IEEE Big Data Cup 2024 Report: Predicting Chess Puzzle Difficulty at KnowledgePit.ai

We summarize the results of the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty – a data science competition organized at the knowledgepit.ai platform in association with the IEEE BigData 2024 conference. We describe the competition goal and tie it to existing research on human-computer interaction, focusing on task difficulty estimation and aligning human and AI behavior. We explain how we acquired and processed the data, separately for training and testing datasets. We review submitted solutions and evaluate their performance by comparing them to a simple benchmark. We explain how the achieved rating differences translate to user experience when solving chess puzzles. We further explore the concept of chess puzzle difficulty by replicating competition results with puzzle ratings obtained using chess bots only. We conclude with a summary of our findings and directions for future studies, as well as an invitation to the next edition of the competition in 2025.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10825289
Citations
18
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
20.33
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article

IEEE Big Data Cup 2024 Report: Predicting Chess Puzzle Difficulty at KnowledgePit.ai

Andrzej Janusz, Dominik Ślȩzak, Maciej Świechowski, Sebastian Stawicki et al.
18 citations
Sports Analytics and Performance
20.33
article

IEEE Big Data Cup 2024 Report: Predicting Chess Puzzle Difficulty at KnowledgePit.ai

Andrzej Janusz, Dominik Ślȩzak, Maciej Świechowski, Sebastian Stawicki, Jan Zyśko, Katarzyna Jagieła
article en
18 citations

Abstract

We summarize the results of the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty – a data science competition organized at the knowledgepit.ai platform in association with the IEEE BigData 2024 conference. We describe the competition goal and tie it to existing research on human-computer interaction, focusing on task difficulty estimation and aligning human and AI behavior. We explain how we acquired and processed the data, separately for training and testing datasets. We review submitted solutions and evaluate their performance by comparing them to a simple benchmark. We explain how the achieved rating differences translate to user experience when solving chess puzzles. We further explore the concept of chess puzzle difficulty by replicating competition results with puzzle ratings obtained using chess bots only. We conclude with a summary of our findings and directions for future studies, as well as an invitation to the next edition of the competition in 2025.

University of Warsaw (PL)
Openalex Percentile: Top 1%
Sports Analytics and Performance
20.33
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