Adversarial Search - 2024

Description

This project focuses on implementing adversarial search algorithms, including Minimax, Alpha-Beta Pruning, and Expectimax, to create an agent that competes against another in a map-based game environment. The objective is for the agent to outmaneuver its opponent by collecting gems and finding the exit first. For further details, refer to ./pdf/consignes.pdf.

Instance of the Aversarial Search Problem, where agent must collect gems and find the exit to beat its opponent.

Features

• Algorithmic graph search approach: Transforms the problem into a graph, with nodes representing "world states", and navigates through them using Minimax, Alpha-Beta Pruning, and Expectimax.
• Visualization: A visual demonstration of the search algorithms, where the agent moves according to the solution found by the algorithm.

Usage

Make sure you have Python >= 3.12 installed.

Running the Project

1. Clone the repository:

   git clone https://github.com/Ant0in/Projet2-IA.git

2. Navigate to the project directory:

   cd Projet2-IA/

3. Install dependencies:

   pip install -r "requirements.txt"

4. Run the project on a grid map1.txt:

   python ".\main.py" ".\tests\map1.txt" --algo "minimax" --maxdepth 5

Running Tests

To run the tests using Pytest (verbose):

pytest .\tests\ -vvv

License

This project is licensed under the MIT License. You are free to use, modify, and distribute this software.

Acknowledgements

This project was developed for the Artificial Intelligence course INFO-F311. Special thanks to Tom Lenaerts & Yannick Molinghen (ULB) for their guidance and support.