Application of machine learning to monster level prediction in tabletop RPG game design
Designing balanced adversaries is a central but labor-intensive task in tabletop role-playing game (TTRPG) development. In systems such as Pathfinder, each monster is described by many numerical attributes that jointly determine its power, summarized as an ordinal level. We investigate whether machine learning can support designers by predicting this level from a monster's attributes, framing the task as tabular ordinal regression. We introduce what is, to our knowledge, the first dataset built specifically for TTRPG monster-level prediction, derived from publicly available Pathfinder Second Edition data. Using it, we compare classical regression models with rounding schemes, dedicated ta...
| Authors | Jolanta Śliwa, Jakub Adamczyk |
|---|---|
| Primary category | cs.LG |
| Published | 10 July 2026 |
| Last updated at source | 24 August 2026 |
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