Predicting Chess Puzzle Difficulty with Transformers
This work addresses personalized chess training and educational technology, but it is incremental as it builds on existing transformer methods for a specific domain.
The study tackled the challenge of quantifying chess puzzle difficulty by developing GlickFormer, a transformer-based model that approximates the Glicko-2 rating system, achieving superior performance over the state-of-the-art ChessFormer baseline on a dataset of over 4 million puzzles and placing 11th in a competition.
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 approximating the Glicko-2 rating system. Unlike conventional chess engines that optimize for game outcomes, GlickFormer models human perception of tactical patterns and problem-solving complexity. The proposed model utilizes a modified ChessFormer backbone for spatial feature extraction and incorporates temporal information via factorized transformer techniques. This approach enables the capture of both spatial chess piece arrangements and move sequences, effectively modeling spatio-temporal relationships relevant to difficulty assessment. Experimental evaluation was conducted on a dataset of over 4 million chess puzzles. Results demonstrate GlickFormer's superior performance compared to the state-of-the-art ChessFormer baseline across multiple metrics. The algorithm's performance has also been recognized through its competitive results in the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty competition, where it placed 11th. The insights gained from this study have implications for personalized chess training and broader applications in educational technology and cognitive modeling.