60 minutes felt fine until I got to the winner prediction part and realized I'd painted myself into a corner with my class structure.
Start by clarifying requirements and defining the game's core abstractions (state, moves, rules) before diving into implementation. Propose a modular design with clear interfaces for extensibility, then discuss algorithms for move validation, win detection, and winner prediction (e.g., minimax with memoization). Balance trade-offs between simplicity and generality, and be ready to code key components.
Pro tip: Demonstrate extensibility by designing a generic game framework (e.g., using the Strategy pattern for rules) and mention how to optimize winner prediction with alpha-beta pruning or transposition tables. Also, proactively discuss testing and edge cases to show production-level thinking.
Ask questions to understand constraints: board size, game rules, performance needs, and extensibility goals. Confirm whether the simulation should support AI players or just human moves.
Outline classes/interfaces for GameState, Move, Player, and GameRules. Emphasize separation of concerns and how new games can be added by implementing these interfaces.
Describe how to represent the board (e.g., 2D array), track turns, and validate moves (e.g., check occupancy, bounds, and rule-specific constraints). Mention immutable state for easier prediction.
Explain algorithms to check for winning conditions (e.g., lines, diagonals) and draw (full board). Discuss efficiency and how to generalize for different win patterns.
Describe using minimax with alpha-beta pruning for perfect play, and memoization for performance. Discuss how the design supports other games (e.g., Connect Four) by swapping rule implementations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.