BFS is the obvious move here and I went straight to it, which was fine.
Model the grid as a graph where each cell is a node connected to its 4-directional neighbors, then use BFS to find the shortest path from start to end. BFS guarantees the shortest path in an unweighted graph, and you should handle edge cases like blocked start/end or out-of-bounds coordinates.
Pro tip: Mention that BFS is optimal for unweighted grids, but if the grid were weighted, Dijkstra's algorithm would be needed. Also, discuss how this problem relates to pathfinding in reinforcement learning environments, showing your ML engineering perspective.
Confirm grid dimensions, movement rules, and what constitutes a valid path. Ask about edge cases: start or end blocked, start equals end, no path exists, and grid boundaries.
Select BFS because it finds the shortest path in an unweighted graph. Explain why DFS or Dijkstra would be less efficient or unnecessary here.
Describe using a queue to explore level by level, a visited set to avoid cycles, and tracking distance. Mention early termination when the target is reached.
State time and space complexity: O(m*n) for both, as each cell is visited once. Discuss potential optimizations like bidirectional BFS or A* if heuristics are available.
Relate the problem to real-world ML applications like robot navigation, game AI, or reinforcement learning path planning, highlighting scalability and efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.