Springer Nature

Procedural Maze Generation with Considering Difficulty from Human Players' Perspectives

This research presents a novel method for procedural maze generation that incorporates human behavioral tendencies to calibrate difficulty levels.

• Researchers successfully calibrated procedural maze difficulty by training a LightGBM model on human navigation patterns, specifically focusing on path selection at branch points.
• The model was developed using data from 20 participants navigating 21 mazes, analyzing 23 distinct features such as distance to the goal and straight-line movement tendencies.
• Difficulty is quantified by the number of extra steps a player takes beyond the shortest possible path, allowing for the categorization of mazes into easy, moderate, and difficult tiers.