This repository focuses on implementing and exploring fundamental search algorithms used in Artificial Intelligence (AI). These algorithms are designed to solve a variety of search problems efficiently and are foundational to many AI systems. Inspired by the "Pacman AI" educational projects from UC Berkeley, this project offers a hands-on approach to learning and experimenting with AI search techniques.
This project includes the implementation of the following search algorithms:
- Depth-First Search (DFS): Explores as far as possible along each branch before backtracking.
- Breadth-First Search (BFS): Explores all neighbors of a node before moving deeper.
- Uniform Cost Search (UCS): Finds the least-cost path by exploring nodes in increasing cost order.
- A Search:* Combines path cost and heuristic estimates to find optimal paths efficiently.
Each algorithm is applied to structured environments to demonstrate its strengths and limitations.
- Educational Purpose: This project is intended for learning and teaching AI concepts, particularly search techniques. It includes practical examples and problem-solving exercises to reinforce theoretical knowledge.
- Customizable Testing: Users can define their own search problems and test the algorithms under various scenarios.
- Modular Design: The project is structured to allow easy integration and extension for other AI-related projects.