Systematic analysis of the evolution of chess computers

Abstract

This thesis presents a systematic review of the evolution of chess computers, tracing the development of key technologies and algorithms from the inception of the first theoretical chess programs to modern AI-driven systems. The study covers eight pivotal chess computers—Turochamp, Mac Hack VI, Chess 4.5, Hitech, Fritz, Deep Blue, Stockfish, and AlphaZero—each representing significant milestones in the history of chess computing. Through a comprehensive literature review and analysis, the thesis identifies the major algorithmic advancements that have shaped the performance and strategic capabilities of these systems, including alpha-beta pruning, bitboard representation, hardware-based pattern recognition, and the application of deep learning. The findings highlight a trend of increasing computational efficiency and strategic depth, driven by both algorithmic and hardware innovations. Additionally, the thesis discusses the broader trends behind these developments. This study not only provides a historical overview but also offers insights into the future potential of chess computers, particularly in the context of emerging technologies such as neural networks and quantum computing.