Calibrating Chess Engine Parameters

The author shares their experience in developing a high-performance chess engine that, despite its overall efficiency, faced inexplicable losses in specific game scenarios. Solving this required deep analysis of match data against competitors and fine-tuning algorithmic coefficients. During the process, the author concluded that using AI chats for direct code optimization was largely ineffective. However, neural networks proved to be an indispensable tool for writing automation scripts to control third-party software and for building specialized tools to analyze engine parameters. The article describes the methodology for testing, comparing versions, and iteratively improving algorithms, allowing game engine developers to better understand the debugging and optimization processes of complex systems in a highly competitive environment.