TODO

1. Implement All Search Algorithms

Algorithm	Progress
Breadth-First Search (BFS)	✅
Depth-First Search (DFS)	✅
Uniform Cost Search (UCS)	✅
A Search (A*)	✅

2. Search

• Search: Use a closed set to track visited nodes and avoid revisiting. ✅
• If a node is already visited, check if a faster path to the node has been found and update accordingly.
• Record the following for each version:
  • Path found ✅
  • Time elapsed ✅
  • Path cost ✅

3. Path Visualisation ✅

• Search exploration (e.g., paths explored).
• The final path found (different colors for clarity).
• e.g.:
  • Yellow lines: Path exploration history.
  • Red: Start state.
  • Magenta: Goal state.
  • Use Matplotlib to display results on the grid.

4. Compare Algorithms

• Run several searches (without visualisation) and log: the time ✅ and memory ✅ used.
• Graph the time and space complexity of the algorithms ✅
• Create a comparison table with the following metrics:
  • Time taken to compute the path. ✅
  • Path length (number of moves). ✅
  • Path cost. ✅
  • Memory/storage usage. ✅

Algorithm	Time (ms)	Nodes Visited	Path Length (m)	Final Path Cost (H:m:s)	Memory Use (KB)
BFS	95.604	31,935	3,307	00:05:38.446	4,111.6
DFS	2.23	4848	93,369	02:26:55.137	925.2
UCS	17.99	11,528	3,421	00:04:59.311	3,235.2
A*	6.317	6161	3,421	00:04:59.311	2,635.6

5. Code Formatting and Structure

• Fully comment and document the code. ✅
• Describe the function of the algorithms in docstrings. ✅
• Make the visuals pretty. ✅
• Write a proper README.