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. Discuss trade-offs such as heuristic admissibility, memory usage, and handling of no-path cases.
Pro tip: Mention that you would use a consistent heuristic (e.g., Manhattan distance for 4-directional movement) to guarantee optimality and avoid reopening nodes, and that you would test with edge cases like start == target and unreachable target.
Ask about grid dimensions, allowed moves (4 or 8 directions), whether diagonal moves have cost sqrt(2), and if the grid can be modified. This ensures you design the correct solution.
Use a priority queue for the open set, a 2D array for g-scores, and a parent map for path reconstruction. Select an admissible heuristic like Manhattan or Euclidean distance based on movement rules.
Initialize open set with start node, g(start)=0, f(start)=h(start). While open set not empty, pop node with lowest f; if it's the target, reconstruct path; otherwise, expand neighbors, update g and f if a better path is found, and push to open set.
If open set empties without reaching target, return no path. Otherwise, backtrack from target using parent pointers to build the path as a list of coordinates.
Discuss time and space complexity (O(b^d) worst-case, but better with good heuristic), and compare A* to BFS/Dijkstra. Mention memory optimizations like using a binary heap or bidirectional search.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.