The questions matched stuff that had already been posted publicly so I fed them into an AI tool and got through the first two parts almost instantly.
Start by clarifying the maze representation, movement rules, and what each part asks (e.g., reachability, shortest path, all paths, optimal path with constraints). Then choose the appropriate algorithm for each part, typically BFS for shortest path, DFS for exhaustive search, and A* or Dijkstra for weighted or heuristic-based scenarios. Implement modularly, test with edge cases, and analyze time/space complexity.
Pro tip: Demonstrate engineering maturity by discussing trade-offs between algorithms (e.g., BFS vs DFS memory usage) and by writing clean, modular code that can be easily extended for each part. Also, proactively mention potential optimizations like bidirectional BFS or early termination.
Ask about maze size, movement directions (4 or 8), obstacles, start/end points, and whether the maze is static. Confirm what each part requires: e.g., part 1 might be simple reachability, part 2 shortest path, part 3 all paths, part 4 path with keys/doors or weighted cells.
For reachability or shortest path in unweighted grid, use BFS. For all paths or backtracking, use DFS with recursion. For weighted grids, use Dijkstra or A*. For multi-part, reuse code by abstracting common operations like neighbor generation.
Write a function for each part, starting with the simplest. Test with small cases, including edge cases like no path, start=end, or large mazes. Ensure visited tracking is correct to avoid infinite loops.
State time and space complexity for each solution (e.g., O(R*C) for BFS). Discuss potential optimizations like bidirectional search, heuristic functions, or pruning for DFS.
If time permits, mention how to handle dynamic obstacles, multiple agents, or memory constraints. Compare BFS vs DFS for different scenarios and justify your choices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.