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Machine learning predicts monster levels in tabletop RPGs

By PulseAugur Editorial · [2 sources] ·

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]

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Machine learning predicts monster levels in tabletop RPGs

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jolanta \'Sliwa, Jakub Adamczyk ·

    Application of machine learning to monster level prediction in tabletop RPG game design

    arXiv:2607.09196v1 Announce Type: new Abstract: 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…

  2. arXiv cs.LG TIER_1 English(EN) · Jakub Adamczyk ·

    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 inves…

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