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.