End-to-End Chess Recognition

Chess recognition refers to the task of identifying the chess pieces configuration from a chessboard image. Contrary to the predominant approach that aims to solve this task through the pipeline of chessboard detection, square localization, and piece classification, we rely on the power of deep learning models and introduce two novel...

A Role of Peripheral Vision in Chess? Evidence from a Gaze-contingent Method

Chunking theory and previous eye-tracking studies suggest that expert chess players use peripheral vision to judge chess positions and determine the best moves to play. However, the role of peripheral vision in chess has largely been inferred rather than tested through controlled experimentation. In this study, we used a gaze-contingent paradigm...

The Influence of the Size of the Search Space on Learning to Play Chess using Deep Reinforcement Learning Algorithms

The current state-of-the-art solutions for playing Chess, are created using deep reinforcement learning. AlphaZero, the current world champion, uses ’policy networks’ and ’value network’ for selecting moves and evaluating positions respectively. However, the training of these networks are done using reinforcement learning from games of selfplay....

Tribler-G: A Decentralized Social Network for Playing Chess Online

There are currently many systems that offer online board games, for instance, social networking sites, where board games enjoy enormous popularity, but also systems such as the Internet Chess Club, which has been around for over a decade. However, these systems are all centralized and typically have drawbacks for the user, such as subscription...