Tic-Tac-Toe AI (Minimax Algorithm)

Overview

This project implements a Tic-Tac-Toe game with an AI opponent using the Minimax algorithm. The graphical interface is built using Pygame, allowing a user to play against a computer that makes optimal decisions.

The AI evaluates possible future board states and selects moves that maximize its chances of winning while minimizing the opponent’s chances.

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Features

• Play Tic-Tac-Toe against an AI opponent

• Choose to play as X or O

• AI uses the Minimax algorithm for decision making

• Detects:

  • Wins
  • Losses
  • Draws

• Interactive graphical interface using Pygame

• Restart option after game completion

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Project Structure

project/
│
├── runner.py          # Main game interface using Pygame
├── tictactoe.py       # Game logic and Minimax AI implementation
├── OpenSans-Regular.ttf
└── README.md

runner.py

Handles:

• Game window
• Player input
• Rendering board and moves
• AI turn management

tictactoe.py

Contains the core game logic:

• Board representation
• Determining valid moves
• Checking winners
• Minimax algorithm for AI decision making

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Requirements

Install the required library:

pip install pygame

Python version:

Python 3.8+

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How to Run

1. Navigate to the project directory.

2. Run the main game file:

python runner.py

3. Choose whether to play as X or O.

4. Click on the grid to place your move.

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Game Rules

• The game is played on a 3 × 3 board.
• Players take turns placing X and O.
• The first player to align three marks in a row, column, or diagonal wins.
• If all spaces are filled and no player wins, the game ends in a draw.

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Minimax Algorithm

The AI uses the Minimax algorithm, a decision-making algorithm used in game theory.

How it works

1. Generate all possible moves from the current board state.

2. Simulate each move recursively.

3. Assign scores to terminal states:

   • X win → +1
   • O win → −1
   • Draw → 0

4. Choose the move that maximizes the AI's chance of winning.

The algorithm ensures the AI always selects the optimal move.

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Example Gameplay

1. Player selects X.
2. Player clicks a square.
3. AI evaluates all possible board states using Minimax.
4. AI makes the best move automatically.

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Concepts Demonstrated

This project demonstrates several Artificial Intelligence and programming concepts:

• Minimax algorithm
• Game state evaluation
• Recursive search
• Adversarial search
• Python object-oriented programming
• GUI development using Pygame

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Possible Improvements

Future enhancements may include:

• Alpha-Beta pruning for faster search
• Improved UI design
• Difficulty levels
• Move animations
• Score tracking

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Author

Developed as part of an Artificial Intelligence / Game AI programming exercise.