基于Playtraces的关键机制自动发现
Automatic Critical Mechanic Discovery Using Playtraces in Video Games
摘要：
我们提出了一种新的方法，自动关键机制发现的视频游戏使用的游戏描述解析和playtrace信息相结合。该方法被应用于通用视频游戏人工智能（GVG-AI）框架内的几个游戏。在用户研究中，将人类识别的机制与系统识别的关键机制进行比较，以验证人类和系统之间的一致性。研究结果表明，新方法能够以比基线更高的一致性匹配人类。我们的系统进行了进一步验证，通过比较与关键力学和香草MCTS代理$4$游戏从GVG-AI增强MCTS代理。我们新的playtrace方法显示，在所有4个测试游戏的基线上，性能有了显着的提高。所提出的方法也显示出匹配或改进的性能比旧的方法，表明playtrace信息负责更完整的关键机制发现。
英文摘要：
We present a new method of automatic critical mechanic discovery for video games using a combination of game description parsing and playtrace information. This method is applied to several games within the General Video Game Artificial Intelligence (GVG-AI) framework. In a user study, human-identified mechanics are compared against system-identified critical mechanics to verify alignment between humans and the system. The results of the study demonstrate that the new method is able to match humans with higher consistency than baseline. Our system is further validated by comparing MCTS agents augmented with critical mechanics and vanilla MCTS agents on $4$ games from GVG-AI. Our new playtrace method shows a significant performance improvement over the baseline for all 4 tested games. The proposed method also shows either matched or improved performance over the old method, demonstrating that playtrace information is responsible for more complete critical mechanic discovery.
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。
