https://github.com/savinrazvan/tictactoe

An AI that plays Tic-Tac-Toe using the Minimax algorithm, ensuring it always wins or draws. Demonstrates optimal decision-making and game logic implementation.
https://github.com/savinrazvan/tictactoe

ai algorithmic-game-theory board-games educational-project game-ai game-development minimax-algorithm optimal-decision-making pygame python tic-tac-toe unbeatable-ai

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An AI that plays Tic-Tac-Toe using the Minimax algorithm, ensuring it always wins or draws. Demonstrates optimal decision-making and game logic implementation.

• Host: GitHub
• URL: https://github.com/savinrazvan/tictactoe
• Owner: SavinRazvan
• License: mit
• Created: 2024-07-28T15:01:15.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-07-28T23:38:31.000Z (10 months ago)
• Last Synced: 2025-01-10T02:32:51.812Z (5 months ago)
• Topics: ai, algorithmic-game-theory, board-games, educational-project, game-ai, game-development, minimax-algorithm, optimal-decision-making, pygame, python, tic-tac-toe, unbeatable-ai
• Language: Python
• Homepage:
• Size: 114 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# Tic-Tac-Toe AI Project

## Project Description

This project involves the creation of an AI that plays Tic-Tac-Toe using the Minimax algorithm. The AI is designed to ensure that it either wins or draws every game, demonstrating optimal decision-making throughout the gameplay.

## Objectives

- **Implement Game Logic:** Develop functions to manage the game state, determine possible actions, and apply those actions.

- **Optimal Decision Making:** Use the Minimax algorithm to compute the best possible move for the AI.

- **End Game Conditions:** Implement checks to determine when the game has ended and the winner, if any.

## Key Components

1. **player(board):** Identifies the current player (X or O).

2. **actions(board):** Lists all possible moves on the board.

3. **result(board, action):** Returns the new board state after a move.

4. **winner(board):** Determines if there's a winner.

5. **terminal(board):** Checks if the game is over.

6. **utility(board):** Assigns a value to the final board state (+1 for X win, -1 for O win, 0 for a tie).

7. **minimax(board):** Computes the optimal move for the current player.

## Installation and Usage

1. **Setup:**

   - Ensure you have Python installed.

   - Download and unzip the project files.

   - Install required dependencies: `pip3 install -r requirements.txt`.

2. **Run the Game:**

   - Start the game using the command: `python runner.py`.

## How It Works

- The game uses the Pygame library to provide a graphical interface.

- Players can choose to play as X or O.

- The AI calculates the best possible move using the Minimax algorithm, ensuring it plays optimally.

- The game continues until there's a winner or a tie, with the AI always making the optimal move.

## AI Implementation Details

- **Minimax Algorithm:** The AI uses the Minimax algorithm to evaluate all possible moves and select the one that maximizes its chances of winning or minimizing its chances of losing.

- **Game State Management:** Functions are implemented to manage the game state, check for winners, and determine possible actions.

- **Optimal Play:** The AI ensures that it never loses by playing the optimal move at every turn.

## Conclusion

This project demonstrates a robust implementation of an unbeatable Tic-Tac-Toe AI, showcasing key AI and algorithmic principles. The use of the Minimax algorithm ensures that the AI always plays optimally, providing a challenging opponent for any player.