论文标题
魔术中起草的AI解决方案：聚会
AI solutions for drafting in Magic: the Gathering
论文作者
论文摘要
在魔术中起草这次聚会是一个较大的交易卡游戏中的子游戏，其中几个玩家通过从公共游泳池中挑选卡片逐渐建造甲板。由于其较大的搜索空间，机械复杂性，多人游戏性质和隐藏的信息，因此起草对游戏和AI研究构成了一个有趣的问题。尽管如此，制图仍在研究中，部分原因是缺乏高质量的公共数据集。为了纠正这个问题，我们提供了一个从草稿中收集的100,000多个模拟的匿名人类草稿的数据集。我们还提出了四种不同的策略来起草剂，包括原始的启发式剂，专家调整的复杂启发式剂，天真的贝叶斯代理商和深层神经网络代理。我们基准了它们模仿人类制图的能力，并表明深神经网络代理商的表现优于其他代理，而天真的贝叶斯和专家调整的代理商的表现优于简单的启发式方法。我们分析了AI代理在草稿时间表中的准确性，并描述了每种方法的独特优势和劣势。这项工作有助于确定创建类似人类的起草剂的下一步，并可以作为下一代起草机器人的基准。
Drafting in Magic the Gathering is a sub-game within a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game and AI research due to its large search space, mechanical complexity, multiplayer nature, and hidden information. Despite this, drafting remains understudied, in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from Draftsim.com. We also propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms other agents, while the Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, and describe unique strengths and weaknesses for each approach. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a benchmark for the next generation of drafting bots.
