ARTIFICIAL INTELLIGENCE IN HIGH-LEVEL CHESS PREPARATION
Keywords:
Artifical intelligence, preparation, top players, chess engines, databases.Abstract
Modern chess at the elite level has been revolutionized by artificial intelligence (AI) and data-driven methods. This article explores how AI is employed in high-level tournament preparation, focusing on building a comprehensive data system to analyze opponents’ playing styles, strengths, weaknesses, and opening repertoires. We discuss the role of AI-powered chess engines and large game databases in modern preparation, and outline key phases of opponent analysis: from PGN game extraction and statistical profiling to automated engine evaluation of mistakes and trends. Methods include leveraging online platforms and professional databases (ChessBase) to gather opponent data, using engines like Stockfish and Leela Chess Zero for automatic game analysis, constructing opening trees and “mistake heatmaps,” and visualizing results through dashboards and graphs. We present case studies, including insights from world championship preparations, demonstrating how top players integrate AI findings into practical game plans. The findings show that AI not only identifies an opponent’s technical tendencies but also informs strategic decisions such as which openings to prepare or which game phase to target. We conclude with a discussion on the implications of AI-assisted preparation for players and coaches, and the future prospects of AI in competitive chess.
References
1. Bilalić, M., Graf, M., & Vaci, N. (2024). Computers and chess masters: The role of AI in transforming elite human performance. British Journal of Psychology. Advance online publication. https://doi.org/10.1111/bjop.12750
2. Intel Corporation. (2025, September 12). Intel-Powered agentic AI analyzes chess players’ every move. Intel Newsroom. Retrieved from https://newsroom.intel.com/artificial-intelligence/intel-powered-agentic-ai-analyzes-chess-players-every-move
3. Skolkovo Institute of Science and Technology. (2021, April 30). Russian chess grandmaster uses Zhores supercomputer to train for tournament [Press release]. Skoltech News. Retrieved from https://sk.ru/news/russian-chess-grandmaster-uses-zhores-supercomputer-to-train-for-tournament
4. Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140–1144. https://doi.org/10.1126/science.aar6404
5. Zhou, C. (2025). The application of artificial intelligence in the training process of chess players. Proceedings of the 3rd International Conference on Software Engineering and Machine Learning, 163(1), 100–105. https://doi.org/10.54254/2755-2721/2025.25188
6. Egamberdieva, B., Ishtayev, J., Korobeynikov, G., Khasanov, O., Gapparov, Z., Xursanova, R., Pirmatov, O., & Juraev, I. (2025). Brilliant moves in chess and early grandmaster success: A study of the world’s youngest grandmasters. Slobozhanskyi Herald of Science and Sport, 29(2), 151–166. https://doi.org/10.15391/snsv.2025-2.06
7. Lane, D. M., & Chang, Y. H. A. (2018). Chess knowledge predicts chess memory even after controlling for chess experience: Evidence for the role of high-level processes. Memory & Cognition, 46, 337–348.
8. Fattahi, F., Geshani, A., Jafari, Z., Jalaie, S., & Mahini, M. S. (2015). Auditory memory function in expert chess players. Medical Journal of the Islamic Republic of Iran, 29, 275.
