Moves Based Prediction of Chess Puzzle Difficulty with Convolutional Neural Networks

Chess is a complex logical game involving ongoing strategic forward planning and evaluation. Solving chess puzzles is one of the most common ways of training and developing chess skills. It involves continuing the game from a certain initial chessboard state against a real or AI opponent until defeat or a significant advantage is achieved. To ensure solving chess puzzles is efficient and engaging for the real player, it is important to understand the difficulty of the puzzle and match it with the skill of the solver. Accurate and fast assessment of puzzle difficulty is therefore a critical problem that online chess platforms need to solve at scale to optimally match many real players with adequate puzzles. To solve this challenge, we propose a chess knowledge-agnostic strategy to predict puzzle difficulty based solely on the moves of the players against their opponents trying to solve the puzzles. Specifically designed deep convolutional neural networks (CNN) were deployed as supervised learning predictors, fed with player moves represented as multichannel chessboard images. Extensive testing of our model with almost 4 million training examples against Glicko-2 evaluated puzzle difficulty ratings—considered as ground truth—resulted in good predictive performance. This was acknowledged by our runner-up result as the 7th place in the IEEE Big Data 2024 Cup and highlighted the capability of fast puzzle difficulty prediction based only on players’ moves as evidence, with no prior chess knowledge nor utilization of computationally expensive chess engines.