This project is an interactive Othello game developed in Python, featuring both graphical and console-based user interfaces. It supports gameplay modes for human vs. human, human vs. AI, and AI vs. AI, showcasing a blend of GUI design, game logic, and AI implementation.
- Dual Interface Options: Play the game using either a graphical interface with Tkinter or a console-based interface.
- AI Opponents: Challenge AI players with varying difficulty levels (Easy, Medium, Hard, Very Hard) implemented using the Minimax algorithm with Alpha-Beta pruning.
- Interactive Gameplay: GUI highlights valid moves, provides real-time score updates, and announces the winner at the end of the game.
- Error Handling: Ensures smooth gameplay by handling invalid moves and situations with no valid moves gracefully.
- Start the game by running the script.
- The GUI window will display the game board.
- Players take turns to click on valid board positions to place their pieces.
- The game will highlight valid moves and update the board accordingly.
- The scores and the current player's turn are displayed in the window title.
- If playing in console mode, follow the on-screen prompts to enter moves.
- Players take turns to input their move coordinates in the format
X Y. - The console will display the current board state, valid moves, and scores.
Colors: Enum for representing the state of each board cell (White, Black, Empty).AIDifficulty: Enum for representing AI difficulty levels.
Board: Handles the game board, move validation, and game logic.BoardView: Abstract base class for different board views (GUI, Console).BoardViewConsole: Implementation of the console-based interface.BoardViewGUI: Implementation of the graphical interface using Tkinter.Game: Manages the overall game flow, switching turns between players and checking for game end conditions.Player: Abstract base class for different player types (Human, AI).HumanPlayer: Handles input for human players.AIPlayer: Implements AI decision-making using the Minimax algorithm.MiniMaxUtility: Utility class for the Minimax algorithm with Alpha-Beta pruning.