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.
- 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.
Make sure you have Python >= 3.12 installed.
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Clone the repository:
git clone https://github.com/Ant0in/Projet2-IA.git
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Navigate to the project directory:
cd Projet2-IA/ -
Install dependencies:
pip install -r "requirements.txt" -
Run the project on a grid
map1.txt:python ".\main.py" ".\tests\map1.txt" --algo "minimax" --maxdepth 5
To run the tests using Pytest (verbose):
pytest .\tests\ -vvvThis project is licensed under the MIT License. You are free to use, modify, and distribute this software.
This project was developed for the Artificial Intelligence course INFO-F311. Special thanks to Tom Lenaerts & Yannick Molinghen (ULB) for their guidance and support.