Machine Learning Algorithms to Predict Chess960 Result and Develop Opening Themes
This work addresses the problem of analyzing Chess960 openings and outcomes for chess enthusiasts and AI researchers, but it is incremental as it applies standard methods to a new variant.
This study applied machine learning algorithms to predict game outcomes in Chess960 and identify opening themes for each starting position, achieving predictions using KNN Clustering, Random Forest, and Gradient Boosted Trees on datasets of about 500 games per position.
This work focuses on the analysis of Chess 960, also known as Fischer Random Chess, a variant of traditional chess where the starting positions of the pieces are randomized. The study aims to predict the game outcome using machine learning techniques and develop an opening theme for each starting position. The first part of the analysis utilizes machine learning models to predict the game result based on certain moves in each position. The methodology involves segregating raw data from .pgn files into usable formats and creating datasets comprising approximately 500 games for each starting position. Three machine learning algorithms -- KNN Clustering, Random Forest, and Gradient Boosted Trees -- have been used to predict the game outcome. To establish an opening theme, the board is divided into five regions: center, white kingside, white queenside, black kingside, and black queenside. The data from games played by top engines in all 960 positions is used to track the movement of pieces in the opening. By analysing the change in the number of pieces in each region at specific moves, the report predicts the region towards which the game is developing. These models provide valuable insights into predicting game outcomes and understanding the opening theme in Chess 960.