Tic-Tac-Toe AI

This project explores different AI strategies for playing Tic-Tac-Toe, demonstrating how various algorithms tackle decision-making in a simple yet strategic game. It’s a fun and educational way to see how AI "thinks" when trying to win or force a draw.

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🤖 AI Types & Strategies

1. Random AI:

   • Strategy: Makes completely random moves.
   • Outcome: Plays unpredictably but lacks any real strategy—fun for testing!

2. Rule-Based AI:

   • Strategy: Follows a basic set of if-else rules (e.g., "Take the center if available").
   • Outcome: Decent against casual players but easily outsmarted by experienced opponents.

3. Minimax AI:

   • Strategy: Uses the minimax algorithm to simulate all possible moves and counter-moves. Always aims to maximize its chances of winning or forcing a draw.
   • Outcome: Nearly unbeatable—will never lose if it goes first and can force a draw if it goes second.

4. Alpha-Beta Pruning AI:

   • Strategy: An optimized version of Minimax that skips irrelevant branches to reduce computation time.
   • Outcome: Same as Minimax but much faster, making it efficient for larger game trees.