arXiv cs.AI· ravyn Hardcastle, Matthew Guzdial·· 14 小时前AI 评分18
Mage-Hand：用 Foundry VTT 半随机模拟分析《龙与地下城》战斗遭遇
Dungeons & Dragons Encounter Analysis with Semi-Random Simulation in Foundry VTT
AI 导读
研究者开发了 Mage-Hand 工具，基于 Foundry Virtual Tabletop 对《龙与地下城》战斗遭遇进行半随机模拟。模拟胜率与 D&D 官方发布的难度评估指标高度相关，同时更贴合部分玩家报告的实际体验。该工具弥补了官方指标未考虑站位、地牢布局、怪物能力协同与队伍构成等缺陷。
正文 · 原文
Abstract:Dungeons & Dragons (D&D) is a tabletop roleplaying game (TTRPG) in which a group of players, guided by a Game Master, roleplay a fantasy adventure with simulated combat and improvisational narrative. In D&D, an encounter is a single instance of simulated combat, where the players must use their characters to attempt to defeat a selection of enemy characters that the GM controls. D&D has multiple systems that approximate encounter difficulty by summing individual difficulty values that designers predefine for each enemy. However, since players and enemies can have a wide variety of capabilities, encounter difficulty depends on more than just the estimated difficulty of each monster. For example, these metrics do not account for positioning and dungeon layout, the synergies between monster abilities, and player party composition. We have created a tool, Mage-Hand, that can simulate D&D encounters semi-randomly using Foundry Virtual Tabletop, a virtual tabletop (VTT) system. In this paper, we show that our tool's simulated winrates have a strong correlation with D&D's published difficulty evaluation metrics, while also better aligning with some players' reported experiences.
| Comments: | 10 pages, 6 figures, 22nd Artificial Intelligence and Interactive Digital Entertainment Conference |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.38975 [cs.AI] |
| (or arXiv:2610.38975v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38975 arXiv-issued DOI via DataCite |
Submission history
From: Matthew Guzdial [view email]
[v1]
Wed, 28 Oct 2026 22:00:55 UTC (11,650 KB)
来源：arXiv cs.AI · arxiv.org