I started with the state representation and got a bit too deep into enum choices for direction before writing any actual logic.
Start by clarifying requirements and edge cases, then design a clean Robot class with clear separation of concerns (state, command parsing, execution). Implement the CLI loop and unit tests, emphasizing testability and extensibility. Discuss trade-offs and potential ML integrations to align with the role.
Pro tip: Demonstrate test-driven development by writing tests for edge cases (e.g., invalid PLACE, boundary moves) before implementation. Also, mention how you would structure the code to easily add new commands or adapt to different grid sizes, showing foresight for maintainability.
Ask questions to confirm grid size, command syntax, and behavior for invalid commands. Identify edge cases like placing outside grid, moving off edge, and initial state before PLACE.
Define a Robot class with attributes (x, y, facing) and methods for each command. Ensure methods handle boundary checks and ignore invalid moves. Consider using an enum for directions.
Create a loop that reads commands from stdin, parses them (e.g., PLACE X,Y,FACING), and invokes corresponding Robot methods. Handle invalid input gracefully.
Write tests covering all commands, edge cases (e.g., move at boundary, invalid place), and sequences. Use a testing framework like pytest or unittest.
Talk about design choices (e.g., immutable vs mutable state, command pattern) and how to extend for ML (e.g., logging states for RL, adding sensors).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easier than I expected once the base class was solid.
Start by clarifying the requirements: obstacle representation, collision semantics, and whether obstacles are static or dynamic. Then propose a clean design that separates obstacle management from movement logic, and discuss trade-offs between different data structures and collision detection strategies. Finally, outline how you would test and extend the solution.
Pro tip: Demonstrate foresight by discussing how your design would handle dynamic obstacles or multiple robots, and mention the importance of keeping the obstacle logic decoupled from the robot's movement to facilitate future extensions.
Ask about obstacle representation (e.g., list of coordinates, grid with flags), whether obstacles are static or dynamic, and if there are constraints on obstacle placement (e.g., not on robot's start).
Choose an efficient way to store obstacles, such as a set of coordinates for O(1) lookup, or a 2D boolean array if the grid is dense. Discuss memory vs. speed trade-offs.
Modify the robot's move method to check if the target cell contains an obstacle before moving. Ensure the check is centralized to avoid duplication.
Consider scenarios like obstacles at the grid boundary, robot starting on an obstacle (if allowed), and attempts to move into an obstacle (e.g., ignore move, raise error, or log).
Write unit tests for movement with obstacles, including cases where the robot is blocked, can navigate around, and when no obstacles are present.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started to feel the pressure.
Start by clarifying the requirements: number of robots, turn order, collision definition, and grid size. Then propose a design that extends the existing simulator with a turn manager and collision detection, discussing trade-offs between different collision resolution strategies. Finally, outline the algorithm and data structures needed, and consider edge cases and scalability.
Pro tip: Demonstrate awareness of real-world multi-agent systems by discussing how collision handling affects path planning and whether robots can communicate or need decentralized coordination. This shows you think beyond the immediate coding task.
Ask about the number of robots, turn order (e.g., round-robin), collision definition (same cell, swapping positions), and whether robots can occupy the same cell at different times. Confirm if the grid size is fixed or dynamic.
Introduce a turn manager that cycles through robots, allowing each to issue one command per turn. Ensure commands are validated against the current state before execution.
Before executing a move, check if the target cell is occupied by another robot. Also check for swap collisions where two robots exchange positions. Decide on a policy: reject the move, delay, or replan.
Use a dictionary mapping robot IDs to positions for O(1) collision checks. Maintain a set of occupied cells for quick lookup. Consider a priority queue if turn order is based on some priority.
Compare centralized vs decentralized collision avoidance. Discuss scalability with many robots, potential deadlocks, and how to handle simultaneous commands if turns are relaxed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.