AI in Game Level Generation Quiz

Answers and explanations appear as you take the quiz.

1. What does 'procedural content generation' (PCG) mean in games?
   • Algorithms automatically creating levels, maps, or content
   • Players manually building every level by hand
   • Recording real-world locations into the game
   • A type of multiplayer matchmaking
2. How does the Wave Function Collapse algorithm build a map?
   • It copies an existing map exactly
   • It collapses tiles one at a time while enforcing adjacency rules, like solving a puzzle
   • It randomly throws tiles down with no rules
   • It asks a human to place each tile
3. Why do developers test generated levels with search agents or other playability checks?
   • To guarantee every generated level is completable before release
   • To remove the need for human designers
   • To test whether levels meet defined reachability or difficulty criteria; this does not guarantee universal playability
   • To increase the graphics resolution of the level
4. Which classic noise function is widely used to generate natural-looking terrain?
   • White noise only
   • A sorting algorithm
   • A hash table
   • Perlin noise
5. What does PCGML add compared to older rule-based generation?
   • It learns patterns from existing levels using machine learning
   • It removes randomness entirely
   • It only works for 2D platformers
   • It requires no computer at all
6. Roughly how many planets does No Man's Sky claim to procedurally generate?
   • About one thousand
   • About 18 quintillion
   • About one million
   • Exactly one hundred
7. In the Mario-level GAN study, how did researchers test whether generated levels were playable?
   • They checked only whether the level image looked realistic
   • They counted the number of colors in the level
   • They ran an A* agent through the levels and measured whether it could complete them
   • They asked the GAN to classify its own training data
8. What is the goal of a quality-diversity method such as MAP-Elites in level generation?
   • To make every generated level identical to one template
   • To maximize image resolution without considering gameplay
   • To remove all difficult levels from a training dataset
   • To find high-quality levels that also vary across selected gameplay characteristics