Machine Learning Algorithms to Predict Chess960 Result and Develop
  Opening Themes
Shreyan Deo Nishchal Dwivedi
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.