Chess as Sequential Data in a Chess Match Outcome Prediction Using Deep Learning with Various Chessboard Representations

Abstract

Moves made by chess players during the match are certainly some kind of sequence. In this work, we tried to answer the question if such data could be interpreted as sequential. To achieve that, a model build with LSTM layers only was designed. Results performed by the model are justifying that thesis. A novel model architecture was also proposed and trained on multiple data types—chess moves and chess game metadata. Its purpose was to perform as high classification accuracy as possible—we managed to achieve it with a result close to 69%. Moreover, we compared a couple of chessboard representation methods, more precisely a bitmap input and algebraic input, to check which one is more relevant for the neural networks training process. Contrary to what one might suppose, better scores were reached for bitmap input, which from the theoretical point of view carries out less information than algebraic input.