论文标题
使用模仿学习在计算机游戏中的知情设计和验证帮助
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning
论文作者
论文摘要
在游戏中，就像在其他许多领域一样，设计验证和测试是一个巨大的挑战，因为系统的尺寸正在增长，并且手动测试变得不可行。本文提出了一种新方法来自动游戏验证和测试。我们的方法利用了数据驱动的模仿学习技术，该技术几乎不需要精力和时间，并且对机器学习或编程不了解，设计人员可以使用该技术有效地训练游戏测试剂。我们通过与行业专家的用户研究一起研究了方法的有效性。调查结果表明，我们的方法确实是一种有效的游戏验证方法，并且数据驱动的编程将是减少努力和提高现代游戏测试质量的有用帮助。该调查还重点介绍了一些开放挑战。在最新文献的帮助下，我们分析了确定的挑战，并提出了适合支持和最大化我们方法效用的未来研究方向。
In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible. This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming, that designers can use to efficiently train game testing agents. We investigate the validity of our approach through a user study with industry experts. The survey results show that our method is indeed a valid approach to game validation and that data-driven programming would be a useful aid to reducing effort and increasing quality of modern playtesting. The survey also highlights several open challenges. With the help of the most recent literature, we analyze the identified challenges and propose future research directions suitable for supporting and maximizing the utility of our approach.
