renato1478/intelligentpathfinding

A repository containing comprehensive implementations of different search algorithms, with specific attention to A-star, for educational and practical purposes.

Intelligent Pathfinding

This repository showcases various search algorithms implemented in Python, with a specific focus on A-star algorithm, a powerful heuristic-based search algorithm. These algorithms are designed to efficiently explore problem spaces, find optimal solutions, and navigate complex environments.

Screenshots

BFS Progress Example	A* Result	DFS Result

Algorithms Included

• Breadth-First Search (BFS): Explores a graph or grid by expanding the nodes in a level-by-level fashion, ensuring that all nodes at a certain depth are visited before moving deeper. It uses a FIFO queue and guarantees the shortest path when all step costs are equal.
• Depth-First Search (DFS): Explores a graph or grid by going as deep as possible along each branch before backtracking. It uses a LIFO stack and may not guarantee the shortest path.
• Uniform Cost Search (UCS): Explores a graph or grid by prioritizing nodes with the lowest path cost. It uses a priority queue and guarantees the optimal path regardless of step costs.
• A-star (A*): A heuristic-based algorithm that intelligently searches through a graph or grid by evaluating the cost of reaching a goal from a given node. It combines the use of a heuristic function and a cost function to guide the search towards the most promising paths.

Features

• Implementation of A-star algorithm in Python, featuring customizable heuristics and cost functions.
• Support for different problem domains, including pathfinding, optimization, and problem-solving.
• Visualization capabilities to observe the search process and the discovered paths.
• Modularity and extensibility, allowing easy integration with other search algorithms and problem-specific extensions.

Getting Started

1. Clone the repository:

git clone https://github.com/your-username/search-algorithms-implementation.git

2. Navigate to the project directory:

cd search-algorithms-implementation

3. Install the required dependencies:

pip install opencv

4. Explore the available examples and problem domains in the examples directory.

5. Run the desired Python scripts to see the algorithms in action and observe the search process and results.

Contributions

Contributions to this project are welcome! If you have any ideas, improvements, or bug fixes, feel free to submit a pull request. Please ensure that your contributions align with the project's goals and follow best coding practices.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute the code for personal and commercial purposes.

Acknowledgements

This project was inspired by the field of artificial intelligence and the need for efficient search algorithms in solving complex problems. Special thanks to the open-source community for providing valuable resources and inspiration.