用编码模型预测万智牌限包抽卡的牌组强度，为复杂卡牌游戏决策提供新方法。
Predicting Drafted Deck Strength for "Magic: the Gathering"
- 基于编码器生成上下文相关卡牌嵌入，捕捉抽卡序列中的协同效应。
- 在真实大规模数据上显著优于线性基线，首次建立万智牌限包结果预测基准。
- 适合对卡牌游戏策略建模、博弈决策研究者参考。
许多现实世界的游戏没有固定且紧凑的规则集：其动态由大量不断演化的游戏组件之间的交互定义，使得通用策略学习难以实现。万智牌（MTG）正是此类场景的典型代表，其中卡牌本身定义并改变游戏规则、战略约束与长期结果，且可用卡牌池持续变化。本文研究了万智牌的一种受限构牌形式——限包抽卡（Draft），八名玩家从半随机牌包中依次选择39至45张卡，构建一支40张牌的牌组，信息不完全。通过将卡牌选择过程与实际对战分离，限包抽卡为研究由卡牌组合协同驱动的决策提供了可处理但非平凡的场景。我们提出一种基于编码器的模型，生成具有集合上下文感知的卡牌嵌入，以编码整个抽卡序列。该模型在大规模真实数据上持续优于线性基线，首次建立了万智牌限包结果预测的可学习基准。代码已公开于github.com/akulen/MtGDraftEncoder。
原文摘要 · Abstract (English)
Many real-world games do not admit a fixed, compact rule set: instead, their dynamics are defined by interactions among a large and often evolving collection of game pieces, making general-purpose policy learning impractical. Magic: the Gathering (MTG) exemplifies this setting, where the cards themselves define and alter gameplay rules, strategic constraints, and long-term outcomes, while the pool of available cards is ever-changing. We study Draft, a constrained deck-building format of MTG in which eight players make 39-45 sequential selections from semi-random packs to construct a 40-card deck under partial information. By isolating the card selection process from gameplay, Draft provides a tractable yet non-trivial setting for studying decision-making driven by combinatorial card synergies. We propose an encoder-based model that produces set-contextualized card embeddings to encode the draft decision sequence, with a consistent improvement over linear baselines on large-scale real-world data, establishing a first learned benchmark for outcome prediction in MTG Draft. Our code is available at github.com/akulen/MtGDraftEncoder.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。