This project implements a Bayesian Network with a focus on inference by enumeration to accurately estimate gem positions within a grid. A noisy sonar sensor provides positional data, which the network uses to make these estimations. For more details, please refer to ./pdf/consignes.pdf.
Instance of the Bayesian Network figuring the gem positions withing a 10x10 grid.
- Bayesian Network Implementation: Solves the problem using a graph-based structure called a "Bayesian Network."
- Visualization: Provides a visual demonstration of the inference-by-enumeration algorithm, showing how the agent estimates gem positions based on Bayesian Network predictions.
Make sure you have Python >= 3.11 installed.
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Clone the repository:
git clone https://github.com/Ant0in/Projet3-IA.git
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Navigate to the project directory:
cd Projet3-IA/ -
Install dependencies:
pip install -r "requirements.txt" -
Run the project:
python ".\main.py" --grid_size 10 --n_gems 3 --moves D R R R R D R D D --gems_positions "(5,2) (0,7) (8,8)"
This 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 & Pascal Tribel (ULB) for their guidance and support.