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This thesis uses Topological Data Analysis to examine the data collected from the lichess.org portal. The analysis was based on the games of players playing at different levels. The purpose of the analysis was to distinguish groups of players and players with the highest ranking from eachother. Each player's game is re... | Chess, Topological Data Analysis, Design Patterns, Data modeling, Modules, Category theory, Topology | null | null | https://ruj.uj.edu.pl/xmlui/handle/item/295689 | null | 2022 | Zelek, Jakub | Topological Data Analysis in chess | thesis | zelek:2022:topological-data-analysis-chess | Master's thesis | \.{Z}elawski Marcin | null | null | null | null | null | null | null | Polish keywords: Szachy, Topologiczna analiza danych, Wzorce Projektowe, Modelowanie danych, Modu\l{}y, Teoria Kategorii, Topologia. Polish abstract: W niniejszej pracy magisterskiej zosta\l{}a wykorzystana Topologiczna Analiza Danych do przeanalizowania partii szachowych, kt\'{o}re zosta\l{}y zgromadzone z portalu lic... | null | null | null | null | null | null | null | null | null | null | null | Jagiellonian 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 |
Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on data generated by humans, we may not expect the artificial model to outperform the humans on their original objectives. In this work, we study... | theory, foundations, generative modelling, sequence modelling | https://huggingface.co/datasets/ezipe/lichess-models/ | https://github.com/KempnerInstitute/chess-research | http://papers.neurips.cc/paper_files/paper/2024/hash/9e3bba153aa362f961dc43de5cababac-Abstract-Conference.html | Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024 | 2024 | Edwin Zhang and Vincent Zhu and Naomi Saphra and Anat Kleiman and Benjamin L. Edelman and Milind Tambe and Sham M. Kakade and Eran Malach | Transcendence: Generative Models Can Outperform The Experts That Train Them | inproceedings | zhang:2024:transcendence-generative-models-can-outperform-experts-that-train-them | null | null | null | null | null | null | null | null | null | null | Amir Globersons and Lester Mackey and Danielle Belgrave and Angela Fan and Ulrich Paquet and Jakub M. Tomczak and Cheng Zhang | https://transcendence.eddie.win/ | null | null | null | null | null | null | null | null | null | null | null | null | We theoretically and empirically demonstrate that generative models can outperform the experts that train them by low-temperature sampling | null | null | null | null | null | null | null | https://github.com/KempnerInstitute/chess-data | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Since the advent of AI, games have served as progress benchmarks. Meanwhile, imperfect-information variants of chess have existed for over a century, present extreme challenges, and have been the focus of significant AI research. Beyond calculation needed in regular chess, they require reasoning about information gathe... | null | null | null | https://arxiv.org/abs/2506.01242 | null | 2025 | Brian Hu Zhang and Tuomas Sandholm | General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess | misc | zhang:2025:general-search-techniques-common-knowledge-imperfect-information-games-fog-of-war-chess | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2506.01242 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | cs.GT | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://lichess.org/study/1zHFym7e, https://lichess.org/study/sja93Uc0 |
Chess has long been a testbed for AI's quest to match human intelligence, and in recent years, chess AI systems have surpassed the strongest humans at the game. However, these systems are not human-aligned; they are unable to match the skill levels of all human partners or model human-like behaviors beyond piece moveme... | chess, alignment, adaptive MCTS, inference-time scaling | null | null | https://openreview.net/forum?id=bc2H72hGxB | The Thirteenth International Conference on Learning Representations, {ICLR} 2025, Singapore, April 24-28, 2025 | 2025 | Yiming Zhang and Athul Paul Jacob and Vivian Lai and Daniel Fried and Daphne Ippolito | Human-Aligned Chess With a Bit of Search | inproceedings | zhang:2025:human-aligned-chess-bit-of-search | null | null | null | null | null | null | null | null | null | null | null | null | null | OpenReview.net | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Exploration remains a key bottleneck for reinforcement learning (RL) post-training of large language models (LLMs), where sparse feedback and large action spaces can lead to premature collapse into repetitive behaviors. We propose Verbalized Action Masking (VAM), which verbalizes an action mask in the prompt and enforc... | null | null | null | https://arxiv.org/abs/2602.16833 | null | 2026 | Zhicheng Zhang and Ziyan Wang and Yali Du and Fei Fang | VAM: Verbalized Action Masking for Controllable Exploration in RL Post-Training -- A Chess Case Study | misc | zhang:2026:vam-verbalized-action-masking-controllable-exploration-rl-post-training-chess-case-study | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2602.16833 | 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 |
Chess, a deterministic game with perfect information, has long served as a benchmark for studying strategic decision-making and artificial intelligence. Traditional chess engines or tools for analysis primarily focus on calculating optimal moves, often neglecting the variability inherent in human chess playing, particu... | null | null | null | https://arxiv.org/abs/2512.01880 | null | 2025 | Daren Zhong and Dingcheng Huang and Clayton Greenberg | Predicting Human Chess Moves: An AI Assisted Analysis of Chess Games Using Skill-group Specific n-gram Language Models | misc | zhong:2025:predicting-human-chess-moves-ai-assisted-analysis-chess-games-skill-group-specific-ngram-language-models | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2512.01880 | 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 |
We summarize the results of the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty – a data science competition organized at the knowledgepit.ai platform in association with the IEEE BigData 2024 conference. We describe the competition goal and tie it to existing research on human-computer interaction, focusing ... | Training;Human computer interaction;Reviews;Predictive models;Big Data;Vectors;Mathematical models;User experience;Problem-solving;Artificial intelligence;Human-Computer Interaction;Chess;Big Data Processing;Data Science Competitions | null | null | null | 2024 IEEE International Conference on Big Data (BigData) | 2024 | Zy\'{s}ko, Jan and \'{S}wiechowski, Maciej and Stawicki, Sebastian and Jagie\l{}a, Katarzyna and Janusz, Andrzej and \'{S}l\k{e}zak, Dominik | IEEE Big Data Cup 2024 Report: Predicting Chess Puzzle Difficulty at KnowledgePit.ai | inproceedings | zysko:2024:ieee-big-data-cup-2024-report-predicting-chess-puzzle-difficulty-knowledgepitai | null | null | null | 10.1109/BigData62323.2024.10825289 | 8423--8429 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 summarize the results of the FedCSIS 2025 machine learning competition organized on the knowledgepit.ai platform. We recall the competition's goals corresponding to estimations of the chess puzzle difficulty levels, we refer to the winning solutions, and we also compare the scope of this year's competition (and part... | null | null | null | http://dx.doi.org/10.15439/2025F5937 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Jan Zy\'{s}ko and Micha\l{} \'{S}l\k{e}zak and Dominik \'{S}l\k{e}zak and Maciej \'{S}wiechowski | FedCSIS 2025 knowledgepit.ai Competition: Predicting Chess Puzzle Difficulty Part 2 & A Step Toward Uncertainty Contests | inproceedings | zysko:2025:fedcis-2025-competition-predicting-chess-puzzle-difficulty-part-2-step-toward-uncertainty-contests | null | null | null | 10.15439/2025F5937 | 849--854 | 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 |
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