About the talk
This talk presents a practical, explainable framework that bridges the gap between raw AI pathfinding metrics and human-centered game design. Using Sokoban as a case study, we show how unsupervised clustering and visual analytics can decode difficulty, uncover hidden structural archetypes, and guide adaptive solver selection. Our results demonstrate how obstacle density, deadlock potential, and other features drive both algorithmic and human difficulty, enabling designers to make faster, more informed decisions about procedural content.
Takeaway
– How to translate opaque AI solver metrics into designer – relevant difficulty insights.
– Evidence – based design heuristics: why obstacle density and deadlock potential are critical levers in puzzle complexity.
– How clustering reveals hidden level archetypes and balances procedural content.
– Practical workflow for integrating explainable AI tools into game design pipelines.
– The value of adaptive solver selection for efficiency and player experience.
Experience level needed: Intermediate