From ccee11805d93f8ef512712c47b87084e215010b3 Mon Sep 17 00:00:00 2001 From: Christopher Akiki Date: Tue, 31 Dec 2024 11:27:41 +0100 Subject: [PATCH] Add Iavich paper and IEEE big data cup proceedings --- lichess.bib | 68 +++++++++++++++++++++++++++++++++++++++++++++++++ lichess.html | 71 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 139 insertions(+) diff --git a/lichess.bib b/lichess.bib index 0e1cf2e..e29c9da 100644 --- a/lichess.bib +++ b/lichess.bib @@ -80,6 +80,14 @@ @inproceedings{bertrand:2023:limitations-elo-real-world-games-transitive-not-add editor = {Francisco J. R. Ruiz and Jennifer G. Dy and Jan{-}Willem van de Meent}, } +@inproceedings{bjorkqvist:2024:estimating-puzzlingness-chess-puzzles, + title = {{Estimating the Puzzlingness of Chess Puzzles}}, + author = {Sebastian Bj\"{o}rkqvist}, + year = {2024}, + booktitle = {{IEEE} International Conference on Big Data, Big Data 2024, Washington DC, USA, December 15-18, 2024}, + publisher = {{IEEE}}, +} + @article{chowdhary:2023:quantifying-human-performance-chess, title = {Quantifying human performance in chess}, author = {Chowdhary, Sandeep and Iacopini, Iacopo and Battiston, Federico}, @@ -270,6 +278,18 @@ @mastersthesis{hoque:2022:classification-anomaly-detection-chess annote = {Chess is a strategy board game with its inception dating back to the 15th century. The Covid-19 pandemic has led to a chess boom online with 95,853,038 chess games being played during January, 2021 on lichess.com. Along with the chess boom, instances of cheating have also become more rampant. Classifications have been used for anomaly detection in different fields and thus it is a natural idea to develop classifiers to detect cheating in chess. However, there are no specific examples of this, and it is difficult to obtain data where cheating has occurred. So, in this paper, we develop 4 machine learning classifiers, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Multinomial Logistic Regression, and K-Nearest Neighbour classifiers to predict chess game results and explore predictors that produce the best accuracy performance. We use Confusion Matrix, K Fold Cross-Validation, and Leave-One-Out Cross-Validation methods to find the accuracy metrics. There are three phases of analysis. In phase I, we train classifiers using 1.94 million over the board game as training data and 20 thousand online games as testing data and obtain accuracy metrics. In phase II, we select a smaller pool of 212 games, select additional predictor variables from chess engine evaluation of the moves played in those games and check whether the inclusion of the variables improve performance. Finally, in phase III, we investigate for patterns in misclassified cases to define anomalies. From phase I, the models are not performing at a utilizable level of accuracy (44-63\%). For all classifiers, it is no better than deciding the class with a coin toss. K-Nearest Neighbour with K = 7 was the best model. In phase II, adding the new predictors improved the performance of all the classifiers significantly across all validation methods. In fact, using only significant variables as predictors produced highly accurate classifiers. Finally, from phase III, we could not find any patterns or significant differences between the predictors for both correct classifications and misclassifications. In conclusion, machine learning classification is only one useful tool to spot instances that indicates anomalies. However, we cannot simply judge anomalous games using only this method.}, } +@inproceedings{iavich:2024:detecting-fair-play-violations-chess-neural-networks, + title = {Detecting Fair Play Violations in Chess Using Neural Networks}, + author = {Iavich, Maksim and Kevanishvili, Zura}, + year = {2024}, + booktitle = {Proceedings of 29th International Conference Information Society and University Studies}, + publisher = {CEUR-WS.org}, + series = {{CEUR} Workshop Proceedings}, + volume = {3341}, + pages = {121--127}, + url = {https://ceur-ws.org/Vol-3885/paper13.pdf}, +} + @article{jenner:2024:evidence-lookahead-chess-neural-network, title = {Evidence of Learned Look-Ahead in a Chess-Playing Neural Network}, author = {Erik Jenner and Shreyas Kapur and Vasil Georgiev and Cameron Allen and Scott Emmons and Stuart Russell}, @@ -414,6 +434,14 @@ @inproceedings{mcilroy-young:2022:learning-models-individual-behavior-chess editor = {Aidong Zhang and Huzefa Rangwala}, } +@inproceedings{milosz:2024:predicting-puzzle-difficulty-transformers, + title = {{Predicting Chess Puzzle Difficulty with Transformers}}, + author = {Szymon Mi{\l}osz and Pawe{\l} Kapusta}, + year = {2024}, + booktitle = {{IEEE} International Conference on Big Data, Big Data 2024, Washington DC, USA, December 15-18, 2024}, + publisher = {{IEEE}}, +} + @article{mok:2021:time-online-digital-well-being, title = {The Complementary Nature of Perceived and Actual Time Spent Online in Measuring Digital Well-being}, author = {Mok, Lillio and Anderson, Ashton}, @@ -565,6 +593,14 @@ @inproceedings{rabii:2021:revealing-game-dynamics-word-embeddings editor = {David Thue and Stephen G. Ware}, } +@inproceedings{rafaralahy:2024-pairwise-ltr-chess-puzzle-difficulty-prediction, + title = {{Pairwise Learning to Rank for Chess Puzzle Difficulty Prediction}}, + author = {Andry Rafaralahy}, + year = {2024}, + booktitle = {{IEEE} International Conference on Big Data, Big Data 2024, Washington DC, USA, December 15-18, 2024}, + publisher = {{IEEE}}, +} + @inproceedings{rosemarin:2019:playing-chess-human-level-style, title = {Playing Chess at a Human Desired Level and Style}, author = {Hanan Rosemarin and Ariel Rosenfeld}, @@ -597,6 +633,14 @@ @article{russel:2022:thinking-online-chess-computation abstract = {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 computational resources are spent. We tested this hypothesis in the domain of complex planning by analyzing how humans managed time available for thinking in over 12 million online chess matches. We found that players spent more time thinking in board positions where planning was more beneficial. This effect was greater in stronger players, and additionally strengthened by considering only the information available to the player at the time of choice. Finally, we found that the qualitative features of this relationship were consistent with a policy that considers the empirically-measured cost of spending time in chess. This provides evidence that human efficiency is supported by intelligent selection of when to apply computation.}, } +@inproceedings{ruta:2024:moves-based-prediction-chess-puzzle-difficulty-convolutional-neural-networks, + title = {{Moves Based Prediction of Chess Puzzle Difficulty with Convolutional Neural Networks}}, + author = {Dymitr Ruta and Ming Liu and Ling Cen}, + year = {2024}, + booktitle = {{IEEE} International Conference on Big Data, Big Data 2024, Washington DC, USA, December 15-18, 2024}, + publisher = {{IEEE}}, +} + @techreport{salant:2022:complexity-satisficing-theory-evidence-chess, title = {Complexity and Satisficing: Theory with Evidence from Chess}, author = {Salant, Yuval and Spenkuch, Jorg L}, @@ -623,6 +667,14 @@ @article{sanjaya:2022-non-transitivity-chess url = {https://doi.org/10.3390/a15050152}, } +@inproceedings{schuett:2024:estimating-chess-puzzle-difficulty-without-past-records-using-neural-network, + title = {{Estimating Chess Puzzle Difficulty Without Past Game Records Using a Human Problem-Solving Inspired Neural Network Architecture}}, + author = {Anan Sch\"{u}tt and Tobias Huber and Elisabeth Andr\'{e}}, + year = {2024}, + booktitle = {{IEEE} International Conference on Big Data, Big Data 2024, Washington DC, USA, December 15-18, 2024}, + publisher = {{IEEE}}, +} + @misc{schultz:2024:mastering-board-games-external-internal-planning-language-models, title = {Mastering Board Games by External and Internal Planning with Language Models}, author = {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 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 Toma\v{s}ev}, @@ -748,6 +800,14 @@ @inproceedings{wieczerzak:2022:dataset-experimental-investigation-chess-position editor = {Roman Wyrzykowski and Jack J. Dongarra and Ewa Deelman and Konrad Karczewski}, } +@inproceedings{woodruff:2024:predicting-chess-puzzle-difficulty, + title = {{The bread emoji Team's Submission to the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty Challenge}}, + author = {Tyler Woodruff and Oleg Filatov and Marco Cognetta}, + year = {2024}, + booktitle = {{IEEE} International Conference on Big Data, Big Data 2024, Washington DC, USA, December 15-18, 2024}, + publisher = {{IEEE}}, +} + @inproceedings{yamada:2023:estimating-online-ratings-decision-tree, title = {A Method for Estimating Online Chess Game Player Ratings with Decision Tree}, author = {Habuki Yamada and Nobuko Kishi and Masato Oguchi and Miyuki Nakano}, @@ -782,3 +842,11 @@ @mastersthesis{zelek:2022:topological-data-analysis-chess keywords = {Chess, Topological Data Analysis, Design Patterns, Data modeling, Modules, Category theory, Topology}, annote = {This thesis uses Topological Data Analysis to examine the data collectedfrom the lichess.org portal. The analysis was based on the games of players playing at different levels. The purpose of the analysis was to distinguish groups of players and players with the highest ranking from eachother. Each player's game is represented by a multidimensional vectorthat encodes information about the course of the game. There are threeapproaches to creating this vector, allowing us to focus on different aspects of the chess game. The proposed analysis was carried out with theintention of verifying the Topological Data Analysis as a tool for analyzing chess games. As a result, it was shown that Topological Data Analysiscan be a potential tool for recognizing the quality of a given player, if wehave enough number of his games, and to reconstruct player rankings. Asignificant result is also the potential for further research for which this thesis could be the foundation.}, } + +@inproceedings{zysko:2024:predicting-chess-puzzle-difficulty, + title = {{IEEE Big Data Cup 2024 Report: Predicting Chess Puzzle Difficulty at KnowledgePit.ai}}, + author = {Jan Zy\'sko and Maciej \'Swiechowski and Sebastian Stawicki and Katarzyna Jagie{\l}a and Andrzej Janusz and Dominik \'{S}l\k{e}zak}, + year = {2024}, + booktitle = {{IEEE} International Conference on Big Data, Big Data 2024, Washington DC, USA, December 15-18, 2024}, + publisher = {{IEEE}}, +} diff --git a/lichess.html b/lichess.html index 434760b..71dc9f7 100644 --- a/lichess.html +++ b/lichess.html @@ -247,6 +247,13 @@ Learning Research. PMLR, 2023. https://proceedings.mlr.press/v206/bertrand23a.html. +
+Björkqvist, Sebastian. “Estimating the +Puzzlingness of Chess Puzzles.” In IEEE +International Conference on Big Data, Big Data 2024, Washington DC, USA, +December 15-18, 2024. IEEE, 2024. +
Chowdhary, Sandeep, Iacopo Iacopini, and Federico Battiston. @@ -400,6 +407,16 @@ Classifiers for Anomaly Detection in Chess.” Master's thesis, Minnesota State University, Mankato, 2021.
+
+Iavich, Maksim, and Zura Kevanishvili. “Detecting Fair Play +Violations in Chess Using Neural Networks.” In Proceedings of +29th International Conference Information Society and University +Studies, 3341:121–27. CEUR Workshop Proceedings. +CEUR-WS.org, 2024. https://ceur-ws.org/Vol-3885/paper13.pdf. +
Jenner, Erik, Shreyas Kapur, Vasil Georgiev, Cameron Allen, Scott @@ -522,6 +539,14 @@ Dauphin, Percy Liang, and Jennifer Wortman Vaughan, 24482–97, 2021. https://proceedings.neurips.cc/paper/2021/hash/ccf8111910291ba472b385e9c5f59099-Abstract.html.
+
+Miłosz, Szymon, and Paweł Kapusta. “Predicting Chess Puzzle Difficulty with +Transformers.” In IEEE International +Conference on Big Data, Big Data 2024, Washington DC, USA, December +15-18, 2024. IEEE, 2024. +
Mok, Lillio. “Measuring the Digital Welfare of Online Social @@ -628,6 +653,15 @@ 187–94. AAAI Press, 2021. https://ojs.aaai.org/index.php/AIIDE/article/view/18907.
+
+Rafaralahy, Andry. “Pairwise Learning to Rank +for Chess Puzzle Difficulty Prediction.” In +IEEE International Conference on Big Data, Big Data +2024, Washington DC, USA, December 15-18, 2024. IEEE, +2024. +
Rosemarin, Hanan, and Ariel Rosenfeld. “Playing Chess at a Human @@ -656,6 +690,15 @@ Value of Computation.” PsyArXiv, 2022. doi:10.31234/osf.io/8j9zx.
+
+Ruta, Dymitr, Ming Liu, and Ling Cen. “Moves +Based Prediction of Chess Puzzle Difficulty with Convolutional Neural +Networks.” In IEEE International +Conference on Big Data, Big Data 2024, Washington DC, USA, December +15-18, 2024. IEEE, 2024. +
Salant, Yuval, and Jorg L Spenkuch. Complexity and Satisficing: @@ -678,6 +721,16 @@ External and Internal Planning with Language Models,” 2024. https://arxiv.org/abs/2412.12119.
+
+Schütt, Anan, Tobias Huber, and Elisabeth André. “Estimating Chess Puzzle Difficulty Without Past Game +Records Using a Human Problem-Solving Inspired Neural Network +Architecture.” In IEEE International +Conference on Big Data, Big Data 2024, Washington DC, USA, December +15-18, 2024. IEEE, 2024. +
Schwarzschild, Avi, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad @@ -765,6 +818,15 @@ Science. Springer, 2022. doi:10.1007/978-3-031-30442-2\_32.
+
+Woodruff, Tyler, Oleg Filatov, and Marco Cognetta. “The bread emoji Team’s Submission to the IEEE BigData +2024 Cup: Predicting Chess Puzzle Difficulty Challenge.” +In IEEE International Conference on Big Data, Big Data +2024, Washington DC, USA, December 15-18, 2024. IEEE, +2024. +
Yamada, Habuki, Nobuko Kishi, Masato Oguchi, and Miyuki Nakano. “A @@ -789,6 +851,15 @@ Zelek, Jakub. “Topological Data Analysis in Chess.” Master's thesis, Jagiellonian University, 2022.
+
+Zyśko, Jan, Maciej Świechowski, Sebastian Stawicki, Katarzyna Jagieła, +Andrzej Janusz, and Dominik Ślęzak. “IEEE Big +Data Cup 2024 Report: Predicting Chess Puzzle Difficulty at +KnowledgePit.ai.” In IEEE International +Conference on Big Data, Big Data 2024, Washington DC, USA, December +15-18, 2024. IEEE, 2024. +