Year: 2025
Type: conference-paper
Abstract: The FedCSIS 2025 Challenge on Predicting Chess Puzzle Difficulty tasked participants with estimating puzzle ratings directly from board states and solution sequences, without relying on human solver statistics.We propose a three-stage hybrid framework integrating gradient-boosting regressors, a multi-modal neural network, and an XGBoost stacking ensemble.The boosting stage modeled handcrafted structural features derived from FEN and engine metadata, while the multi-modal network jointly learned from structured features and image-rendered chessboards to capture positional and tactical patterns.The residual-based stacking stage explicitly modeled prediction errors to correct systematic biases and enhance performance, particularly for high-difficulty puzzles.Our method achieved a competitive performance, ranking 7th in the preliminary stage and 8th in the final leaderboard.These results demonstrate that combining interpretable boosting models with visual-tactical deep representations and meta-learning provides a robust and computationally efficient alternative to large-scale transformer-based approaches.
DOI: 10.15439/2025f3675
Authors: Ming Liu, Junye Wang, Yinghan Hu, Xiaolin Yang, Defu Lin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415420665