Estimating Chess Puzzle Difficulty Without Past Game Records Using a Human Problem-Solving Inspired Neural Network Architecture

Anan Schütt, Tobias Huber, Elisabeth André

Abstract: For chess players to sharpen their tactical skills effectively, they train on chess puzzles with a fitting difficulty level. This paper presents an approach to estimate the difficulty level of chess puzzles using a deep neural network. The proposed approach achieved second place in the IEEE BigData Cup 2024 competition: Predicting chess puzzle difficulty. For the design of our network architecture, we take inspiration from the human problem-solving process for chess puzzles. We train the model to predict the correct move as an auxiliary task to improve the training process. We also predict themes, which are patterns in chess puzzles as a second auxiliary task. Finally, we use the uncertainty in the position, i.e. how incorrect the model’s move prediction is, as a further input to guide the estimation of the puzzle difficulty.

External IDs:dblp:conf/bigdataconf/SchuttHA24