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English(EN) Application of machine learning to monster level prediction in tabletop RPG game design

机器学习助力桌面RPG怪物平衡

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种机器学习模型来预测桌面RPG中的怪物等级，具体使用了《Pathfinder Second Edition》的数据。该方法将任务构建为表格序数回归，旨在帮助游戏设计师更有效地创建平衡的对手。研究发现，基于树的集成模型表现最佳，能很好地近似人类设计师的判断并符合游戏规则，表明机器学习可以成为TTRPG设计的宝贵工具。 AI

影响 这项研究展示了机器学习如何自动化和改进创意设计过程的各个方面，有可能加快游戏开发速度。

排序理由 该集群包含一篇学术论文，详细介绍了机器学习在特定领域（游戏设计）的新颖应用。

在 arXiv cs.LG 阅读 →

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机器学习助力桌面RPG怪物平衡

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该集群包含一篇学术论文，详细介绍了机器学习在特定领域（游戏设计）的新颖应用。
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报道来源 [2]

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

    机器学习在桌面角色扮演游戏设计中怪物等级预测的应用

    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 ·

    机器学习在桌面角色扮演游戏设计中怪物等级预测的应用

    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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