arXiv 1708.007302017-08-02cs.AIcs.GT

帮助AI玩《炉石传说》：AAIA '17数据采掘挑战赛

Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge

Institute of Informatics, University of Warsaw（华沙大学信息学研究所）；Silver Bullet Solutions（银弹解决方案公司）

Andrzej Janusz, Maciej Świechowski, Tomasz Tajmajer

备注：: Federated Conference on Computer Science and Information Systems, Prague (FedCSIS-2017) (Prague, Czech Republic)

摘要：

本文总结了2017年3月23日至5月15日在Knowledge Pit平台举行的AAIA“17数据采掘挑战赛：帮助人工智能玩炉石传说”。我们简要描述了这个比赛的范围和背景，在一个更一般的项目的背景下，与视频游戏的AI引擎的开发，称为圣杯。我们还讨论了这一挑战的结果，并展示了如何预测模型的球员的获胜机会的评估，可以利用在一个智能代理的建设，玩炉石传说。最后，我们展示了几种选定的机器学习方法，用于在Hearthstone中对状态和动作值进行建模。我们提供了一些有前途的解决方案，可用于创建更先进的类型的代理，特别是在结合蒙特卡罗树搜索算法的评估。

英文摘要：

This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also discuss the outcomes of this challenge and demonstrate how predictive models for the assessment of player's winning chances can be utilized in a construction of an intelligent agent for playing Hearthstone. Finally, we show a few selected machine learning approaches for modeling state and action values in Hearthstone. We provide evaluation for a few promising solutions that may be used to create more advanced types of agents, especially in conjunction with Monte Carlo Tree Search algorithms.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。