FedCSIS 2025 knowledgepit.ai Competition: Predicting Chess Puzzle Difficulty Part 2 & A Step Toward Uncertainty Contests
We summarize the results of the FedCSIS 2025 machine learning competition organized on the knowledgepit.aionline platform.We recall the competition's goals corresponding to estimations of the chess puzzle difficulty levels, we refer to the winning solutions, and we also compare the scope of this year's competition (and particularly the data available to competition participants) with its previous edition associated with the IEEE BigData 2024 conference.Finally, we discuss the new functionality of the knowledgepit.aiplatform, which enables competition participants to submit additional uncertainty masks reflecting their assessment of test cases that are mostly problematic for their machine learning models.
Authors
- Dominik Ślȩzak (ORCID: https://orcid.org/0000-0003-2453-4974)
- Maciej Świechowski (ORCID: https://orcid.org/0000-0002-8941-3199)
- Michał Ślęzak (ORCID: https://orcid.org/0009-0009-4414-8617)
- Jan Zyśko
Institutions
- Grail (United States) (US)
- University of Warsaw (PL)
- Software (Spain) (ES)
Publication Details
- Journal
- Annals of Computer Science and Information Systems
- Published
- 2025-10-15
- DOI
- https://doi.org/10.15439/2025f5937
- Citations
- 7
- Primary Topic
- Sports Analytics and Performance
- Type
- article
- Field-Weighted Citation Impact
- 34.89
Funders
- European Regional Development Fund