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With the development of chess engines, cheating online has never been easier, resulting in a need for more robust and accurate detection systems. This paper presents a novel approach to chess cheater detection that combines conventional chess engines and neural networks to help identify which games are authentically pl... | null | null | null | null | Information and Software Technologies | 2025 | Iavich, Maksim and Kevanishvili, Zura | A Neural Network Approach to Chess Cheat Detection | inproceedings | maksim:2025:neural-network-approach-chess-cheat-detection | null | null | null | null | 131--145 | null | null | null | null | null | Lopata, Audrius and Gudonien{\.{e}}, Daina and Butkien{\.{e}}, Rita and {\v{C}}eponis, Jonas | null | null | Springer Nature Switzerland | null | null | null | Cham | null | null | null | null | 978-3-031-84263-4 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Context: This study aims to confirm, replicate and extend the findings of a previous article entitled ''Metamorphic Testing of Chess Engines'' that reported inconsistencies in the analyses provided by Stockfish, the most widely used chess engine, for transformed chess positions that are fundamentally identical. Initial... | Reproducibility, Replicability, Metamorphic testing, Chess engines | null | null | https://www.sciencedirect.com/science/article/pii/S0950584925000187 | null | 2025 | Axel Martin and Djamel Eddine Khelladi and Th\'{e}o Matricon and Mathieu Acher | Re-evaluating metamorphic testing of chess engines: A replication study | article | martin:2025:re-evaluating-metamorphic-testing-chess-engines-replication-study | null | null | null | 10.1016/j.infsof.2025.107679 | 107679 | null | null | Information and Software Technology | null | null | null | null | null | null | null | null | null | null | null | null | 0950-5849 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
As artificial intelligence becomes increasingly intelligent---in some cases, achieving superhuman performance---there is growing potential for humans to learn from and collaborate with algorithms. However, the ways in which AI systems approach problems are often different from the ways people do, and thus may be uninte... | Human-AI collaboration, Action Prediction, Chess | null | https://github.com/CSSLab/maia-chess | https://doi.org/10.1145/3394486.3403219 | {KDD} '20: The 26th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020 | 2020 | Reid McIlroy{-}Young and Siddhartha Sen and Jon M. Kleinberg and Ashton Anderson | Aligning Superhuman {AI} with Human Behavior: Chess as a Model System | inproceedings | mcilroy-young:2020:aligning-superhuman-ai-human-behavior | null | null | https://dl.acm.org/doi/pdf/10.1145/3394486.3403219 | 10.1145/3394486.3403219 | 1677--1687 | null | null | null | null | null | Rajesh Gupta and Yan Liu and Jiliang Tang and B. Aditya Prakash | https://www.maiachess.com/ | 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 |
The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that interact with humans. Many building blocks are essential for this activity, with a central one being the algorithmic characterization of human behavior. While muc... | chess, deep-learning, embeddings, few-shot-learning, behavioral-stylometry | null | https://github.com/CSSLab/behavioral-stylometry | https://proceedings.neurips.cc/paper_files/paper/2021/file/ccf8111910291ba472b385e9c5f59099-Paper.pdf | Advances in Neural Information Processing Systems | 2021 | McIlroy-Young, Reid and Wang, Yu and Sen, Siddhartha and Kleinberg, Jon and Anderson, Ashton | Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess | inproceedings | mcilroy-young:2021:chess-stylometry | null | null | null | null | 24482--24497 | null | 34 | null | null | keywords from github repo | M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan | null | null | Curran Associates, Inc. | null | null | null | null | null | null | null | null | null | null | null | null | https://github.com/CSSLab/behavioral-stylometry/blob/main/documents/chess_embedding_slides.pdf | null | null | null | null | null | null | null | null | null | null | null | https://slideslive.com/38970556/detecting-individual-decisionmaking-style-exploring-behavioral-stylometry-in-chess?ref=speaker-92823 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AI systems that can capture human-like behavior are becoming increasingly useful in situations where humans may want to learn from these systems, collaborate with them, or engage with them as partners for an extended duration. In order to develop human-oriented AI systems, the problem of predicting human actions---as o... | Mimetic models; Human-AI interaction; Chess; Action prediction; Machine learning; Behavioral stylometry | null | https://github.com/CSSLab/maia-Individual | https://doi.org/10.1145/3534678.3539367 | {KDD} '22: The 28th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 | 2022 | Reid McIlroy{-}Young and Russell Wang and Siddhartha Sen and Jon M. Kleinberg and Ashton Anderson | Learning Models of Individual Behavior in Chess | inproceedings | mcilroy-young:2022:learning-models-individual-behavior-chess | null | null | null | 10.1145/3534678.3539367 | 1253--1263 | null | null | null | null | null | Aidong Zhang and Huzefa Rangwala | 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 | https://dl.acm.org/doi/suppl/10.1145/3534678.3539367/suppl_file/maia-individual.mp4 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Skill acquisition is central to developing expertise, yet the behavioral mechanisms that separate more successful learners from less successful ones remain poorly understood. Using a large naturalistic dataset of about one million online chess games played by ~\hspace{0.167em}820 individuals over three years (2013–2015... | null | null | null | https://www.researchsquare.com/article/rs-7789635/v1 | null | 2025 | Meireles, Lu\'{\i}s and Mendes-Neves, Tiago and Moreira, Jo\~{a}o | Practice Structure Predicts Skill Growth in Online Chess: A Behavioral Modeling Approach | misc | meireles:2025:practice-structure-predicts-skill-growth-online-chess-behavioral-modeling-approach | null | null | null | 10.21203/rs.3.rs-7789635/v1 | null | null | null | null | October | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 canonical example of a task that requires rigorous reasoning and long-term planning. Modern decision Transformers - trained similarly to LLMs - are able to learn competent gameplay, but it is unclear to what extent they truly capture the rules of chess. To investigate this, we train a 270M parameter chess Tr... | null | null | https://github.com/meszarosanna/ood_chess | https://arxiv.org/abs/2510.20783 | null | 2025 | Anna M\'{e}sz\'{a}ros and Patrik Reizinger and Ferenc Husz\'{a}r | Out-of-distribution Tests Reveal Compositionality in Chess Transformers | misc | meszaros:2025:out-of-distribution-tests-reveal-compositionality-chess-transformers | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2510.20783 | 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 |
This study addresses the challenge of quantifying chess puzzle difficulty - a complex task that combines elements of game theory and human cognition and underscores its critical role in effective chess training. We present GlickFormer, a novel transformer-based architecture that predicts chess puzzle difficulty by appr... | Training;Measurement;Accuracy;Games;Predictive models;Transformers;Feature extraction;Data models;Problem-solving;Context modeling | null | null | https://doi.ieeecomputersociety.org/10.1109/BigData62323.2024.10825919 | 2024 IEEE International Conference on Big Data (BigData) | 2024 | Milosz, Szymon and Kapusta, Pawel | { Predicting Chess Puzzle Difficulty with Transformers } | inproceedings | milosz:2024:predicting-puzzle-difficulty-transformers | null | null | null | 10.1109/BigData62323.2024.10825919 | 8377--8384 | null | null | null | December | null | null | null | null | IEEE Computer Society | null | null | null | Los Alamitos, CA, 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 |
This paper presents our third-place solution for the FedCSIS 2025 Challenge: Predicting Chess Puzzle Difficulty - Second Edition. Building on our prior GlickFormer architecture, we develop a transformer-based approach featuring a novel multitask pretraining strategy that combines masked-square reconstruction with solut... | null | null | null | http://dx.doi.org/10.15439/2025F7603 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Szymon Mi\l{}osz | Pretraining Transformers for Chess Puzzle Difficulty Prediction | inproceedings | milosz:2025:pretraining-transformers-chess-puzzle-difficulty-prediction | null | null | null | 10.15439/2025F7603 | 831--835 | 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 |
From the observational methodology approach, this study analyses definitive errors or losing blunders, i.e. errors that result in the loss of the game, in elite players at U8 level. An ad hoc observation instrument has been designed as a combination of field format and category systems, based on a thorough theoretical ... | Chess, Definitive Errors, Children, Elite, Stockfish NNUE | null | null | https://doi.org/10.2478/ijcss-2025-0012 | null | 2025 | Miranda, Jorge and Arana, Javier and Lapresa, Daniel and Anguera, M. Teresa | Observational Analysis of Mistakes in Chess Initiation, Using Decision Trees | article | miranda:2025:observational-analysis-mistakes-chess-initiation-decision-trees | null | null | null | 10.2478/ijcss-2025-0012 | 45--60 | 2 | 24 | International Journal of Computer Science in Sport | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Within the observational methodology, and based on a detailed analysis of FIDE laws of Chess, an observation system has been developed ad hoc for analyzing the illegal moves that children commit in chess. The reliability of the resulting data was confirmed by analysis of interobserver agreement, using Cohen's kappa sta... | observational methodology, chess learning, illegal moves, under-12 years of age | null | null | https://dialnet.unirioja.es/servlet/tesis?codigo=397721 | null | 2026 | Miranda P\'{e}rez, Jorge | An\'{a}lisis observacional de los movimientos ilegales y err\'{o}neos en la iniciaci\'{o}n al ajedrez | thesis | miranda:2026:observational-analysis-illegal-erroneous-moves-chess-beginners | phdthesis | Lapresa Ajamil, Daniel and Arana Idiakez, Xabier Sabino | null | null | null | null | null | null | null | Spanish abstract: En el seno de la metodolog\'{\i}a observacional, y a partir de un pormenorizado an\'{a}lisis del reglamento -Leyes FIDE-, se ha elaborado un sistema de observaci\'{o}n ad hoc que permite analizar los movimientos ilegales en el ajedrez de iniciaci\'{o}n. La fiabilidad de los datos, en forma de concorda... | null | null | null | null | null | null | null | null | null | null | null | null | null | Logro\~{n}o, Spain | null | null | null | null | null | null | null | null | null | Universidad de La Rioja | null | null | null | null | null | null | null | null | null | null | null | null | spanish | 262 | Observational analysis of illegal and erroneous moves in chess beginners | null | null | null | null | null | null | null | null | null | null |
As online platforms become ubiquitous, there is growing concern that their use can potentially lead to negative outcomes in users' personal lives, such as disrupted sleep and impacted social relationships. A central question in the literature studying these problematic effects is whether they are associated with the am... | online well-being, problematic platform use, specification curve analysis, survey methodology | null | null | https://doi.org/10.1145/3449160 | null | 2021 | Mok, Lillio and Anderson, Ashton | The Complementary Nature of Perceived and Actual Time Spent Online in Measuring Digital Well-being | article | mok:2021:time-online-digital-well-being | null | null | null | 10.1145/3449160 | null | CSCW1 | 5 | Proc. ACM Hum.-Comput. Interact. | April | null | null | null | null | Association for Computing Machinery | 27 | 86 | April 2021 | 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 |
We rely on all manners of digital systems to organize and facilitate our human functions. From social networks connecting us to each other, to content providers keeping us perpetually entertained, to search engines serving each of our informational needs, to computational models informing us how healthy we are, to arti... | Computational Social Science, Data Science, Human-AI Interaction, Human-Computer Interaction, Web Science | null | null | null | null | 2024 | Mok, Lillio | Measuring the Digital Welfare of Online Social Systems | thesis | mok:2024:measuring-digital-welfare-online-systems | Doctoral Thesis | Anderson, Ashton | null | null | null | null | null | null | null | http://hdl.handle.net/1807/140863 | null | null | null | null | null | null | null | null | null | null | null | University of Toronto | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 game of strategic thinking and time management, where a player can lose a game on time despite making all the best moves. Finding the best move is a deliberate and energy-intensive process in a game where players are often under time pressure. Therefore, players who can balance this trade-off will have a sig... | Chess, Adaptive Decision Making, Resource Constraints, Skilled Decision Maker, Evaluation | null | null | https://doi.org/10.1080/13546783.2025.2550306 | null | 2025 | Supratik Mondal and Jakub Traczyk | Adaptive decision making in the wild: a case study of chess | article | mondal:2025:adaptive-decision-making-in-the-wild-case-study-chess | null | null | null | 10.1080/13546783.2025.2550306 | 1--21 | 0 | 0 | Thinking \& Reasoning | null | null | null | null | null | Routledge | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Ranking items from pairwise comparisons is common in domains ranging from sports to consumer preferences. Statistical inference-based methods, such as the Bradley--Terry model, have emerged as flexible and powerful tools to tackle ranking in empirical data. However, in situations with limited and/or noisy comparisons, ... | null | null | https://github.com/seb310/partial-rankings | https://doi.org/10.1038/s42005-025-02461-y | null | 2025 | Morel-Balbi, Sebastian and Kirkley, Alec | Estimation of partial rankings from sparse, noisy comparisons | article | morel-balbi:2025:estimation-partial-rankings-sparse-noisy-comparisons | null | null | https://www.nature.com/articles/s42005-025-02461-y.pdf | 10.1038/s42005-025-02461-y | 30 | 1 | 9 | Communications Physics | December | null | null | null | null | null | null | null | null | null | null | null | 2399-3650 | null | null | null | null | null | null | null | null | null | 20 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://arxiv.org/abs/2501.02505 | null | null | null | null | null | null | null | null | null |
Methods of Explainable AI (XAI) try to illuminate the decision making process of complex Machine Learning models by generating explanations. However, for most real-world data there is no ``groundtruth'' explanation, which makes evaluating the correctness of XAI methods and model decisions difficult. Often visual assess... | Explainable AI, Trustworthy AI, Convolutional Neural Networks, Chess | null | null | https://ceur-ws.org/Vol-3341/KDML-LWDA_2022_CRC_8977.pdf | Proceedings of the {LWDA} 2022 Workshops: FGWM, FGKD, and FGDB, Hildesheim (Germany), Oktober 5-7th, 2022 | 2022 | Sascha M{\"{u}}cke and Lukas Pfahler | Check Mate: {A} Sanity Check for Trustworthy {AI} | inproceedings | muecke:2022:check-mate-sanity-check-trustworthy-ai | null | null | null | null | 91--103 | null | 3341 | null | null | Section 4.1 of the paper mentions code being available alongside the data on kaggle | Pascal Reuss and Viktor Eisenstadt and Jakob Michael Sch{\"{o}}nborn and Jero Sch{\"{a}}fer | 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 | https://www.kaggle.com/datasets/smuecke/chess-xai-benchmark | null | null | 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 world of competitive chess has long been a captivating arena for intellectual competition, where human intelligence, strategic thinking, and long-term planning converge. This study delves into the intricate web of factors that influence a chess player's competitive success through the lens of predictive modeling an... | null | null | null | null | Advanced Technologies, Systems, and Applications IX | 2024 | Mujagi{\'{c}}, Amar and Mujagi{\'{c}}, Adnan and Mehanovi{\'{c}}, D{\v{z}}elila | Predictive Analysis of Chess Player Performance: An Analysis of Factors Influencing Competitive Success Using Machine Learning Techniques | inproceedings | mujagic:2024:predictive-analysis-chess-player-performance-maching-learning | null | null | null | null | 392--408 | null | null | null | null | null | Ademovi{\'{c}}, Naida and Ak{\v{s}}amija, Zlatan and Karabegovi{\'{c}}, Almir | null | null | Springer Nature Switzerland | null | null | null | Cham | null | null | null | null | 978-3-031-71694-2 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Security is an integral requirement of any trustworthy software architecture, particularly critical for application programming interfaces (APIs). In this paper, we survey security documentation practices, specifically API security schemes related to authentication and authorization, by mining a large collection of Ope... | API Analytics, OpenAPI, Security | null | null | null | 22nd IEEE International Conference on Software Architecture (ICSA) | 2025 | Diana Carolina Mu{\~n}oz Hurtado and Souhaila Serbout and Cesare Pautasso | Mining Security Documentation Practices in OpenAPI Descriptions | inproceedings | munoz-hurtado:2025:mining-security-documentation-practices-openapi-descriptions | null | null | null | null | null | null | null | null | March | null | null | null | null | null | null | null | null | Odense, Denmark | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Designing AI systems that capture human-like behavior has attracted growing attention in applications where humans may want to learn from, or need to collaborate with, these AI systems. Many existing works in designing human-like AI have taken a supervised learning approach that learns from data of human behavior, with... | Human-like AI, Curriculum Learning | null | null | https://openreview.net/forum?id=fJY2iCssvIs | null | 2023 | Saumik Narayanan and Kassa Korley and Chien-Ju Ho and Siddhartha Sen | Improving the Strength of Human-Like Models in Chess | misc | narayanan:2023:improving-strength-human-models-chess | null | null | https://openreview.net/pdf?id=fJY2iCssvIs | null | null | null | null | null | null | Rejected submission to ICLR 2023, also submitted as a poster at the Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022 (https://neurips.cc/virtual/2022/64426) | null | null | null | null | null | null | null | null | null | null | null | null | null | null | We efficiently train Human-like AI models to play chess at a stronger level, while retaining their human-like style, by extending the concept of curriculum learning to support multiple teachers | null | null | null | null | null | null | null | null | null | null | 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 large language models (LLMs) have revolutionized natural language processing (NLP) by demonstrating exceptional performance in interpreting and generating human language. There has been some exploration of their application to non-linguistic tasks, which could lead to significant advancements in fields that ... | large language models, natural language processing, model adaptation techniques | null | null | https://doi.org/10.1145/3672608.3707740 | Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing | 2025 | Nguyen, Khoa and Jahan, Sadia and Slavin, Rocky | A Comparison of the Effects of Model Adaptation Techniques on Large Language Models for Non-Linguistic and Linguistic Tasks | inproceedings | nguyen:2025:comparison-effects-model-adaptation-techniques-large-language-models-non-linguistic-tasks | null | null | null | 10.1145/3672608.3707740 | 936--944 | null | null | null | null | null | null | null | SAC '25 | Association for Computing Machinery | 9 | null | null | New York, NY, USA | null | null | null | null | 9798400706295 | Catania International Airport, Catania, 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 |
The goal of this research is to analyze the structure of the network of chess players that play on Lichess.org. We aim to understand the way that Lichess randomizes player pairings and how closely related players are in order to better understand the relationship between ranking and pairing systems. We will also observ... | null | null | null | https://github.com/lichess-org/database/blob/master/web/chess-social-networks-paper.pdf | null | 2021 | Nolan, Eva and Scognamillo, Valentin | Online Chess Social Networks | misc | nolan:2021:online-chess-social-networks | null | null | null | null | null | null | null | null | null | Student project | null | null | null | null | null | null | null | null | null | null | null | Hamilton College | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
To help evaluate and understand the latent capabilities of language models, this paper introduces an approach using optimized input embeddings, or 'soft prompts,' as a metric of conditional distance between a model and a target behavior. The technique aims to facilitate latent capability discovery as a part of automate... | null | null | https://github.com/RossNordby/SoftPromptsForEvaluation | https://arxiv.org/abs/2505.14943 | null | 2025 | Ross Nordby | Soft Prompts for Evaluation: Measuring Conditional Distance of Capabilities | misc | nordby:2025:soft-prompts-evaluation-measuring-conditional-distance-capabilities | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2505.14943 | 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 ranking of players and particularly of chess players has been a topic of debate throughout the last 80 years. Such exploration spawned what has become the benchmark of evaluating professional chess players since the 1970s: the Elo rating model. The Elo system, the first to have a sound statistical basis, was design... | null | null | null | http://dx.doi.org/10.13140/RG.2.2.18931.13604 | null | 2024 | O'Rourke, Patrick | An alternative chess rating model based on latent variables | thesis | o-rourke:2024:alternative-chess-rating-model-latent-variables | mathesis | Riccardo Rastelli | https://www.researchgate.net/profile/Patrick-Orourke-7/publication/383313248_An_alternative_chess_rating_model_based_on_latent_variables/links/66c87d5975613475fe76987d/An-alternative-chess-rating-model-based-on-latent-variables.pdf | 10.13140/RG.2.2.18931.13604 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | University College Dublin | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Current chess rating systems update ratings incrementally and may not always accurately reflect a player's true strength at all times, especially for rapidly improving players or very rusty players. To overcome this, we explore a method to estimate player ratings directly from game moves and clock times. We compiled a ... | Chess, Rating Estimation, Cheating Detection | null | null | https://link.springer.com/chapter/10.1007/978-3-031-86585-5_1 | Computers and Games | 2025 | Omori, Michael and Tadepalli, Prasad | Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTM | inproceedings | omori:2024:chess-rating-estimation-moves-clock-times-cnn-lstm | null | null | https://arxiv.org/pdf/2409.11506 | null | 3--13 | null | null | null | null | null | Hartisch, Michael and Hsueh, Chu-Hsuan and Schaeffer, Jonathan | null | null | Springer Nature Switzerland | null | null | null | Cham | null | null | null | null | 978-3-031-86585-5 | 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 |
Introduction: Quantifying signals in large, sparse datasets is challenging, as noise and redundant features often obscure informative patterns. Chess middlegames, with their dynamic complexity and endless possibilities, provide a testbed for exploring such challenges. Building on 12 studies that identified three catego... | Chess Complexity, Move Prediction, Cognitive Modeling, Big Data Analysis, Sparse Data | null | https://github.com/sgjustino/Chess_Thesis | null | null | 2024 | Ong, Justin and Bilali{\'c}, Merim and Vaci, Nemanja | Sparse but Strategic: Quantitative Insights into Chess Middlegame Complexity | article | ong:2024:sparse-but-strategic-quantitative-insights-chess-middlegame-complexity | null | null | https://www.researchsquare.com/article/rs-5574128/v1.pdf?c=1748726628000 | 10.21203/rs.3.rs-5574128/v1 | null | null | null | Research Square | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Concept probing is one prominent methodology for interpreting and analyzing (deep) neural network models. It has, for example, formed the backbone of several recent works to understand better the high-level knowledge learned and employed by game-playing agents, particularly in chess. However, some recent theoretical an... | null | null | null | https://doi.org/10.3233/FAIA240574 | {ECAI} 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems {(PAIS} 2024) | 2024 | A{\dh}alsteinn P{\'{a}}lsson and Yngvi Bj{\"{o}}rnsson | Empirical Evaluation of Concept Probing for Game-Playing Agents | inproceedings | palsson:2024:empirical-evaluation-concept-probing-game-playing-agents | null | null | null | 10.3233/FAIA240574 | 874--881 | null | 392 | null | null | null | Ulle Endriss and Francisco S. Melo and Kerstin Bach and Alberto Jos{\'{e}} Bugar{\'{\i}}n Diz and Jose Maria Alonso{-}Moral and Sen{\'{e}}n Barro and Fredrik Heintz | null | Frontiers in Artificial Intelligence and Applications | {IOS} 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 |
With the widespread use of chess engines cheating in chess has become easier than ever, especially in online chess. Cheating obviously brings a negative impact to the sport. However, research on the topic on cheat detection in chess is still scarcely found. Thus, this paper will discuss data and algorithms that can be ... | Seminars;Neural networks;Games;Convolutional neural networks;Intelligent systems;Information technology;Engines;Cheat Detection;Online Chess Games;Convolutional Neural Network;Dense Neural Network;Neural Network | null | null | https://ieeexplore.ieee.org/document/9702792 | 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) | 2021 | Patria, Reyhan and Favian, Sean and Caturdewa, Anggoro and Suhartono, Derwin | Cheat Detection on Online Chess Games using Convolutional and Dense Neural Network | inproceedings | patria:2021:cheat-detection-online-chess | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9702792 | 10.1109/ISRITI54043.2021.9702792 | 389--395 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Social robots (SRs) should autonomously interact with humans, while exhibiting proper social behaviors associated to their role. By contributing to health-care, education, and companionship, SRs will enhance life quality. However, personalization and sustaining user engagement remain a challenge for SRs, due to their l... | Mathematical Dynamic Model of Mental States, Adaptive Cognition-Aware Social Robots, Model-based Control | null | https://github.com/marialuis-mp/MMM-Controller-for-Social-Robot | https://arxiv.org/abs/2504.21548 | null | 2025 | Maria Mor\~{a}o Patr\'{\i}cio and Anahita Jamshidnejad | Leveraging Systems and Control Theory for Social Robotics: A Model-Based Behavioral Control Approach to Human-Robot Interaction | misc | patricio:2025:leveraging-systems-control-theory-social-robotics-model-based-behavioral-control-human-robot-interaction | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2504.21548 | null | null | null | null | null | null | null | null | null | null | null | null | null | https://data.4tu.nl/datasets/ccadc914-9502-46d6-9ba5-fef581f2933f | null | eess.SY | 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 use logistic regression to estimate the value of the pieces in standard chess and several chess variants, namely Chess 960, Atomic chess, Antichess, and Horde chess. We perform our regressions on several years of data from Lichess, the free and open-source internet chess server. We use the published player ratings t... | null | null | null | https://arxiv.org/abs/2509.04691 | null | 2025 | Steven Pav | Inferring Piece Value in Chess and Chess Variants | misc | pav:2025:inferring-piece-value-chess-variants | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2509.04691 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | stat.AP | arXiv | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Development teams for mobile applications can receive thousands of user reviews daily. At the same time, these developers use different communication channels, such as the GitHub issue tracker. Although GitHub issues are accessible and manageable for developers, their content often differs starkly from what users write... | Semantic Textual Similarity, User Feedback Mining, GitHub Issues, Information Retrieval, Software Repository Mining | null | null | null | 2025 International Conference on Software Maintenance and Evolution (ICSME) | 2025 | Pilone, Arthur and Raglianti, Marco and Lanza, Michele and Kon, Fabio and Meirelles, Paulo | Automatically Augmenting GitHub Issues with Informative User Reviews | inproceedings | pilone:2025:automatically-augmenting-github-issues-informative-user-reviews | null | null | null | null | null | null | null | null | 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://figshare.com/articles/dataset/Replication_package_for_the_paper_Automatically_Augmenting_GitHub_Issues_with_Informative_User_Reviews_/28578140 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://gitlab.com/ArthurPilone/deepermatcher | null | null | null | null | null | null | null | null |
Classical models for supervised machine learning, such as decision trees, are efficient and interpretable predictors, but their quality is highly dependent on the particular choice of input features. Although neural networks can learn useful representations directly from raw data (e.g., images or text), this comes at t... | null | null | https://github.com/gpoesia/leapr/ | https://arxiv.org/abs/2510.14825 | null | 2025 | Gabriel Poesia and Georgia Gabriela Sampaio | Programmatic Representation Learning with Language Models | misc | poesia:2025:programmatic-representation-learning-language-models | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2510.14825 | 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 |
Interpretability of Deep Neural Networks (DNNs) is a growing field driven by the study of vision and language models. Yet, some use cases, like image captioning, or domains like Deep Reinforcement Learning (DRL), require complex modelling, with multiple inputs and outputs or use composable and separated networks. As a ... | null | null | https://github.com/Xmaster6y/tdhook | https://arxiv.org/abs/2509.25475 | null | 2025 | Yoann Poupart | TDHook: A Lightweight Framework for Interpretability | misc | poupart:2025:tdhook-lightweight-framework-interpretability | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2509.25475 | 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 |
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches ... | Deep Reinforcement Learning, Saliency maps, Chess, Go, Atari, Interpretable AI, Explainable AI | null | https://github.com/nikaashpuri/sarfa-saliency | https://openreview.net/forum?id=SJgzLkBKPB | International Conference on Learning Representations | 2020 | Nikaash Puri and Sukriti Verma and Piyush Gupta and Dhruv Kayastha and Shripad Deshmukh and Balaji Krishnamurthy and Sameer Singh | Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution | inproceedings | puri:2020:explain-your-move-understanding-agent-actions-using-specific-relevant-feature-attribution | null | null | https://openreview.net/pdf?id=SJgzLkBKPB | null | null | null | null | null | null | null | null | https://nikaashpuri.github.io/sarfa-saliency/ | null | null | null | null | null | null | null | null | null | null | null | null | We propose a model-agnostic approach to explain the behaviour of black-box deep RL agents, trained to play Atari and board games, by highlighting relevant portions of the input state. | null | null | null | null | null | null | null | https://nikaashpuri.github.io/sarfa-saliency/jekyll/update/2020/04/25/chess-saliency-dataset.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 |
Online chess serves as a naturalistic context where cognitive processes can be studied within rule-based, constrained environments. Player skills are defined via ELO rating system accomodating game factors such as wins/losses, and change in ratings over time. Furthermore, quitting a chess match possesses actual consequ... | null | null | null | https://www.cgs.iitk.ac.in/user/hariharan22/site/pdfs/Paper126_ACCS11_chess_quit_final.pdf | Proceedings of the 11th Annual Conference of Cognitive Science (ACCS 2024) | 2024 | Purohit, Hariharan and Srivastava, Nisheeth | `Sounds like a skill issue': what makes you quit at chess? | inproceedings | purohit:2024:sounds-like-skill-issue-what-makes-you-quit-chess | null | null | null | null | null | null | null | null | null |
abstract is the last paragraph from the introduction. The author describes the paper on the website: I am currently investigating the cognitive mechanisms underlying quitting behavior, using computational models and behavioral experiments. My work aims to bridge theoretical frameworks with real-world quitting scenario... | null | https://www.cgs.iitk.ac.in/user/hariharan22/site/ | null | null | null | null | null | null | null | null | null | null | null | Mumbai, India | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Stopping decisions are frequently modeled as decisions to switch to alternative activities once the current activity stops being adequately rewarding, such as in optimal foraging theory, as well as more recent metacognitive models. However, the sense of stopping and making decisions in such frameworks is highly platoni... | null | null | null | https://escholarship.org/uc/item/02n5p1j5 | null | 2025 | Purohit, Hariharan and Srivastava, Nisheeth | A metacognitive appraisal of quitting | article | purohit:2025:metacognitive-appraisal-quitting | 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 |
With the development of technology, more and more online educational products emerge in chess, which makes it difficult for different users to choose from. It's important to develop methodologies to assist different levels chess players to learn in varies environment. List method and rubric evaluation has been conducte... | Chess, Online education, Products, Comparative study | null | null | https://doi.org/10.1007/978-3-030-51968-1_9 | Blended Learning. Education in a Smart Learning Environment: 13th International Conference, ICBL 2020, Bangkok, Thailand, August 24–27, 2020, Proceedings | 2020 | Dong, Qian and Miao, Rong | A Comparative Study of Chess Online Educational Products | inproceedings | qian:2020:comparative-study-online-chess-educational-products | null | null | null | 10.1007/978-3-030-51968-1_9 | 101–113 | null | null | null | null | null | null | null | null | Springer-Verlag | 13 | null | null | Berlin, Heidelberg | null | null | null | null | 978-3-030-51967-4 | Bangkok, Thailand | null | null | null | null | null | null | null | null | null | null | null | 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 show how word embeddings, a technique used most commonly for natural language processing, can be repurposed to analyse gameplay data. Using a large study of chess games and applying the popular Word2Vec algorithm, we show that the resulting vector representation can reveal both common knowledge and sub... | null | null | null | https://ojs.aaai.org/index.php/AIIDE/article/view/18907 | Proceedings of the Seventeenth {AAAI} Conference on Artificial Intelligence and Interactive Digital Entertainment, {AIIDE} 2021, virtual, October 11-15, 2021 | 2021 | Youn{\`{e}}s Rabii and Michael Cook | Revealing Game Dynamics via Word Embeddings of Gameplay Data | inproceedings | rabii:2021:revealing-game-dynamics-word-embeddings | null | null | https://dl.acm.org/doi/pdf/10.5555/3505520.3505544 | null | 187--194 | null | null | null | null | null | David Thue and Stephen G. Ware | https://knivesandpaintbrushes.org/younes | null | {AAAI} Press | null | null | null | null | null | null | null | null | 978-1-57735-871-8 | null | This paper shows that word embedding techniques such as Word2Vec can be applied to gameplay data, helping show possible relationships between elements of a game's design. We apply Word2Vec to chess and show how it rediscovers interesting strategic knowledge about the game. | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://www.youtube.com/watch?v=Qj96jh4c6As | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
A game's theme is an important part of its design – it conveys narrative information, rhetorical messages, helps the player intuit strategies, aids in tutorialisation and more. Thematic elements of games are notoriously difficult for AI systems to understand and manipulate, however, and often rely on large amounts of h... | automated game design, computational creativity, procedural content generation | null | null | https://doi.org/10.1145/3649921.3659851 | Proceedings of the 19th International Conference on the Foundations of Digital Games | 2024 | Rabii, Youn\`{e}s and Cook, Michael | "Hunt Takes Hare": Theming Games Through Game-Word Vector Translation | inproceedings | rabii:2024:hunt-takes-hare-theming-games-through-game-word-vector-translation | null | null | null | 10.1145/3649921.3659851 | null | null | null | null | null | null | null | null | FDG '24 | Association for Computing Machinery | 7 | 74 | 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 |
Machine learning has shown great success in various aspects of chess, particularly in game-playing engines such as AlphaZero. However, predicting the difficulty of chess puzzles remains a relatively unexplored area. In the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty competition, participants are asked to ... | Deep learning;Computer vision;Transfer learning;Games;Predictive models;Big Data;Transformers;Data models;Engines;chess;deep learning;learning to rank;glicko-2 | null | null | https://ieeexplore.ieee.org/document/10825356 | 2024 IEEE International Conference on Big Data (BigData) | 2024 | Rafaralahy, Andry | Pairwise Learning to Rank for Chess Puzzle Difficulty Prediction | inproceedings | rafaralahy:2024-pairwise-ltr-learning-to-rank-chess-puzzle-difficulty-prediction | null | null | null | 10.1109/BigData62323.2024.10825356 | 8385--8389 | null | null | null | December | null | null | null | null | null | null | null | null | null | null | null | 2573-2978 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Households increasingly play and engage with video games. We examined how households play video games among 20 interviewees coming from varied and familial households. Our study focused on interactions, examining how gaming influences daily household dynamics. Previous studies have focused mainly on the impact on relat... | digital games, household, media-centric, qualitative methods, social interactions | null | null | https://doi.org/10.1145/3748619 | null | 2025 | Rautalahti, Heidi and Ma, Rongjun and Bourdoucen, Amel and Wang, Yajing and Lindqvist, Janne | Fluid Roles for Close-Knit Gaming: Households Playing Digital Games | article | rautalahti:2025:fluid-roles-close-knit-gaming-households-playing-digital-games | null | null | null | 10.1145/3748619 | null | 6 | 9 | Proc. ACM Hum.-Comput. Interact. | October | null | null | null | null | Association for Computing Machinery | 35 | GAMES024 | October 2025 | 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 |
Chess is a complex game characterized by diverse strategies and time constraints, making quick decision-making essential for success. While Elo ratings are widely recognized as indicators of player skill, the predictability of match outcomes based solely on these ratings remains a challenge. The study aims to develop a... | Training;Measurement;Analytical models;Logistic regression;Accuracy;Focusing;Psychology;Games;Predictive models;Time factors;Logistic regression;machine learning;predictive modeling | null | null | null | 2025 International Conference on Electronics, Information, and Communication (ICEIC) | 2025 | Reyes, Ma. Julianna Re-an DG. and Dicreto, Eirnan and Santos, Emmanuel Gabriel D. and Limbag, Daniella Franxene P. and Sampedro, Gabriel Avelino | EloMetrics: Advanced Outcome Prediction for Chess Matches with Elo Ratings and Logistic Regression | inproceedings | reyes:2025:elometrics-advanced-outcome-prediction-chess-elo-ratings-logistic-regression | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879733 | 10.1109/ICEIC64972.2025.10879733 | 1--4 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 ability to predict blunders in chess plays a crucial role in improving players' performance and enabling strategic decision-making. We introduce a novel, scalable, and personalized blunder prediction model for chess. Unlike prior work requiring a separate model per player, our unified architecture learns a collabor... | null | null | null | https://doi.org/10.1007/s10489-026-07131-2 | null | 2026 | Rokach, Yarden and Shapira, Bracha | Blunder prediction in chess | article | rokach:2026:blunder-prediction-chess | null | null | null | 10.1007/s10489-026-07131-2 | 92 | 4 | 56 | Applied Intelligence | February | null | null | null | null | null | null | null | null | null | null | null | 1573-7497 | null | null | null | null | null | null | null | null | null | 16 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Coordination, debate, and reflection have shown promising improvements in multi-agent Large Language Model (LLM) task performance. Inspired by the role of questioning in human group reasoning, this research introduces a novel component to multi-agent LLM systems: a Question-Asking Agent (QAA) that guides collaboration ... | Large Language Models, Chess, Expected Information Gain, Multi-Agent | null | null | https://digital.wpi.edu/concern/etds/9p290f765 | null | 2025 | Roohani, Keon | Coordination in Multi-Agent LLM Systems: The Role of a Question-Asking Agent in Guiding Collaborative Consensus | thesis | roohani:2025:coordination-multi-agent-llm-systems-role-question-asking-agent-guiding-collaborative-consensus | mathesis | Murai, Fabricio | https://digital.wpi.edu/pdfviewer/fq978037t | null | null | null | null | null | April | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Worcester, MA, USA | null | null | null | null | null | null | null | null | null | Worcester Polytechnic Institute | null | 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 research investigates temporal differences in chess gameplay between ADHD and neurotypical players, analyzing over 9,800 games across various skill levels and time controls. The study reveals distinct patterns in time management and decision-making, with significant variations observed across different game phases... | null | null | null | https://flatfish4u.github.io/research/2024/02/22/chess-research.html | null | 2024 | Benjamin Rosales | The Temporal Differences in Chess Between ADHD and Neurotypical Individuals | misc | rosales:2024:temporal-differences-chess-adhd-neurotypical-individuals | null | null | https://flatfish4u.github.io/assets/papers/chess_study.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 |
Human chess players prefer training with human opponents over chess agents as the latter are distinctively different in level and style than humans. Chess agents designed for human-agent play are capable of adjusting their level, however their style is not aligned with that of human players. In this paper, we propose a... | chess, game playing agents, human-agent play | null | null | https://doi.org/10.1145/3349537.3351904 | Proceedings of the 7th International Conference on Human-Agent Interaction, {HAI} 2019, Kyoto, Japan, October 06-10, 2019 | 2019 | Hanan Rosemarin and Ariel Rosenfeld | Playing Chess at a Human Desired Level and Style | inproceedings | rosemarin:2019:playing-chess-human-level-style | null | null | null | 10.1145/3349537.3351904 | 76--80 | null | null | null | null | null | Natsuki Oka and Tomoko Koda and Mohammad Obaid and Hideyuki Nakanishi and Omar Mubin and Kazuaki Tanaka | 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 |
This paper uses chess, a landmark planning problem in AI, to assess transformers' performance on a planning task where memorization is futile -- even at a large scale. To this end, we release ChessBench, a large-scale benchmark dataset of 10 million chess games with legal move and value annotations (15 billion data poi... | chess, supervised learning, transformer, scaling, benchmark | null | https://github.com/google-deepmind/searchless_chess | https://dl.acm.org/doi/10.5555/3737916.3740018 | Proceedings of the 38th International Conference on Neural Information Processing Systems | 2024 | Ruoss, Anian and Del\'{e}tang, Gr\'{e}goire and Medapati, Sourabh and Grau-Moya, Jordi and Wenliang, Li Kevin and Catt, Elliot and Reid, John and Lewis, Cannada A. and Veness, Joel and Genewein, Tim | Amortized planning with large-scale transformers: a case study on chess | inproceedings | ruoss:2024:amortized-planning-transformers-case-study-chess | null | null | https://proceedings.neurips.cc/paper_files/paper/2024/file/78f0db30c39c850de728c769f42fc903-Paper-Conference.pdf | null | null | null | null | null | null | Previously known as "Grandmaster-Level Chess Without Search" (https://arxiv.org/pdf/2402.04494v1) | null | https://neurips.cc/virtual/2024/poster/94747 | NeurIPS '24 | Curran Associates Inc. | 26 | 2102 | null | Red Hook, NY, USA | null | null | null | null | 9798331314385 | Vancouver, BC, Canada | null | null | null | null | null | null | null | https://storage.googleapis.com/searchless_chess | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://arxiv.org/abs/2402.04494 | null | null | null | null | null | null | null | null | null | |
Although artificial intelligence systems can now outperform humans in a variety of domains, they still lag behind in the ability to arrive at good solutions to problems using limited resources. Recent proposals have suggested that the key to this cognitive efficiency is intelligent selection of the situations in which ... | null | null | null | https://doi.org/10.31234/osf.io/8j9zx | null | 2022 | Russek, Evan and Acosta-Kane, Daniel and van Opheusden, Bas and Mattar, Marcelo and Griffiths, Tom | Time spent thinking in online chess reflects the value of computation | article | russel:2022:thinking-online-chess-computation | null | null | null | 10.31234/osf.io/8j9zx | 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 | null | null | null | null | null | null |
The rapidly evolving field of Human-Computer Interaction (HCI) faces a fundamental constraint: the limited bandwidth of information exchange between users and computing systems. One promising approach to increasing this bandwidth is implicit interaction: a paradigm in which applications modify their state based on info... | Computer science, Human-Computer Interaction, Brain-Computer Interfaces | null | null | http://hdl.handle.net/10427/B2774940P | null | 2025 | Russell, Matthew | Beyond Workload: Paving the Road for the Next Generation of Implicit Prefrontal Cortex Based Brain-Computer Interfaces | thesis | russel:2025:beyond-workload-paving-road-next-generation-implicit-prefrontal-cortex-brain-computer-interface | PhD thesis | Jacob, Robert | null | null | null | null | null | null | null | Second two keywords are from the defense page: https://www.cs.tufts.edu/t/colloquia/current/?event=1651 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Tufts University, Department of Computer 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 |
Consumer-grade electroencephalography (EEG) devices show promise for Brain-Computer Interface (BCI) applications, but their efficacy in detecting subtle cognitive states remains understudied. We developed a comprehensive study paradigm which incorporates a combination of established cognitive tasks (N-Back, Stroop, and... | null | null | https://github.com/mattrussell2/chess-mw-MUSE | https://arxiv.org/abs/2505.07592 | null | 2025 | Matthew Russell and Samuel Youkeles and William Xia and Kenny Zheng and Aman Shah and Robert J. K. Jacob | Neural Signatures Within and Between Chess Puzzle Solving and Standard Cognitive Tasks for Brain-Computer Interfaces: A Low-Cost Electroencephalography Study | misc | russell:2025:neural-signatures-chess-puzzle-solving-standard-cognitive-tasks-brain-computer-interfaces-low-cost-electroencephalography-study | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2505.07592 | null | null | null | null | null | null | null | null | null | null | null | null | null | https://github.com/mattrussell2/chess-mw-MUSE-DATA | null | cs.HC | 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 is a complex logical game involving ongoing strategic forward planning and evaluation. Solving chess puzzles is one of the most common ways of training and developing chess skills. It involves continuing the game from a certain initial chessboard state against a real or AI opponent until defeat or a significant a... | Training;Costs;Games;Computer architecture;Predictive models;Big Data;Data models;Complexity theory;Convolutional neural networks;Engines;chess puzzle difficulty;deep learning;convolutional neural networks;ensemble learning;Glicko-2 rating | null | null | https://ieeexplore.ieee.org/document/10825595 | 2024 IEEE International Conference on Big Data (BigData) | 2024 | Ruta, Dymitr and Liu, Ming and Cen, Ling | Moves Based Prediction of Chess Puzzle Difficulty with Convolutional Neural Networks | inproceedings | ruta:2024:moves-based-prediction-chess-puzzle-difficulty-convolutional-neural-networks | null | null | null | 10.1109/BigData62323.2024.10825595 | 8390--8395 | null | null | null | December | null | null | null | null | null | null | null | null | null | null | null | 2573-2978 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 propose a novel supervised learning approach for training Artificial Neural Networks (ANNs) to evaluate chess positions. The method that we present aims to train different ANN architectures to understand chess positions similarly to how highly rated human players do. We investigate the capabilities tha... | Deep Learning, COMPUTER GAMES, Machine Learning | null | null | http://www.icpram.org/ | 7th International Conference on Pattern Recognition Applications and Methods | 2018 | Matthia Sabatelli and Francesco Bidoia and Valeriu Codreanu and Marco Wiering | Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead | inproceedings | sabatelli:2018:learning-evaluate-chess-positions-deep-neural-networks-limited-lookahead | null | null | null | 10.5220/0006535502760283 | 276--283 | null | null | null | January | 7th International Conference on Pattern Recognition Applications and Methods ; Conference date: 16-01-2018 Through 18-01-2018 | null | null | null | SciTePress | null | null | null | null | null | null | null | null | 978-989758276-9 | null | null | null | null | null | null | null | 20 | English | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Agentic AI systems execute a sequence of actions, such as reasoning steps or tool calls, in response to a user prompt. To evaluate the success of their trajectories, researchers have developed verifiers, such as LLM judges and process-reward models, to score the quality of each action in an agent's trajectory. Although... | null | null | https://github.com/shuvom-s/e-valuator | https://arxiv.org/abs/2512.03109 | null | 2025 | Shuvom Sadhuka and Drew Prinster and Clara Fannjiang and Gabriele Scalia and Aviv Regev and Hanchen Wang | E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing | misc | sadhuka:2025:evaluator-reliable-agent-verifiers-sequential-hypothesis-testing | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2512.03109 | 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 | https://pypi.org/project/e-valuator/ | null | null | null | null | null | null | null |
Starting with early successes in computer vision tasks, deep learning based techniques have since overtaken state of the art approaches in a multitude of domains. However, it has been demonstrated time and again that these techniques fail to capture semantic context and logical constraints, instead often relying on spu... | logical constraints, domain knowledge, deep learning, computer vision | null | https://github.com/espressoVi/VALUE-Dataset | https://openreview.net/forum?id=nS9oxKyy9u | null | 2024 | Soumadeep Saha and Saptarshi Saha and Utpal Garain | {VALUED} - Vision and Logical Understanding Evaluation Dataset | article | saha:2024:valued-vision-logical-understanding-dataset | null | null | null | null | null | null | null | Journal of Data-centric Machine Learning Research | 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/10607059 | null | null | null | null | null | null | https://www.youtube.com/watch?v=6V9VlTEfHT4 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Deep learning, a family of data-driven artificial intelligence techniques, has shown immense promise in a plethora of applications, and it has even outpaced experts in several domains. However, unlike symbolic approaches to learning, these methods fall short when it comes to abiding by and learning from pre-existing es... | null | null | null | https://digitalcommons.isical.ac.in/doctoral-theses/629/ | null | 2025 | Saha, Soumadeep | Domain Obedient Deep Learning | thesis | saha:2025:domain-obedient-deep-learning | PhD thesis | Garain, Utpal | null | null | null | null | null | null | null | Check if http://hdl.handle.net/10263/7608 works and replace url | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Computer Vision and Pattern Recognition Unit, Indian Statistical Institute | null | 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 develop a satisficing model of choice in which the available alternatives differ in their inherent complexity. We assume--and experimentally validate--that complexity leads to errors in the perception of alternatives' values. The model yields sharp predictions about the effect of complexity on choice probabilities, ... | null | null | null | http://www.nber.org/papers/w30002 | null | 2022 | Salant, Yuval and Spenkuch, Jorg L | Complexity and Satisficing: Theory with Evidence from Chess | techreport | salant:2022:complexity-satisficing-theory-evidence-chess | Working Paper | null | null | 10.3386/w30002 | null | 30002 | null | null | April | null | null | null | Working Paper Series | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | National Bureau of Economic Research | null | 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 explore the role of memory for choice behavior in unfamiliar environments. Using a unique data set, we document that decision makers exhibit a "memory premium." They tend to choose in-memory alternatives over out-of-memory ones, even when the latter are objectively better. Consistent with well-established regularit... | null | null | null | http://www.nber.org/papers/w33649 | null | 2025 | Salant, Yuval and Spenkuch, Jorg L and Almog, David | The Memory Premium | techreport | salant:2025:memory-premium | Working Paper | null | null | 10.3386/w33649 | null | 33649 | null | null | April | null | null | null | Working Paper Series | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | National Bureau of Economic Research | null | 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 chess platforms generate vast amounts of game data, presenting opportunities to analyze match outcomes using machine learning approaches. This study develops and compares four machine learning models to classify chess game results (White win, Black win, or Draw) by integrating player rating information with game... | chess prediction; machine learning; classification algorithms; online gaming; player rating systems; gradient boosting; game outcome forecasting | null | null | https://www.mdpi.com/2079-9292/15/1/1 | null | 2026 | Samara, Kamil and Antreassian, Aaron and Klug, Matthew and Hasan, Mohammad Sakib | Machine Learning Approaches for Classifying Chess Game Outcomes: A Comparative Analysis of Player Ratings and Game Dynamics | article | samara:2026:machine-learning-approaches-classifying-chess-game-outcomes-comparative-analysis-player-ratings-game-dynamics | null | null | null | 10.3390/electronics15010001 | null | 1 | 15 | Electronics | null | null | null | null | null | null | null | null | null | null | null | null | 2079-9292 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Do neural networks build their representations through smooth, gradual refinement, or via more complex computational processes? We investigate this by extending the logit lens to analyze the policy network of Leela Chess Zero, a superhuman chess engine. We find strong monotonic trends in playing strength and puzzle-sol... | Understanding high-level properties of models, Probing, logit lens, chess, iterative inference | null | https://github.com/hartigel/leela-logit-lens | https://openreview.net/forum?id=nRPQhySXJP | Mechanistic Interpretability Workshop at NeurIPS 2025 | 2025 | Elias Sandmann and Sebastian Lapuschkin and Wojciech Samek | Iterative Inference in a Chess-Playing Neural Network | inproceedings | sandmann:2025:iterative-inference-chess-playing-neural-network | 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 extended the logit lens to Post-LN to analyze Leela Chess, revealing interpretable intermediate policies with monotonic capability improvement but non-monotonic policy dynamics that contrast with smooth language model convergence | null | null | null | 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://figshare.com/s/5342980a9ba8b26985a9 | null | null | null | null | null | null |
In this paper, we quantify the non-transitivity in chess using human game data. Specifically, we perform non-transitivity quantification in two ways--Nash clustering and counting the number of rock-paper-scissor cycles--on over one billion matches from the Lichess and FICS databases. Our findings indicate that the stra... | game theory; multi-agent AI; non-transitivity quantification | null | null | https://doi.org/10.3390/a15050152 | null | 2022 | Ricky Sanjaya and Jun Wang and Yaodong Yang | Measuring the Non-Transitivity in Chess | article | sanjaya:2022-non-transitivity-chess | null | null | null | 10.3390/A15050152 | 152 | 5 | 15 | Algorithms | null | code access expired: https://anonymous.4open.science/r/MSc-Thesis-8543 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 research investigates the potential for large language models to learn to generate valid chess moves solely through pre-training on chess game data. The primary objective of this study is to investigate the impact of custom notation systems and tokenisation methods specifically designed for use with chess games. T... | AI, Chess, GPT-2, LLM, Mamba, NLP, KI, Schach | null | null | https://digitalcollection.zhaw.ch/items/2ca7f5f3-535c-406a-87af-432ea6ba940b | null | 2024 | Schmid, Lars and Maag, Jerome | Optimizing language models for chess : the impact of custom notation and Elo-based fine-tuning | thesis | schmid-maag:2024:optimizing-language-models-chess-impact-custom-notation-elo-based-finetuning | Bachelor's thesis | Cieliebak, Mark and von D\"{a}niken, Pius | null | 10.21256/zhaw-31999 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Z{\"u}rcher Hochschule f{\"u}r Angewandte Wissenschaften | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Decision-making researchers often face a trade-off when conducting controlled laboratory experiments, as these can limit the ability to identify stable relationships between decision-making quality and individual differences, such as expertise or personality traits. This study introduces an innovative paradigm that lev... | Artificial intelligence, Decision quality, Expertise, Naturalistic decision making, Individual differences | null | null | https://www.sciencedirect.com/science/article/pii/S0191886925001369 | null | 2025 | Robin Schr\"{o}dter and Katrin Heyers and Jan Birkemeyer and Stefanie Klatt | The role of expertise, impulsivity, and preference for intuition on decision quality | article | schroedter:2025:role-expertise-impulsivity-preference-intuition-decision-quality | null | null | null | 10.1016/j.paid.2025.113174 | 113174 | null | 240 | Personality and Individual Differences | null | null | null | null | null | null | null | null | null | null | null | null | 0191-8869 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
For chess players to sharpen their tactical skills effectively, they train on chess puzzles with a fitting difficulty level. This paper presents an approach to estimate the difficulty level of chess puzzles using a deep neural network. The proposed approach achieved second place in the IEEE BigData Cup 2024 competition... | Training;Uncertainty;Fitting;Estimation;Games;Artificial neural networks;Predictive models;Network architecture;Big Data;Problem-solving;chess puzzle;difficulty estimation;neural network | null | null | https://ieeexplore.ieee.org/document/10826087 | 2024 IEEE International Conference on Big Data (BigData) | 2024 | Sch\"{u}tt, Anan and Huber, Tobias and Andr\'{e}, Elisabeth | Estimating Chess Puzzle Difficulty Without Past Game Records Using a Human Problem-Solving Inspired Neural Network Architecture | inproceedings | schuett:2024:estimating-chess-puzzle-difficulty-without-past-records-using-neural-network | null | null | null | 10.1109/BigData62323.2024.10826087 | 8396--8402 | null | null | null | December | null | null | null | null | null | null | null | null | null | null | null | 2573-2978 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Advancing planning and reasoning capabilities of Large Language Models (LLMs) is one of the key prerequisites towards unlocking their potential for performing reliably in complex and impactful domains. In this paper, we aim to demonstrate this across board games (Chess, Fischer Random / Chess960, Connect Four, and Hex)... | search, planning, language models, games, chess | null | null | https://openreview.net/forum?id=KKwBo3u3IW | Forty-second International Conference on Machine Learning | 2025 | John Schultz and Jakub Adamek and Matej Jusup and Marc Lanctot and Michael Kaisers and Sarah Perrin and Daniel Hennes and Jeremy Shar and Cannada A. Lewis and Anian Ruoss and Tom Zahavy and Petar Veli{\v{c}}kovi{\'c} and Laurel Prince and Satinder Singh and Eric Malmi and Nenad Tomasev | Mastering Board Games by External and Internal Planning with Language Models | inproceedings | schultz:2025:mastering-board-games-external-internal-planning-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 | We pre-trained an LLM capable of playing board games at a high level. We further introduce external and internal planning methods that achieve Grandmaster-level performance in chess while operating closer to the human search budget. | null | null | null | null | null | null | null | null | null | null | null | null | null | null | https://www.youtube.com/watch?v=JyxE_GE8noc | null | null | null | null | null | null | null | null | null | null | null | null | null | 1.Large Language Models (LLMs) demonstrate impressive performance across various tasks that require complex reasoning. Yet, they still struggle to play board games as simple as tic-tac-toe.\n2.We developed an LLM that can play different board games, reaching Grandmaster-level chess performance. We investigated differen... | null | null | null | null | null |
As AI systems become more capable, they may internally represent concepts outside the sphere of human knowledge. This work gives an end-to-end example of unearthing machine-unique knowledge in the domain of chess. We obtain machine-unique knowledge from an AI system (AlphaZero) by a method that finds novel yet teachabl... | null | null | null | https://www.pnas.org/doi/abs/10.1073/pnas.2406675122 | null | 2025 | Lisa Schut and Nenad Toma\v{s}ev and Thomas McGrath and Demis Hassabis and Ulrich Paquet and Been Kim | Bridging the human–AI knowledge gap through concept discovery and transfer in AlphaZero | article | schut:2025:briding-human-ai-knowledge-gap-concept-discovery-transfer-alphazero | null | null | null | 10.1073/pnas.2406675122 | e2406675122 | 13 | 122 | Proceedings of the National Academy of Sciences | null | null | null | null | null | null | null | null | null | null | https://www.pnas.org/doi/pdf/10.1073/pnas.2406675122 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | Lichess openings cited in appendix | null | null | null | null |
What factors of our learning experiences enable us to best acquire complex skills? Recent ideas from artificial intelligence point to two such factors: (1) a balance of real experience with simulated experience acquired during planning itself, and (2) appropriate diversity in training examples. To test whether these fa... | null | null | null | https://escholarship.org/uc/item/5c76v07h | null | 2025 | Schut, Lisa and Russek, Evan and Kuperwajs, Ionatan and Mattar, Marcelo G and Ma, Wei Ji and Griffiths, Tom | Learning in online chess increases with more time spent thinking and diversity of experience | inproceedings | schut:2025:learning-online-chess-increases-time-thinking-diversity-experience | 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 |
Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans may still be difficult for neural models. Humans possess the ability to extrapolate reasoning strategies learned on simple problems to solve harder examples, often by thinking for longer. For example,... | Deep learning, algorithms, generalization, recurrent networks, prefix sums, mazes, chess | null | https://github.com/aks2203/easy-to-hard | https://openreview.net/forum?id=Tsp2PL7-GQ | Proceedings of the 35th International Conference on Neural Information Processing Systems | 2021 | Schwarzschild, Avi and Borgnia, Eitan and Gupta, Arjun and Huang, Furong and Vishkin, Uzi and Goldblum, Micah and Goldstein, Tom | Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks | inproceedings | schwarzschild:2021:can-you-learn-algorithm-generalizing-easy-hard-examples- | null | null | https://openreview.net/pdf?id=Tsp2PL7-GQ | null | null | null | null | null | null | null | null | https://proceedings.neurips.cc/paper/2021/hash/3501672ebc68a5524629080e3ef60aef-Abstract.html | NeurIPS '21 | Curran Associates Inc. | 12 | 513 | null | Red Hook, NY, USA | null | null | null | null | 9781713845393 | null | Recurrent netowrks can learn processes that can generalize from easy training data to harder examples at test time by iterating more times. | null | null | null | null | null | null | 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://openreview.net/attachment?id=Tsp2PL7-GQ&name=supplementary_material | null | null | null |
We describe new datasets for studying generalization from easy to hard examples. | null | null | null | https://arxiv.org/abs/2108.06011 | null | 2021 | Avi Schwarzschild and Eitan Borgnia and Arjun Gupta and Arpit Bansal and Zeyad Emam and Furong Huang and Micah Goldblum and Tom Goldstein | Datasets for Studying Generalization from Easy to Hard Examples | article | schwarzschild:2021:datasets-easy-hard-examples | null | null | null | null | null | null | abs/2108.06011 | CoRR | null | null | null | null | null | null | null | null | null | null | 2108.06011 | 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 | https://pypi.org/project/easy-to-hard-data/ | null | null |
At the moment, it's clear that AI has surpassed human ability in almost every field. But, how useful really is this to us? Several fields (eg. law, education, games) have noticed that having a ``perfect'' AI isn't as good as it seems in all cases. One of the marquee examples is law, where an AI wouldn't consider highly... | Artificial Intelligence, Human-AI Interaction, Chess, Transformer, Move Prediction, Multitask Learning, AI Alignment, Human Cognition | null | null | https://ieeexplore.ieee.org/document/11050701 | 2025 IEEE Conference on Artificial Intelligence (CAI) | 2025 | Hari Sekar, Easwar Gnana and Jin, Roger | Human-Aligned Chess AI: A Multitask Transformer for Humanlike Decision-Making | inproceedings | sekar:2025:human-aligned-chess-ai-multitask-transformer-humanlike-decision-making | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11050701 | 10.1109/CAI64502.2025.00213 | 1230--1234 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Psychology and social science research offer some promising work in the field of decision-making science. However, given the qualitative nature of much of this research, understanding some physiological bases of decision-making may assist by providing more objectivity. The purpose of this study, therefore, was to explo... | Testosterone, Cortisol, Stress, Decision-making | null | null | https://doi.org/10.1007/s40750-025-00264-7 | null | 2025 | Serpell, Benjamin G. and Crewther, Blair T. and Fourie, Phillip J. and Goodman, Stephen P. J. and Cook, Christian J. | Stress and Strategic Decision Making | article | serpell:2025:stress-strategic-decision-making | null | null | null | 10.1007/s40750-025-00264-7 | 12 | 3 | 11 | Adaptive Human Behavior and Physiology | June | null | null | null | null | null | null | null | null | null | null | null | 2198-7335 | null | null | null | null | null | null | null | null | null | 27 | null | null | null | null | 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 aims to determine the results of the analysis of chess playing skills on mathematics learning outcomes for junior athletes of the Raja Kombi Trenggalek chess club. The research method used is a qualitative descriptive method with a quantitative approach. Participants in this study were 8 junior athletes of ... | Analysis, Chess Skills, Mathematics Learning Outcomes | null | null | https://doi.org/10.20961/phduns.v18i1.51318 | null | 2018 | Setiawan, Andika Yogi and Pratama, Henri Gunawan | Analysis of Chess Playing Skills on Mathematics Learning Outcomes Junior Athletes Raja Kombi Trenggalek Chess Club | article | setiawan:2018:analysis-chess-skills-mathematics-learning | null | null | null | 10.20961/phduns.v18i1.51318 | 37--46 | 1 | 18 | PHEDHERAL | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Summarizing event sequences is a key aspect of data mining. Most existing methods neglect conditional dependencies and focus on discovering sequential patterns only. In this paper, we study the problem of discovering both conditional and unconditional dependencies from event sequences. We do so by discovering rules of ... | sequential patterns, rule mining, minimum description length | null | null | https://arxiv.org/abs/2505.06049 | Proceedings of the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26) | null | Aleena Siji and Joscha C\"{u}ppers and Osman Ali Mian and Jilles Vreeken | Seqret: Mining Rule Sets from Event Sequences | inproceedings | siji:2026:seqret-mining-rule-sets-event-sequences | null | null | null | null | null | null | null | null | null | preprint: https://arxiv.org/abs/2505.06049 | null | https://eda.rg.cispa.io/prj/seqret/ | null | AAAI Press | null | null | null | null | null | null | null | null | null | Singapore | null | null | null | null | null | null | null | null | null | 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://eda.rg.cispa.io/prj/seqret/seqret-v20250526.zip | null |
Predicting player behavior in strategic games, especially complex ones like chess, presents a significant challenge. The difficulty arises from several factors. First, the sheer number of potential outcomes stemming from even a single position, starting from the initial setup, makes forecasting a player's next move inc... | Knowledge Representation, Machine Learning, Behavioral Programming, Predicting Human Actions, Human Decision-Making in Chess, Feature Engineering, Chess | null | null | https://arxiv.org/abs/2504.05425 | null | 2025 | Benny Skidanov and Daniel Erbesfeld and Gera Weiss and Achiya Elyasaf | A Behavior-Based Knowledge Representation Improves Prediction of Players' Moves in Chess by 25% | misc | skidanov:2025:behavior-based-knowledge-representation-improves-prediction-player-moves | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2504.05425 | 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 |
Despite the impressive generative capabilities of large language models (LLMs), their lack of grounded reasoning and susceptibility to hallucinations limit their reliability in structured domains such as chess. We present Ca{\"i}ssa AI, a neuro-symbolic chess agent that augments LLM-generated move commentary with symbo... | Neuro-Symbolic AI, Chess Agents, Explainable Reasoning | null | null | null | KI 2025: Advances in Artificial Intelligence | 2026 | Soliman, Mazen and Ehab, Nourhan | Ca{\"i}ssa AI: A Neuro-Symbolic Chess Agent for~Explainable Move Suggestion and~Grounded Commentary | inproceedings | soliman:2026:caissa-ai-neuro-symbolic-chess-agent-explainable-move-suggestion-grounded-commentary | null | null | null | null | 148--160 | null | null | null | null | null | Braun, Tanya and Paa{\ss}en, Benjamin and Stolzenburg, Frieder | null | null | Springer Nature Switzerland | null | null | null | Cham | null | null | null | null | 978-3-032-02813-6 | 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 |
Chess games demonstrate players' ability to envision the situation on a large scale, cope with variations, and take precautions. It's been proven statistically and mathematically that the white sides are more likely to win due to offensive advantage. Nonetheless, utilizing numerous defensive gambits, the black enhances... | Chess games; Sicilian defense; Chi-square Test | null | null | https://doi.org/10.61173/v2xdqn32 | null | 2023 | Song, Ziming | Investigation of the Sicilian Defense: Winning rates and strategic discrimination | article | song:2023:investigation-sicilian-defense | null | null | https://www.deanfrancispress.com/index.php/hc/article/view/323/HC000572.pdf | null | null | 4 | 1 | Interdisciplinary Humanities and Communication Studies | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 evaluation of Large Language Models (LLMs) in complex reasoning domains typically relies on performance alignment with ground-truth oracles. In the domain of chess, this standard manifests as accuracy benchmarks against strong engines like Stockfish. However, high scalar accuracy does not necessarily imply robust c... | Large Language Models, Geometric Stability, Chess Evaluation, Robustness Analysis, AI Reasoning, Evaluation Metrics | null | null | https://arxiv.org/abs/2512.15033 | null | 2025 | Xidan Song and Weiqi Wang and Ruifeng Cao and Qingya Hu | Beyond Accuracy: A Geometric Stability Analysis of Large Language Models in Chess Evaluation | misc | song:2025:beyond-accuracy-geometric-stability-analysis-large-language-models-chess-evaluation | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2512.15033 | 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 |
Contemporary chess engines offer precise yet opaque evaluations, typically expressed as centipawn scores. While effective for decision-making, these outputs obscure the underlying contributions of individual pieces or patterns. In this paper, we explore adapting SHAP (SHapley Additive exPlanations) to the domain of che... | chess, explainable AI, shap | null | https://github.com/fspinna/chessplainer | https://ai4hgi.github.io/paper13.pdf | Proceedings of AI4HGI 2025, the First Workshop on Artificial Intelligence for Human-Game Interaction at the 28th European Conference on Artificial Intelligence (ECAI 2025) | 2025 | Spinnato, Francesco | Towards Piece-by-Piece Explanations for Chess Positions with {SHAP} | inproceedings | spinnato:2025:towards-piece-by-piece-explanations-chess-positions-shap | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 shows the weaknesses of two symmetric encryption schemes – Chessography and Cascaded Spin Shuffle. The security claims made by their authors are unsubstantiated. Despite being featured in peer-reviewed publications, their flaws are readily apparent and do not require any sophisticated cryptanalysis. Conseque... | encryption, symmetric ciphers, cryptanalysis, chess | null | null | https://ceur-ws.org/Vol-4092/paper32.pdf | Proceedings of the Workshop on Applied Security (WAS 2025) at the 25th Conference Information Technologies – Applications and Theory (ITAT 2025) | 2025 | Stanek, Martin | Bad cipher design: Chessography and Cascaded Spin Shuffle | inproceedings | stanek:2025:bad-cipher-design-chessography-cascaded-spin-shuffle | null | null | null | null | 395--403 | null | 4092 | null | null | Older version with only chessography scheme covered: https://arxiv.org/abs/2412.09742 | 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 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
The Quantum Approximate Optimization Algorithm (QAOA) is extensively benchmarked on synthetic random instances such as MaxCut, TSP, and SAT problems, but these lack semantic structure and human interpretability, offering limited insight into performance on real-world problems with meaningful constraints. We introduce Q... | null | null | null | https://arxiv.org/abs/2601.00318 | null | 2026 | Gerhard Stenzel and Michael K\"{o}lle and Tobias Rohe and Julian Hager and Leo S\"{u}nkel and Maximilian Zorn and Claudia Linnhoff-Popien | Quantum King-Ring Domination in Chess: A QAOA Approach | misc | stenzel:2026:quantum-king-ring-domination-chess-qaoa-approach | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2601.00318 | 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 |
We analyse how a transformer-based language model learns the rules of chess from text data of recorded games. We show how it is possible to investigate how the model capacity and the available number of training data influence the learning success of a language model with the help of chess-specific metrics. With these ... | null | null | null | https://aclanthology.org/2021.ranlp-1.153 | Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021) | 2021 | St{\"o}ckl, Andreas | Watching a Language Model Learning Chess | inproceedings | stockl:2021:watching-language-model-learning-chess | null | null | null | null | 1369--1379 | null | null | null | September | null | Mitkov, Ruslan and Angelova, Galia | null | null | INCOMA Ltd. | null | null | null | Held 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 |
There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into human decision-making.... | Human-AI Alignment, Action Prediction, Chess, Skill-aware Attention | null | https://github.com/CSSLab/maia2 | null | Proceedings of the 38th International Conference on Neural Information Processing Systems | 2025 | Tang, Zhenwei and Jiao, Difan and McIlroy-Young, Reid and Kleinberg, Jon and Sen, Siddhartha and Anderson, Ashton | Maia-2: a unified model for human-AI alignment in chess | inproceedings | tang:2024:maia-2-unified-model-human-ai-alignment-chess | null | null | null | null | null | null | null | null | null | null | null | null | NeurIPS '24 | Curran Associates Inc. | 26 | 659 | null | Red Hook, NY, USA | null | null | null | null | 9798331314385 | Vancouver, BC, Canada | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Elo rating, widely used for skill assessment across diverse domains ranging from competitive games to large language models, is often understood as an incremental update algorithm for estimating a stationary Bradley-Terry (BT) model. However, our empirical analysis of practical matching datasets reveals two surprising ... | Pairwise comparison, ranking | null | null | https://arxiv.org/abs/2502.10985 | null | 2025 | Shange Tang and Yuanhao Wang and Chi Jin | Is Elo Rating Reliable? A Study Under Model Misspecification | misc | tang:2025:is-elo-rating-reliable-study-under-model-misspecification | null | null | null | null | null | null | null | null | null | submitted to ICLR 2026: https://openreview.net/forum?id=uUq0gemhnv | null | null | null | null | null | null | null | null | 2502.10985 | 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 |
Evaluating whether vision-language models (VLMs) reason consistently across representations is challenging because modality comparisons are typically confounded by task differences and asymmetric information. We introduce SEAM, a benchmark that pairs semantically equivalent inputs across four domains that have existing... | null | https://huggingface.co/datasets/lilvjosephtang/SEAM-Benchmark | https://github.com/CSSLab/SEAM | https://openreview.net/forum?id=lI4LgGv4sX | Second Conference on Language Modeling | 2025 | Zhenwei Tang and Difan Jiao and Blair Yang and Ashton Anderson | {SEAM}: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models | inproceedings | tang:2025:seam-semantically-equivalent-modalities-benchmark-vlm-vision-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 | https://lilv98.github.io/SEAM-Website/ | null | null | null | null |
The ability to make good decisions is critical in life. Although anecdotal and preliminary evidence suggests that social comparison could impair decision making, surprisingly little attention has been paid to such dynamics within cognitive science. The present study aimed to address this gap by exploring, via a sample ... | decision making; error rate; cognitive psychology, social psychology, regression discontinuity design; chess | null | null | https://escholarship.org/uc/item/85d620jz | Proceedings of the Annual Meeting of the Cognitive Science Society | 2023 | Tay, Li Qian | Can higher social status of competitors cause decision makers to commit more errors? | inproceedings | tay:2023:social-status-competitors-cause-decision-maker-errors | null | null | null | null | null | null | 45 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Traditionally, the relative strength of a chess player within a competitive pool is identified by a rating number. In order to reach a fair rating that best represents their level of play, chess players are required to play numerous games against various opponents within that pool. However, intuitively, experienced che... | null | null | null | https://doi.org/10.1109/CoG57401.2023.10333133 | {IEEE} Conference on Games, CoG 2023, Boston, MA, USA, August 21-24, 2023 | 2023 | Tim Tijhuis and Paris Mavromoustakos Blom and Pieter Spronck | Predicting Chess Player Rating Based on a Single Game | inproceedings | tijhuis:2023:predicting-chess-rating-single-game | null | null | null | 10.1109/COG57401.2023.10333133 | 1--8 | null | null | null | null | null | null | null | null | {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 |
This book presents a compelling account of the historic FIDE world chess championship match between Ding Liren of China and Gukesh Dommaraju of India, held in Singapore from November 25 to December 13, 2024. Sponsored by Google, the 14-game match marked several significant milestones: the first All-Asian world chess ch... | null | null | null | https://www.worldscientific.com/worldscibooks/10.1142/14303 | null | 2025 | Urcan, Olimpiu G | East Meets East: Inside The 2024 World Chess Championship In Singapore | book | urcan:2025:east-meets-east-inside-2024-world-chess-championship-singapore | null | null | null | 10.1142/14303 | null | null | null | null | null | null | null | null | null | World Scientific Publishing Company | null | null | null | null | null | null | null | null | 9789819812820 | null | null | null | null | null | null | null | null | null | null | null | null | 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 advancement of Generative AI has raised significant questions regarding its ability to produce creative and novel outputs. Our recent work investigates this question within the domain of chess puzzles and presents an AI system designed to generate puzzles characterized by aesthetic appeal, novelty, counter-in... | null | null | null | https://arxiv.org/abs/2510.23772 | null | 2025 | Vivek Veeriah and Federico Barbero and Marcus Chiam and Xidong Feng and Michael Dennis and Ryan Pachauri and Thomas Tumiel and Johan Obando-Ceron and Jiaxin Shi and Shaobo Hou and Satinder Singh and Nenad Toma\v{s}ev and Tom Zahavy | Evaluating In Silico Creativity: An Expert Review of AI Chess Compositions | misc | veeriah:2025:evaluating-silico-creativity-expert-review-ai-chess-competitions | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2510.23772 | 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 |
Convolutional neural networks are typically applied to image analysis problems. We investigate whether a simple convolutional neural network can be trained to evaluate chess positions by means of predicting Stockfish (an existing chess engine) evaluations. Publicly available data from lichess.org was used, and we obtai... | null | null | null | null | null | 2019 | Vikstr{\"o}m, Joel | Training a Convolutional Neural Network to Evaluate Chess Positions | thesis | vikstrom:2019:convolutional-neural-network-cnn-evaluate-chess-positions | Bachelor's thesis | Markidis, Stefano | null | null | 18 | 2019:377 | null | null | null | null | null | null | TRITA-EECS-EX | null | null | null | null | null | null | null | null | KTH, School of Electrical Engineering and Computer Science (EECS) | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Reasoning is a central capability of human intelligence. In recent years, with the advent of large-scale datasets, pretrained large language models have emerged with new capabilities, including reasoning. However, these models still struggle with long-term, complex reasoning tasks, such as playing chess. Based on the o... | null | https://huggingface.co/OutFlankShu/MATE | null | https://aclanthology.org/2025.naacl-short.52/ | Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) | 2025 | Wang, Shu and Ji, Lei and Wang, Renxi and Zhao, Wenxiao and Liu, Haokun and Hou, Yifan and Wu, Ying Nian | Explore the Reasoning Capability of {LLM}s in the Chess Testbed | inproceedings | wang:2025:explore-reasoning-capability-llms-chess-testbed | null | null | null | 10.18653/v1/2025.naacl-short.52 | 611--622 | null | null | null | April | null | Chiruzzo, Luis and Ritter, Alan and Wang, Lu | https://mate-chess.github.io/ | null | Association for Computational Linguistics | null | null | null | Albuquerque, New Mexico | null | null | null | null | 979-8-89176-190-2 | null | null | null | null | null | null | null | null | null | https://huggingface.co/datasets/OutFlankShu/MATE_NAACL2025_Explore-the-Reasoning-Capability-of-LLMs-in-the-Chess-Testbed | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Lossless data compression has evolved into an indispensable tool for reducing data transfer times in heterogeneous systems. However, performing decompression on host systems can create performance bottlenecks. Accelerator libraries, such as nvCOMP, address this problem by providing custom GPU-enabled versions of some g... | Burrows-Wheeler transform, CUDA, GPU, Move-to-front transform, accelerators, bzip2, data compression | null | null | https://doi.org/10.1145/3673038.3673067 | Proceedings of the 53rd International Conference on Parallel Processing | 2024 | Wei{\ss}enberger, Andr{\'e} and Schmidt, Bertil | Massively Parallel Inverse Block-sorting Transforms for bzip2 Decompression on GPUs | inproceedings | weissenberger:2025:massively-parallel-inverse-block-sorting-transforms-bzip2-decompression-gpu | null | null | null | 10.1145/3673038.3673067 | 856–865 | null | null | null | null | null | null | null | ICPP '24 | Association for Computing Machinery | 10 | null | null | New York, NY, USA | null | null | null | null | 9798400717932 | Gotland, Sweden | null | null | null | null | null | null | null | null | null | null | null | 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 provides an ideal testbed for evaluating the reasoning, modeling, and abstraction capabilities of large language models (LLMs), as it has well-defined structure and objective ground truth while admitting a wide spectrum of skill levels. However, existing evaluations of LLM ability in chess are ad hoc and narrow i... | null | null | null | https://arxiv.org/abs/2510.23948 | null | 2025 | Qianfeng Wen and Zhenwei Tang and Ashton Anderson | ChessQA: Evaluating Large Language Models for Chess Understanding | misc | wen:2025:chessqa-evaluating-large-language-models-chess-understanding | null | null | null | null | null | null | null | null | null | submitted here: https://openreview.net/forum?id=gBz9NMbvYS | null | null | null | null | null | null | null | null | 2510.23948 | null | null | null | null | null | null | null | null | null | null | null | null | null | https://huggingface.co/datasets/wieeii/ChessQA-Benchmark | 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 idea of training Artificial Neural Networks to evaluate chess positions has been widely explored in the last ten years. In this paper we investigated dataset impact on chess position evaluation. We created two datasets with over 1.6 million unique chess positions each. In one of those we also included randomly gene... | chess position evaluation, deep neural network, model evaluation, accuracy | null | null | https://doi.org/10.1007/978-3-031-30442-2_32 | Parallel Processing and Applied Mathematics - 14th International Conference, {PPAM} 2022, Gdansk, Poland, September 11-14, 2022, Revised Selected Papers, Part {I} | 2022 | Dawid Wieczerzak and Pawel Czarnul | Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network | inproceedings | wieczerzak:2022:dataset-experimental-investigation-chess-position-evaluation-neural-network | null | null | null | 10.1007/978-3-031-30442-2_32 | 429--440 | null | 13826 | null | null | null | Roman Wyrzykowski and Jack J. Dongarra and Ewa Deelman and Konrad Karczewski | 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 |
We detail the bread emoji team's submission to the IEEE BigData 2024 Predicting Chess Puzzle Difficulty Challenge. Our solution revolved around the use of ensembled, pretrained, neural chessboard embedders (specifically, truncated Maia and Leela models) and an empirically-guided distribution rescaling postprocessing st... | Training;Transfer learning;Artificial neural networks;Predictive models;Big Data;Data models;Emojis | null | https://github.com/mcognetta/ieee-chess | null | 2024 IEEE International Conference on Big Data (BigData) | 2024 | Woodruff, Tyler and Filatov, Oleg and Cognetta, Marco | The bread emoji Team's Submission to the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty Challenge | inproceedings | woodruff:2024:predicting-chess-puzzle-difficulty | null | null | null | 10.1109/BigData62323.2024.10826037 | 8415--8422 | null | null | null | December | null | null | null | null | null | null | null | null | null | null | null | 2573-2978 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
Estimating the difficulty of chess puzzles provides a rich testbed for studying human–computer interaction and adaptive learning. Building on recent advances and the FedCSIS 2025 Challenge, we address the task of predicting chess puzzle difficulty ratings using a multi-source representation approach. Our approach integ... | null | null | null | http://dx.doi.org/10.15439/2025F2456 | Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS) | 2025 | Haitao Xiao and Daiyuan Yu and Xuegang Wen and Le Chen and Kun Fu | Multi-Source Feature Fusion and Neural Embedding for Predicting Chess Puzzle Difficulty | inproceedings | xiao:2025:multi-source-feature-fusion-neural-embedding-predicting-chess-puzzle-difficulty | null | null | null | 10.15439/2025F2456 | 843--848 | 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 |
Chess is a popular game among many people worldwide and is frequently played online. Although players are ranked based on existing rating systems, automation is essential for coordinating matches in tournaments with thousands of participants. In this study, we analyzed the game records of highly skilled chess players a... | Chess log, player rating prediction, Training, Analytical models, Automation, Computational modeling, Games, Big Data, Decision trees, Model Training Time, Skilled Players, Chess Players, Thousands Of Participants, Process Mining, Internet Gaming, Tree Depth, Stage Of The Game | null | null | https://doi.org/10.1109/BigComp57234.2023.00066 | {IEEE} International Conference on Big Data and Smart Computing, BigComp 2023, Jeju, Republic of Korea, February 13-16, 2023 | 2023 | Habuki Yamada and Nobuko Kishi and Masato Oguchi and Miyuki Nakano | A Method for Estimating Online Chess Game Player Ratings with Decision Tree | inproceedings | yamada:2023:estimating-online-ratings-decision-tree | null | null | null | 10.1109/BIGCOMP57234.2023.00066 | 320--321 | null | null | null | null | null | Hyeran Byun and Beng Chin Ooi and Katsumi Tanaka and Sang{-}Won Lee and Zhixu Li and Akiyo Nadamoto and Giltae Song and Young{-}Guk Ha and Kazutoshi Sumiya and Yuncheng Wu and Hyuk{-}Yoon Kwon and Takehiro Yamamoto | null | null | {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 |
Neurons in large language models often exhibit polysemanticity, simultaneously encoding multiple unrelated concepts and obscuring interpretability. Instead of relying on post-hoc methods, we present MoE-X, a mixture-of-experts (MoE) language model designed to be intrinsically interpretable. Our approach is motivated by... | Mixture of Expert; Interpretability; Polysemanticity | null | null | https://openreview.net/forum?id=6QERrXMLP2 | Forty-second International Conference on Machine Learning | 2025 | Xingyi Yang and Constantin Venhoff and Ashkan Khakzar and Christian Schroeder de Witt and Puneet K. Dokania and Adel Bibi and Philip Torr | Mixture of Experts Made Intrinsically Interpretable | inproceedings | yang2025miyang:2025:mixture-experts-intrinsically-interpretable | null | null | null | null | null | null | null | null | null | https://proceedings.mlr.press/v267/yang25ag.html | null | https://icml.cc/virtual/2025/poster/46377 | null | null | null | null | null | null | null | null | null | null | null | null | We present MoE-X a mixture-of-experts (MoE) language model designed to be intrinsically interpretable. | null | null | null | null | null | null | null | null | null | null | 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 post-AlphaGo era, there has been a renewed interest in search techniques such as Monte Carlo Tree Search (MCTS), particularly in their application to Large Language Models (LLMs). This renewed attention is driven by the recognition that current next-token prediction models often lack the ability for long-term pl... | discrete diffusion model, search, planning, chess, MCTS | https://huggingface.co/datasets/jiacheng-ye/chess10k | https://github.com/HKUNLP/DiffuSearch | https://openreview.net/forum?id=A9y3LFX4ds | The Thirteenth International Conference on Learning Representations, {ICLR} 2025, Singapore, April 24-28, 2025 | 2025 | Jiacheng Ye and Zhenyu Wu and Jiahui Gao and Zhiyong Wu and Xin Jiang and Zhenguo Li and Lingpeng Kong | Implicit Search via Discrete Diffusion: {A} Study on Chess | inproceedings | ye:2025:implicit-search-discrete-diffusion-study-chess | 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 | We propose a model that does implicit search by looking into the future world via discrete diffusion modeling. | null | null | null | null | null | null | null | null | null | null | 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 chess has opened up a way for players to sharpen their skills through move analytics. Based on this feature, a support system called chess advisor can be utilized to assist players in a real-time match. However such system doesn't exist within the website itself, rather an involvement of 3rd party software is re... | Training;Image recognition;Computational modeling;Transfer learning;Neural networks;Software;Real-time systems;Chess Pieces;CNN;Transfer Learning;Simple Neural Network;Image Recognition | null | null | null | 2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS) | 2023 | Yohanes, Gabriel and Nursalim, Mario and Nicholas and Kurniadi, Felix Indra | Chess Piece Image Recognition Using Transfer Learning, Simple Neural Network, and Convolutional Neural Network | inproceedings | yohanes:2024:chess-piece-image-recognition-nn-cnn | null | null | null | 10.1109/AiDAS60501.2023.10284718 | 160--164 | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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 complete connectome of the Drosophila larva brain offers a unique opportunity to investigate whether biologically evolved circuits can support artificial intelligence. We convert this wiring diagram into a Biological Processing Unit (BPU)---a fixed recurrent network derived directly from synaptic connectivity. Desp... | biological inspired AI, biological connectome, chess | null | null | null | Artificial General Intelligence | 2026 | Yu, Siyu and Qin, Zihan and Liu, Tingshan and Xu, Beiya and Vogelstein, R. Jacob and Brown, Jason and Vogelstein, Joshua T. | Biological Processing Units: Leveraging an Insect Connectome to~Pioneer Biofidelic Neural Architectures | inproceedings | yu:2026:biological-processing-units-leveraging-insect-connectome-pioneer-biofidelic-neural-architectures | null | null | https://arxiv.org/pdf/2507.10951 | null | 361--369 | null | null | null | null | null | Ikl{\'e}, Matthew and Kolonin, Anton and Bennett, Michael | null | null | Springer Nature Switzerland | null | null | null | Cham | null | null | null | null | 978-3-032-00800-8 | 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 |
In recent years, Artificial Intelligence (AI) systems have surpassed human intelligence in a variety of computational tasks. However, AI systems, like humans, make mistakes, have blind spots, hallucinate, and struggle to generalize to new situations. This work explores whether AI can benefit from creative decision-maki... | null | null | null | https://doi.org/10.48550/arXiv.2308.09175 | null | 2023 | Tom Zahavy and Vivek Veeriah and Shaobo Hou and Kevin Waugh and Matthew Lai and Edouard Leurent and Nenad Tomasev and Lisa Schut and Demis Hassabis and Satinder Singh | Diversifying {AI:} Towards Creative Chess with AlphaZero | article | zahavy:2023:diversifying-ai-towards-creative-chess-alphazero | null | null | null | 10.48550/ARXIV.2308.09175 | null | null | abs/2308.09175 | CoRR | null | null | null | null | null | null | null | null | null | null | 2308.09175 | 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 |
AI research in chess has been primarily focused on producing stronger agents that can maximize the probability of winning. However, there is another aspect to chess that has largely gone unexamined: its aesthetic appeal. Specifically, there exists a category of chess moves called ``brilliant'' moves. These moves are ap... | null | null | https://github.com/kamronzaidi/brilliant-moves-clf | https://computationalcreativity.net/iccc24/papers/ICCC24_paper_200.pdf | Proceedings of the 15th International Conference on Computational Creativity | 2024 | Zaidi, Kamron and Guerzhoy, Michael | Predicting User Perception of Move Brilliance in Chess | inproceedings | zaidi:2024:predicting-user-perception-move-brilliance-chess | null | null | https://computationalcreativity.net/iccc24/papers/ICCC24_paper_200.pdf | null | 423--427 | null | null | null | null | null | Grace, Kazjon and Llano, Maria Teresa and Martins, Pedro and Hedblom, Maria M. | null | null | Association for Computational Creativity | null | null | null | J{\"o}nk{\"o}ping, Sweden | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | 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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