The Use of Different Artificial Intelligence Models in Chess

Abstract

Chess has long served as a fundamental environment for the development and evaluation of artificial intelligence methods. This paper examines the evolution of AI models used in chess, starting from early rule-based systems and search algorithms to modern machine learning and deep reinforcement learning approaches. Particular attention is given to key milestones such as minimax-based engines and advanced systems like AlphaZero. The study also compares the strengths and limitations of different models and discusses their impact on both chess and the broader field of artificial intelligence. The findings suggest that the progression of chess AI reflects major technological shifts, with future developments likely focusing on efficiency, interpretability, and hybrid model design