发表机构
AGH（AGH）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在桌面角色扮演游戏开发中，通过构建数据集，将机器学习用于从怪物属性预测等级，经多种模型比较和评估，发现基于树的集成模型表现优，能近似设计师判断，可辅助怪物平衡及系统设计。
AI 中文摘要
在桌面角色扮演游戏（TTRPG）开发中，设计平衡的对手是一项核心但耗时的任务。在《开拓者》等系统中，每个怪物由许多数值属性描述，这些属性共同决定其能力，并总结为一个序数等级。我们研究机器学习能否通过从怪物属性预测该等级来支持设计师，将任务构建为表格序数回归。我们引入了据我们所知第一个专门为TTRPG怪物等级预测构建的数据集，该数据集源自公开可用的《开拓者》第二版数据。使用它，我们将经典回归模型与舍入方案、专用表格序数回归算法以及具有序数感知损失的神经网络进行比较。为反映实际设计工作流程，我们在时间顺序和扩展窗口协议下用几个互补指标评估所有模型。结果表明，基于树的集成模型优于线性模型和神经网络方法，实现了近乎完美的序数排名和高预测准确率。可解释人工智能分析表明该模型与人类直觉一致且遵循游戏规则中的模式。这些结果共同表明，机器学习可以可靠地近似设计师的判断，并作为怪物平衡和更广泛的TTRPG系统设计的有效计算机辅助工具。
英文摘要
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 tabular ordinal regression algorithms, and neural networks with ordinal-aware losses. To mirror real design workflows, we evaluate all models under chronological and expanding-window protocols with several complementary metrics. Results show that tree-based ensembles outperform linear models and neural approaches, achieving near-perfect ordinal ranking and high predictive accuracy. Explainable AI analyses, such as feature importance and error distributions, show that the model is aligned with human intuition and follows patterns grounded in game rules. Together, these results show that machine learning can reliably approximate designer judgments and serve as an effective computer-aided tool for monster balancing and broader TTRPG system design.
