Chess ADC – An Automated Aide-De-Camp

Abstract

Various types of tools and techniques are used to analyse chess games. The existing most successful and accredited method is, electronic boards where it is able to track and extract the movement data with the help of electronic equipment and pressure detecting sensors [1]. But that solution is expensive. Chess ADC is a comprehensive framework that can be used by anyone for practicing and developing chess skills. It allows users to play chess games on a real chessboard and measure their level of skill. Although chess is a very complicated game that has many different patterns of piece movements, all the number of states that a game can have is finite. We can solve chess with just math if we have unlimited amount of computing power [2]. Deep learning models have already been used in research on various board games such as backgammon, checkers, Go and chess [3]. Chess ADC also utilizes these technologies to give a better user experience for the players. We call this system “Chess ADC – An Automated Aide-De-Camp” because it functions as an aide-de-camp for chess. The system uses a special camera rig to capture different states of the board as images. Players are guided with onscreen instructions to set up the environment at the beginning of each game. At this stage, the position of each chess piece is validated. If the system was able to find any misplaced piece, it notifies the player to correct the position. This process is handled using image processing combined with machine learning. After setting up the board correctly, players can start the game. While in the game, each position of the chess piece is tracked and validated against chess rules. This helps to correct the mistakes of the players. The system asks the players to correct the mistakes if it has detected any mistake. Image processing and chess.js library will be used to achieve this. In difficult situations, players can request hints from the system about the best move they can make. The system will give the best move for that situation using the Stockfish engine. At the same time, the system tries to predict the opponent’s next move based on the generated hint from the engine. The best move and the prediction are displayed on the mobile screen of the player so that the player can decide the next move. An artificial neural network (ANN) developed combining one Convolutional Long Short-term Memory (ConvLSTM) neural network and six different Convolutional neural networks (CNN) is used to make predictions about the opponent. Chess-ADC can recognize the winning probability of every move of the chess pieces. And recognize special moves that have an important impact on the probability of winning. And the player can see those good-bad moves and it is very important for the learning process. We use portable notation files for the storing of game details so that the players will be able to view the past games. The system stores all the matches in a database. This way the players can re-watch the games that they have played before and improve their game strategies while looking at the changes in the win percentage. Gathered data are analyzed and advanced reports are generated. Players can access these reports through user accounts. These reports will help the players to identify the best moves and the worst moves that they have made.