MazeSolver

Technologies Used: Python, NumPy, Matplotlib, Prolog

Overview

MazeSolver is an AI-based project focused on generating randomized mazes and solving them using various search algorithms. The project demonstrates the implementation and comparison of multiple pathfinding algorithms including Breadth-First Search (BFS), Depth-First Search (DFS), Greedy Best-First Search, and A* Search.

Features

• Maze Generation: Utilizes a randomized depth-first search algorithm to generate dynamic mazes.
• Pathfinding Algorithms: Implements BFS, DFS, Greedy Best-First Search, and A* Search to solve the mazes.
• Visualization: Uses Matplotlib to visualize the paths found by each algorithm.
• Performance Analysis: Compares the execution times of different algorithms and visualizes the results with bar charts.
• Prolog Integration: Includes logic reasoning with Prolog to define and query node adjacency relationships (currently in prototype phase).

Usage

1. Run the main script to generate and solve a maze:

   mazesolver.py

2. Adjust the maze dimensions and algorithm selection through the user interface or by modifying the script parameters.

Files

• mazesolver.py: Main script to generate and solve mazes.
• projectreport.pdf: Detailed project report describing the methodology, algorithms, and results.

Contributions

Contributions are welcome! Please create a pull request or submit issues for any bugs or feature requests.