/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.

Primary LanguagePythonMIT LicenseMIT

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.