Tic-Tac-Toe Game with AI

 

Tic-Tac-Toe Game with AI - IT & Computer Engineering Project Guide

1. Introduction

This document provides a comprehensive guide for developing a Tic-Tac-Toe game with AI, suitable for IT and Computer Engineering students. The project involves building the traditional 3x3 Tic-Tac-Toe game with a computer player that uses artificial intelligence techniques to play against a human opponent.

2. Objective

To design and implement a classic Tic-Tac-Toe game with an AI opponent using the Minimax algorithm. The game should allow human vs AI and optionally human vs human gameplay.

3. Requirements

Hardware Requirements:

·         - PC or Laptop

·         - 4GB RAM minimum

Software Requirements:

·         - Python 3.x

·         - Tkinter (for GUI) or Console-based interface

·         - IDE or text editor (VS Code, PyCharm, etc.)

4. Methodology

Step-by-step process to build the AI-based Tic-Tac-Toe game:

1.       1. Design the game board and user interface (GUI or console).

2.       2. Implement game logic for two-player Tic-Tac-Toe.

3.       3. Develop an AI using the Minimax algorithm.

4.       4. Integrate AI into the game to play against the user.

5.       5. Add conditions for game end (win/draw) and replay.

6.       6. Test and refine the game.

5. Game Logic

The game logic includes:
- Board Representation: 2D list or array
- Player Moves: X or O
- Win Conditions: Rows, columns, diagonals
- Turn Management: Alternate between player and AI

6. AI Logic - Minimax Algorithm

The Minimax algorithm is a recursive strategy for decision-making in turn-based games:

def minimax(board, depth, is_maximizing):
    if check_winner(board) == 'AI':
        return 1
    elif check_winner(board) == 'Player':
        return -1
    elif is_draw(board):
        return 0

    if is_maximizing:
        best_score = -float('inf')
        for move in get_available_moves(board):
            make_move(board, move, 'AI')
            score = minimax(board, depth + 1, False)
            undo_move(board, move)
            best_score = max(score, best_score)
        return best_score
    else:
        best_score = float('inf')
        for move in get_available_moves(board):
            make_move(board, move, 'Player')
            score = minimax(board, depth + 1, True)
            undo_move(board, move)
            best_score = min(score, best_score)
        return best_score

7. Features

·         - Single-player mode against AI

·         - Smart AI using Minimax algorithm

·         - Graphical or console-based user interface

·         - Win, lose, or draw outcomes

8. Limitations

·         - Limited to 3x3 board only

·         - Minimax performance slows with larger boards (not scalable)

9. Future Scope

- Extend the game to support multiplayer over the network
- Improve AI with pruning (Alpha-Beta Pruning)
- Add support for custom board sizes (e.g., 4x4, 5x5)
- Enhance GUI using frameworks like Pygame or web-based UI

10. Conclusion

This project introduces students to game development and basic AI implementation. It provides a hands-on experience with algorithms and enhances understanding of decision-making logic in games.