimensionality reduction ... | neural network, chess, PCA, t-SNE, MDS, Kmeans, kde | null | null | https://hdl.handle.net/2078.2/42841 | null | 2025 | Lequenne, Victor | Characterizing Chess Player Styles with Neural Network Embeddings from Stockfish | thesis | lequenne:2025:characterizing-chess-player-styles-neural-networks-embeddings-stockfish | Master's thesis | Delvenne, Jean-Charles | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | \'{E}cole polytechnique de Louvain, Universit\'{e} catholique de Louvain | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
FedCSIS 2025 competition is to predict the difficulty of chess puzzles, we present a structured multi-stage regression pipeline developed for the FedCSIS 2025 Challenge. The approach consists of three stages: (i) four Elo-banded base models trained on separate rating ranges to capture localized difficulty semantics and... | null | null | null | http://dx.doi.org/10.15439/2025F1698 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Alan Liang and Cenzhi Liu and Kai Wang and Ethan Liu | A Stacking-Based Ensemble Approach for Predicting Chess Puzzle Difficulty | inproceedings | liang:2025:stacking-based-ensemble-approach-predicting-chess-puzzle-difficulty | null | null | null | 10.15439/2025F1698 | 819--824 | null | 43 | null | null | null | Marek Bolanowski and Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik \'{S}l\k{e}zak | null | Annals of Computer Science and Information Systems | IEEE | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Recent large language models (LLMs) have shown strong reasoning capabilities. However, a critical question remains: do these models possess genuine reasoning skills particularly complex strategic reasoning or are they primarily excelling at sophisticated pattern recognition within their training data? To address this q... | null | null | null | https://arxiv.org/abs/2509.24239 | null | 2025 | Jincheng Liu and Sijun He and Jingjing Wu and Xiangsen Wang and Yang Chen and Zhaoqi Kuang and Siqi Bao and Yuan Yao | ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models | misc | liu:2025:chessarena-chess-testbed-evaluating-strategic-reasoning-capabilities-large-language-models | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2509.24239 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | cs.LG | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The FedCSIS 2025 Challenge on Predicting Chess Puzzle Difficulty tasked participants with estimating puzzle ratings directly from board states and solution sequences, without relying on human solver statistics. We propose a three-stage hybrid framework integrating gradient-boosting regressors, a multi-modal neural netw... | null | null | null | http://dx.doi.org/10.15439/2025F3675 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Ming Liu and Junye Wang and Yinghan Hu and Xiaolin Yang and Defu Lin | Hybrid Boosting and Multi-Modal Fusion for Chess Puzzle Difficulty Prediction | inproceedings | liu:2025:hybrid-boosting-multi-modal-fusion-chess-puzzle-difficulty-prediction | null | null | null | 10.15439/2025F3675 | 825--830 | null | 43 | null | null | null | Marek Bolanowski and Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik \'{S}l\k{e}zak | null | Annals of Computer Science and Information Systems | IEEE | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Abstract Gambits are central to human decision-making. Our goal is to provide a theory of Gambits. A Gambit is a combination of psychological and technical factors designed to disrupt predictable play. Chess provides an environment to study gambits and behavioral game theory. Our theory is based on the Bellman optimali... | adversarial risk analysis, AI, AlphaZero, behavioral economics, behavioral game theory, behavioral science, chess gambits, decision-making, deep learning, neural network, Q learning, rationality, skewness preference, Stafford Gambit, Stockfish 14 | null | null | https://onlinelibrary.wiley.com/doi/abs/10.1002/asmb.2684 | null | 2022 | Maharaj, Shiva and Polson, Nick and Turk, Christian | Gambits: Theory and evidence | article | maharaj:2022:gambits-theory-evidence | null | null | null | 10.1002/asmb.2684 | 572--589 | 4 | 38 | Applied Stochastic Models in Business and Industry | null | null | null | null | null | null | null | null | null | null | https://onlinelibrary.wiley.com/doi/pdf/10.1002/asmb.2684 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
In the intricate landscape of game-playing algorithms, Crazyhouse stands as a complex variant of chess where captured pieces are reintroduced, presenting unique evaluation challenges. This paper explores a hybrid approach that combines traditional evaluation functions with neural network-based evaluations, seeking an o... | Crazyhouse, chess variants, heuristic evaluation functions, neural networks, Best-Change rates, Monte Carlo tree search | null | null | https://doi.org/10.1007/978-3-031-54968-7_2 | Advances in Computer Games: 18th International Conference, ACG 2023, Virtual Event, November 28–30, 2023, Revised Selected Papers | 2023 | Makovec, Anei and Pirker, Johanna and Guid, Matej | Merging Neural Networks with Traditional Evaluations in Crazyhouse | inproceedings | makovec:2023:merging-neural-networks-traditional-evaluations-crazyhouse | null | null | null | 10.1007/978-3-031-54968-7_2 | 15--25 | null | null | null | null | null | null | null | null | Springer-Verlag | 11 | null | null | Berlin, Heidelberg | null | null | null | null | 978-3-031-54967-0 | Siegen, Germany | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
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