Paper Contents
Abstract
This paper presents a strategic analysis and implementation of the classic game Tic-Tac-Toe using principles of Artificial Intelligence (AI) and Game Theory. By modeling the game as a finite, deterministic, and zero-sum game, we apply the Minimax algorithm with optimization techniques such as alpha-beta pruning to enable an AI agent to make optimal moves. The study explores how AI can simulate human-like decision-making through strategic evaluation of game states. Game theory is employed to analyze optimal strategies and equilibria, ensuring that the AI either wins or forces a draw against any opponent.This work demonstrates how fundamental AI algorithms and game theory can be effectively applied to simple strategic games, making it an educational tool for understanding core concepts in both disciplines. Future work may involve extending this to more complex games and learning-based models.
Copyright
Copyright © 2025 Sourabha B R. This is an open access article distributed under the Creative Commons Attribution License.
