From Minimax to AI: Exploring Game Theory in Chess Strategy

Author(s)	Mr. Girik Gupta
Country	India
Abstract	This paper explores the application of game theory in chess, examining its evolution from classical minimax algorithms to modern artificial intelligence approaches. The study investigates how game-theoretic principles have shaped strategic thinking in chess, analyzing key concepts like Nash equilibrium, minimax, and reinforcement learning. It also discusses the intersection of psychology and strategy, highlighting how human behavior complicates the theoretical model of perfect information. The paper concludes by reflecting on the role of AI in transforming chess and its implications for broader decision-making frameworks.
Keywords	Chess, Game Theory, Artificial Intelligence, AI, Minimax, Nash Equilibrium
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-09
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.47194
Short DOI	https://doi.org/g9pzxz

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