Researchers have developed a machine learning model to predict monster levels in tabletop role-playing games, specifically for Pathfinder Second Edition. This approach frames the task as tabular ordinal regression, utilizing a novel dataset derived from the game's publicly available monster attributes. Tree-based ensemble models demonstrated superior performance over linear models and neural networks, achieving high accuracy in predicting monster power levels and aligning with human intuition through explainable AI analyses. AI
IMPACT This research demonstrates how machine learning can be applied to game design, potentially streamlining the creation of balanced game content.
RANK_REASON The item is an academic paper detailing a novel application of machine learning to a specific problem domain. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- IArxiv
- machine learning
- Pathfinder Ai
- Pathfinder Second Edition
- ScienceCast
- tabular ordinal regression
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