Game AI Interview Prep: Common Questions u0026 Scenarios Quiz Quiz

Sharpen your Game AI interview skills with this quiz covering core algorithms, decision-making strategies, behavior modeling, and real-world development scenarios. Perfect for those preparing for Game AI roles, this set challenges your knowledge of fundamental and practical concepts in designing intelligent game behaviors.

1. Pathfinding in Games

   In a grid-based tactical game, which pathfinding algorithm is most efficient for finding the shortest path around static obstacles from one point to another?

   1. Q-tree search
   2. Backpropagation
   3. A* (A-star) algorithm
   4. Bayesian inference

   Explanation: A* (A-star) algorithm is widely used for shortest path finding in grid-based environments due to its efficiency and ability to handle dynamic or static obstacles. Backpropagation is related to neural networks and is not used for pathfinding. Bayesian inference deals with probability and inference, which does not apply to this scenario. Q-tree search is not a standard or recognized pathfinding algorithm.

2. Behavior Trees in Game AI

   Which primary advantage do behavior trees offer over finite state machines when controlling non-player character (NPC) behavior in a stealth game?

   1. They provide more modular and scalable behavior structures
   2. They are faster at rendering graphics
   3. They require less memory than finite state machines
   4. They guarantee optimal pathfinding

   Explanation: Behavior trees are favored in AI because they allow for modular, hierarchical, and reusable behavior design, making it easier to manage complex NPC behaviors. Although behavior trees can be memory efficient, saying they always require less memory is inaccurate. Rendering graphics and performing optimal pathfinding are not direct advantages of behavior trees, as these concern separate game systems.

3. Flocking Algorithms

   When simulating a flock of birds in a 3D space, which rule ensures that each bird keeps an appropriate distance from others to prevent collisions?

   1. Boiling
   2. Attraction
   3. Separation
   4. Alignment

   Explanation: Separation is the rule within flocking algorithms that helps each agent avoid crowding and potential collisions by steering away from nearby flockmates. Alignment refers to matching velocity with neighbors, not collision avoidance. Boiling is unrelated to flock simulation and is likely a typo or distractor. Attraction is a general term, but in flocking, 'separation' specifically addresses collision avoidance.

4. Utility-Based AI

   In a real-time strategy game, what is the main purpose of using utility-based AI to select an agent’s action?

   1. To execute a fixed action sequence regardless of situation
   2. To render agent models smoothly
   3. To prioritize graphics over logic
   4. To dynamically evaluate and choose actions based on current game context

   Explanation: Utility-based AI systems score possible actions using context-dependent factors, enabling adaptive and intelligent agent behavior. Executing fixed action sequences ignores context, which utility systems are designed to avoid. Rendering agent models smoothly and prioritizing graphics are unrelated to AI decision-making and instead pertain to visual aspects of game development.

5. Debugging AI Behavior

   During playtesting, a patrol guard AI suddenly stops responding after reaching a corner waypoint. Which is the most likely reason for this issue?

   1. The game uses turn-based combat
   2. The state transition conditions are not being met
   3. The AI script uses a greedy algorithm
   4. The model textures are too high resolution

   Explanation: If an AI stops responding at a waypoint, it is often due to state transition conditions not being fulfilled, causing the behavior system to stall. High-resolution textures would affect visuals but not core AI behavior. Using turn-based combat does not explain a single agent stalling at a waypoint. A greedy algorithm is related to decision-making but would not typically result in an AI becoming unresponsive at a specific location without other logic errors.