I started fine with the priority queue and g/h/f setup, but fumbled a bit when they asked me to justify why Manhattan distance is admissible for 4-connected movement specifically.
Start by clarifying the problem constraints (grid size, movement allowed, heuristic choice) and then outline the A* algorithm: maintain open and closed sets, use a priority queue ordered by f = g + h, and reconstruct the path via parent pointers. Emphasize the importance of an admissible heuristic (e.g., Manhattan distance for 4-directional movement) and discuss trade-offs like memory usage and performance.
Pro tip: Mention that for large grids, using a binary heap for the open set and a hash set for the closed set optimizes performance, and consider bidirectional A* if the search space is huge. Also, note that A* is optimal only if the heuristic is admissible and consistent.
Ask about grid size, movement directions (4 or 8), whether diagonal moves have different costs, and if the heuristic must be admissible. Confirm the output format (list of cells or null).
Choose a priority queue (min-heap) for the open set, a set for closed nodes, and a dictionary for g-scores and parent pointers. Select an appropriate heuristic (e.g., Manhattan distance for 4-directional, Euclidean or Chebyshev for 8-directional).
While the open set is not empty, pop the node with lowest f-score. If it's the goal, reconstruct and return the path. Otherwise, for each valid neighbor, compute tentative g-score and update if better than existing.
If the open set empties without reaching the goal, return no path. Reconstruct the path by following parent pointers from the goal back to the start, then reverse it.
Discuss time and space complexity (O(b^d) worst-case, but often much better with a good heuristic). Mention alternatives like BFS for unweighted grids or Dijkstra's algorithm, and when A* is preferable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.