The core design part was fine, Team, Monster, Outcome classes aren't that hard to sketch out.
Start by clarifying requirements and defining core classes like Monster, Team, and Battle. Then design the battle logic to simulate turn-based fights in order, tracking health and logging events. Finally, discuss extensibility and edge cases.
Pro tip: Emphasize separation of concerns: keep battle logic separate from monster data, and use interfaces for abilities to allow easy extension. This shows you think about maintainability and scalability.
Ask about monster attributes, turn order, win conditions, and log format. Confirm if monsters can have special abilities or if it's a simple attack exchange.
Design Monster with health, attack, and possibly defense. Team holds a list of monsters. Battle orchestrates the fight and produces a log.
Simulate turns: each monster from team A attacks corresponding monster in team B in order, then vice versa. Track health and remove defeated monsters. Continue until one team has no monsters left.
Record each attack, damage dealt, remaining health, and monster defeats. Ensure log is readable and includes turn order.
Consider adding abilities, different attack strategies, or multiple monster types. Handle ties, simultaneous defeats, and empty teams.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a pretty natural extension once the base was working.
Start by clarifying the requirements and constraints of the simulation, then design a flexible type effectiveness system that can be easily extended. Discuss trade-offs between different data representations and algorithms, and outline how to integrate the system with minimal disruption to existing code.
Pro tip: Emphasize the importance of separating data from logic: store type matchups in a configuration file or database so that game designers can update them without code changes. This demonstrates foresight and maintainability.
Ask questions to understand the scope: How many types? Are matchups symmetric? Should effectiveness multipliers be configurable? What are performance constraints?
Choose a representation for type matchups, such as a 2D matrix or a map of type pairs to multipliers. Consider using an enum for types and a lookup table for effectiveness.
Modify the damage calculation to incorporate type effectiveness. Ensure the system is decoupled from specific monsters and types for extensibility.
Address cases like dual-type monsters, immunity (0x damage), and future additions of new types. Discuss how to update the system without breaking existing code.
Compare performance of different data structures (e.g., matrix vs. hash map). Outline a testing strategy to verify correctness of matchups and damage calculations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the requirements: how attacks are defined, how damage is calculated, and what 'most damage' means (e.g., considering resistances). Then, design a data structure to store multiple attacks per monster and implement a selection algorithm that evaluates each attack against the current opponent, picking the one with maximum damage. Finally, discuss trade-offs like time complexity and potential optimizations.
Pro tip: Mention that you would encapsulate the damage calculation and attack selection logic to keep it maintainable and testable, and consider edge cases like ties or no valid attacks.
Ask questions to understand the attack model: are attacks fixed damage or variable? Do they have types, cooldowns, or costs? How is 'most damage' determined (e.g., after applying resistances)?
Represent each monster with a list of attacks, where each attack has properties like damage, type, etc. Ensure the opponent's defensive attributes are accessible for damage calculation.
Create a function that computes the effective damage of an attack against a given opponent, accounting for any modifiers such as resistances or vulnerabilities.
Iterate through the monster's attacks, compute effective damage for each, and select the attack with the highest damage. Handle ties (e.g., pick first or random) and cases with no attacks.
Analyze time complexity (O(n) per selection) and suggest optimizations if needed, such as caching or pre-sorting attacks if opponent defenses are static. Discuss maintainability and extensibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.