Add A* (A-Star) Pathfinding Algorithm Implementation

[vivekkumarrathour]

Feature description

Feature Description

The repository currently contains various graph traversal algorithms (BFS, DFS, Dijkstra) but lacks the A* (A-Star) pathfinding algorithm, which is one of the most widely used pathfinding algorithms in computer science, game development, robotics, and AI applications.

Proposed Implementation

I propose adding an A* pathfinding algorithm implementation in the graphs/ directory with the following features:

1. Core A algorithm* with configurable heuristic functions
2. Support for weighted graphs (grid-based and adjacency list representations)
3. Multiple heuristic functions:
   • Manhattan distance (for grid-based pathfinding)
   • Euclidean distance
   • Chebyshev distance
4. Comprehensive doctests demonstrating various use cases
5. Type hints for all function parameters and return values
6. Clear documentation with examples and complexity analysis

Why This Enhancement?

• A* is a fundamental algorithm taught in AI and algorithms courses
• It's widely used in real-world applications (GPS navigation, game AI, robotics)
• Complements existing graph algorithms in the repository
• Provides educational value for learners understanding informed search algorithms

Example Use Cases

• Grid-based pathfinding (game maps, mazes)
• Route planning in weighted graphs
• Robot navigation
• Comparison with uninformed search algorithms (BFS, DFS)

Benefits

• Educational resource for students learning AI and graph algorithms
• Practical implementation that can be used in real projects
• Demonstrates the difference between informed and uninformed search strategies

I would be happy to implement this feature following the repository's contributing guidelines if approved.