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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 represented by a multidimensional vectorthat encodes information about the course of the game. There are threeapproaches to creating this vector, allowing us to focus on different aspects of the chess game. The proposed analysis was carried out with theintention of verifying the Topological Data Analysis as a tool for analyzing chess games. As a result, it was shown that Topological Data Analysis can be a potential tool for recognizing the quality of a given player, if wehave enough number of his games, and to reconstruct player rankings. Asignificant result is also the potential for further research for which this thesis could be the foundation. | 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 lichess.org. Celem analizy jest rozr\'{o}\.{z}nienie graczy z r\'{o}\.{z}nym poziomem umiej\k{e}tno\'{s}ci oraz jak najlepsze odtworzenie rankingu dla najlepszych graczy. Partia ka\.{z}dego gracza jest reprezentowana przezwielowymiarowy wektor, kt\'{o}ry koduje informacje o przebiegu rozgrywki.Przedstawione s\k{a} trzy podej\'{s}cia do stworzenia tego wektora, co pozwalaskupi\'{c} si\k{e} na r\'{o}\.{z}nych aspektach partii szachowej. Proponowana analiza zosta\l{}a przeprowadzona w celu weryfikacji, czy Topologiczna Analiza Danych jest odpowiednia dla problem\'{o}w powi\k{a}zanych z szachami. W rezultacie zosta\l{}o pokazane, \.{z}e Topologiczna Analiza Danych jest potencjalnym narz\k{e}dziem do rozpoznania jako\'{s}ci gracza, o ile mamy wystarczaj\k{a}c\k{a} liczb\k{e} jego partii, oraz do rekonstruowania ranking\'{o}w. Wa\.{z}nym rezultatem jest r\'{o}wnie\.{z} potencja\l{} do dalszych bada\'{n}, dla kt\'{o}rych ta praca magisterska mo\.{z}e by\'{c} fundamentalna. | 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 the phenomenon of transcendence: when a generative model achieves capabilities that surpass the abilities of the experts generating its data. We demonstrate transcendence by training an autoregressive transformer to play chess from game transcripts, and show that the trained model can sometimes achieve better performance than all players in the dataset. We theoretically prove that transcendence can be enabled by low-temperature sampling, and rigorously assess this claim experimentally. Finally, we discuss other sources of transcendence, laying the groundwork for future investigation of this phenomenon in a broader setting. | 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 gathering, the opponent's knowledge, signaling, etc. The most popular variant, Fog of War (FoW) chess (aka. dark chess) is a recognized challenge problem in AI after superhuman performance was reached in no-limit Texas hold'em poker. We present Obscuro, the first superhuman AI for FoW chess. It introduces advances to search in imperfect-information games, enabling strong, scalable reasoning. Experiments against the prior state-of-the-art AI and human players -- including the world's best -- show that Obscuro is significantly stronger. FoW chess is the largest (by amount of imperfect information) turn-based game in which superhuman performance has been achieved and the largest game in which imperfect-information search has been successfully applied. | 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 movement. In this paper, we introduce Allie, a chess-playing AI designed to bridge the gap between artificial and human intelligence in this classic game. Allie is trained on log sequences of real chess games to model the behaviors of human chess players across the skill spectrum, including non-move behaviors such as pondering times and resignations In offline evaluations, we find that Allie exhibits humanlike behavior: it outperforms the existing state-of-the-art in human chess move prediction and ponders at critical positions. The model learns to reliably assign reward at each game state, which can be used at inference as a reward function in a novel time-adaptive Monte-Carlo tree search (MCTS) procedure, where the amount of search depends on how long humans would think in the same positions. Adaptive search enables remarkable skill calibration; in a large-scale online evaluation against players with ratings from 1000 to 2600 Elo, our adaptive search method leads to a skill gap of only 49 Elo on average, substantially outperforming search-free and standard MCTS baselines. Against grandmaster-level (2500 Elo) opponents, Allie with adaptive search exhibits the strength of a fellow grandmaster, all while learning exclusively from humans. | 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 enforces that the model outputs an action from the masked set. Building on this interface, we introduce iterative action-space pruning: if the target action is not sampled, we remove valid sampled actions from the mask and resample under the reduced candidate set, repeating until the target is sampled or a fixed budget is exhausted. We study VAM in chess and evaluate it under two training regimes: an engine-play regime that generates states via play against an engine opponent and a fixed-dataset regime that trains from a fixed dataset of positions with verifier scores. Across held-out chess puzzles and full-game play measured by average centipawn loss (ACPL), VAM improves learning efficiency and final performance over strong baselines, highlighting verbalized masking as a practical mechanism for controllable exploration in LLM RL post-training. | 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, particularly across different skill levels. To overcome this limitation, we propose a novel and computationally efficient move prediction framework that approaches chess move prediction as a behavioral analysis task. The framework employs n-gram language models to capture move patterns characteristic of specific player skill levels. By dividing players into seven distinct skill groups, from novice to expert, we trained separate models using data from the open-source chess platform Lichess. The framework dynamically selects the most suitable model for prediction tasks and generates player moves based on preceding sequences. Evaluation on real-world game data demonstrates that the model selector module within the framework can classify skill levels with an accuracy of up to 31.7\% when utilizing early game information (16 half-moves). The move prediction framework also shows substantial accuracy improvements, with our Selector Assisted Accuracy being up to 39.1\% more accurate than our benchmark accuracy. The computational efficiency of the framework further enhances its suitability for real-time chess analysis. | 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 on task difficulty estimation and aligning human and AI behavior. We explain how we acquired and processed the data, separately for training and testing datasets. We review submitted solutions and evaluate their performance by comparing them to a simple benchmark. We explain how the achieved rating differences translate to user experience when solving chess puzzles. We further explore the concept of chess puzzle difficulty by replicating competition results with puzzle ratings obtained using chess bots only. We conclude with a summary of our findings and directions for future studies, as well as an invitation to the next edition of the competition in 2025. | 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 particularly the data available to competition participants) with its previous edition associated with the IEEE BigData 2024 conference. Finally, we discuss the new functionality of the knowledgepit.ai platform, which enables competition participants to submit additional "uncertainty masks" reflecting their assessment of test cases that are mostly problematic for their machine learning models. | 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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