Artificial Intelligence & Constrained Optimization

This repository contains a collection of exercises and projects developed for the Artificial Intelligence and Constrained Optimization course. The goal is to provide clean, documented, and efficient implementations of core AI search and optimization algorithms.

Key Features

The repository is organized into thematic modules:

• State-Space Search: Implementation of Informed and Uninformed search algorithms (A*, BFS, DFS, and Uniform Cost Search).
• Constraint Satisfaction Problems (CSP): Solvers for N-Queens, Map Coloring, and Sudoku using Forward Checking and Arc Consistency (AC-3).
• Combinatorial Optimization: Local search heuristics, Hill Climbing, and [add other algorithms like Genetic Algorithms if applicable].
• Exam Lab Scenarios: Solutions to past exam papers and complex problem-solving scenarios.

Tech Stack

• Language: Python 3.x
• Libraries: numpy, scipy, python-constraint (modify according to your tools)
• Environment: Jupyter Notebooks / CLI Scripts

Installation & Usage

1. Clone the repository:

   git clone [https://github.com/Cantellos/IA-Ottimizzazione-Vincolata.git](https://github.com/Cantellos/IA-Ottimizzazione-Vincolata.git)

2. Install dependencies:

   pip install -r requirements.txt

3. Run an example:

   python src/search_example.py

Project Structure

├── src/                # Core algorithm implementations
├── notebooks/          # Interactive walkthroughs and tutorials
├── docs/               # Theoretical notes and exercise descriptions
└── tests/              # Unit tests for algorithm verification