This repository contains a Python implementation of the Othello (Reversi) game, including an AI opponent that uses the Minimax algorithm with Alpha-Beta pruning and custom heuristics for different phases of the game.

Prerequisites: Python 3.x Application Installation: Clone the repository:

git clone https://github.com/GuillaumeDorschner/Othello-ESILV.git

Navigate to the project directory:

cd Othello-ESILV

To start the game, simply run the main.py file:

python main.py

At the beginning of the game, the player can choose to play as Black or White. Black always plays first. The chessboard will be displayed in the command line, with B (black), W (white). During their turn, the player must enter the row and column of the desired move. The AI then makes its move, and the updated board is displayed. The game continues until there are no more legal moves for either player or the board is full. The player with the most pieces of their color on the board wins.

• Textual user interface
• Human versus AI gameplay
• Minimax algorithm with Alpha-Beta pruning for AI decision-making
• Custom heuristics for the early, middle, and endgame phases
• Ability to display available moves for the current player

The AI uses a Minimax algorithm with Alpha-Beta pruning to search the game tree and decide on the best move. The evaluation function considers different factors depending on the current game phase (beginning, middle, or end):

Beginning (first 12 moves):

Priority on placing pieces away from the center Priority on capturing pieces Middle (next 36 moves):

Priority on edge and corner positions Priority on capturing pieces Favor moves that allow future mobility End (last 12 moves):

Priority on capturing pieces Priority on important corners These heuristics help the AI adapt its strategy to the evolving game state and create a more challenging opponent.

This project is under the MIT license. See the LICENSE file for more details.