Using Go-Explore for efficient automatic playtesting of modern tabletop games

自动游戏设计学习（AGDL）是人工智能研究中的一个新兴领域，其目的是通过玩游戏来学习游戏设计模型。当前的策略是在数千次迭代中玩出完整的游戏，这对于具有大状态空间和计算昂贵的前向模型的复杂游戏来说是不切实际的。更有效地探索状态空间可以帮助改善这一点。Go-Explore是一种最新的勘探模式，它比以前的最先进的技术有很大的优势。该研究将侧重于应用Go-Explore来提高桌面游戏自动测试的效率，通过使用有趣的游戏状态存档来减少自我游戏所需的时间。该研究将主要在TAG框架内进行，旨在成为游戏不可知论者。在成功完成后，这项研究将改善游戏开发周期，产生更高质量的游戏，并可能为游戏设计过程提供独特的见解。

Automated Game Design Learning (AGDL) is an emerging field in AI research with the purpose of learning game design models through playing. The current strategy is to play out the full game in thousands of iterations, which can be impractical for complex games with large state space and computationally expensive forward models. More efficient exploration of the state space can help improve that. Go-Explore is a recent exploration paradigm that outperforms previous state-of-the-art by a large margin. The proposed research will focus on applying Go-Explore to improve the efficiency of automated playtesting of tabletop games by using an archive of interesting game states to reduce the time needed for self-play. The research will be primarily conducted within the TAG framework and aim to be game-agnostic. On successful completion, this research will improve game development cycles, resulting in higher-quality games, and potentially give unique insights into the game design process.