AI Search Algorithm Implementations

This repository contains implementations of common Artificial Intelligence search algorithms using Python.
The project is designed for learning, practice, and academic purposes.

📌 Features

• Implementation of classic AI search algorithms
• Easy-to-understand Python code
• Beginner-friendly project structure
• Suitable for AI and algorithm learning

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🚀 Technologies Used

• Python 3
• Git & GitHub
• VS Code

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📂 Project Structure

ai-search-algorithm-implementations/
│
├── README.md
├──documents.ipynb
├── graph.py
├── algorithms/
│   ├── bfs.ipynb
│   ├── dfs.ipynb
│   ├── astar.ipynb
│   └── greaddy.ipynb
└── examples/

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🧠 Algorithms Included

• Breadth First Search (BFS)
• Depth First Search (DFS)
• Uniform Cost Search (UCS)
• A* Search Algorithm
• Greedy Best First Search
• Other AI search techniques

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⚙️ Installation

1 Clone the Repository

git clone https://github.com/YOUR_USERNAME/ai-search-algorithm-implementations.git

2 Navigate into the Project Folder

cd ai-search-algorithm-implementations

3 Install Dependencies

pip install -r requirements.txt

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▶️ Running the Project

Run the main Python file:

python main.py

Or run a specific algorithm file:

python bfs.py

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📖 Learning Objectives

This project helps students understand:

• Problem-solving techniques in AI
• Graph traversal algorithms
• Pathfinding methods
• Search optimization strategies
• Python programming practices

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💻 Recommended IDE

• Visual Studio Code
• PyCharm

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🤝 Contributing

Contributions are welcome.

1. Fork the repository
2. Create a new branch
3. Make improvements
4. Submit a pull request

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📜 License

This project is for educational purposes.

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👨‍💻 Author

Developed by Kura Lemma

GitHub: https://github.com/kura458