Understanding Artificial Intelligence in Chess: The RubiChess Case Study

Software chess engines and advanced solutions based on Artificial Intelligence (AI) are changing the historical game of chess. Several AI-based chess engines are now available as closed or open software solutions with unique characteristics and game abilities. However, these AI-based engines are very complex to analyze from a functional point-of-view, and a deep study of their source code is not often enough to understand the advantages and disadvantages of their game approach. In this paper, we adopted a two-fold approach to study the behavior of a well-known AI-based chess engine called RubiChess. From one side, we studied the RubiChess architecture to understand its main game strategies and adopted algorithms. On the other side, we simulated a set of matches played against other AI chess engines, and we evaluated these matches (in different conditions) by using statistical tests and data visualization to determine the properties that made RubiChess unique. For example, the simulations highlight that RubiChess performs better as a white player and during "slow" games. This gives engine developers and players important insights into how RubiChess plays.