Chess: Toward a Methodological Solution of Chess

Abstract

In this article, I argue that powerful modern chess tools—including neural network engines, cloud-based databases, and endgame tablebases—now enable a human player to play consistently “error-free” chess. I begin with a personal history of my correspondence chess career, which spans over 50 years. Then, I detail the specific tools that contribute to what I call the methodological solution to chess. These tools apply across the Opening, Middlegame, and Endgame. My conclusion is that the resources currently available are fully adequate to achieve accurate evaluations for virtually all remaining unanalyzed middlegame positions. The only possible counterargument is the existence of long, maneuvering positions that might still challenge today's neural networks. However, I have yet to find one that does not ultimately result in a draw. The methodological solution to chess has significant implications for the future of the game. The ability to analyze with confidence and understand optimal play will accelerate the learning process for players at all levels. Looking forward, the remaining challenge is to determine whether truly unique structural positions exist that remain beyond the reach of current neural networks.