Maze Pathfinding Algorithms

This project implements and visualizes various pathfinding algorithms in a maze environment. It includes two main Python scripts that demonstrate different approaches to maze solving.

Files

Forward chaining

Preview of forward_chaining_solver.py: This file implements a Forward Chaining approach to maze solving, demonstrating a rule-based system for pathfinding.

Graph based solver

Preview of maze_solver.py: This file contains the core implementation of the graph traversal algorithms, including Breadth-First Search (BFS) and A* Search.

maze_solver.py

This script includes:

• A Maze class for representing the maze structure
• Node and Step classes for managing maze elements and search steps
• SearchAlgorithm abstract base class
• Implementations of BFS and A* Search algorithms
• Visualization tools for displaying the maze and search progress
• Functions for generating random mazes

Key features:

• Interactive visualization with step-by-step playback
• Options to show/hide the final path, all steps, or current steps
• Heatmap visualization of visited nodes

forward_chaining_solver.py

This script focuses on a Forward Chaining approach to maze solving:

• Implements a knowledge-based system for maze navigation
• Uses a set of rules to make decisions about movement
• Visualizes the search process, including the agent's knowledge base
• Demonstrates fog-of-war mechanics, where the agent discovers the maze as it explores

Key features:

• Rule-based decision making
• Visualization of the agent's knowledge accumulation
• Step-by-step execution of the forward chaining process

Usage

To run either solver:

1. Ensure you have the required libraries installed (matplotlib, numpy)
2. Run the desired script:

Customization

Both scripts allow for customization of maze size and obstacle density. You can modify these parameters in the main execution block of each script.