用贝叶斯优化自动寻找游戏漏洞，提升测试效率与覆盖率。
Bayesian Optimization-based Search for Agent Control in Automated Game Testing
- 基于贝叶斯优化，高效规划下一步测试位置。
- 相比传统方法，探索分布更均匀，覆盖范围提升显著。
- 专为游戏测试设计模型，兼顾精度与可扩展性。
本文提出一种自动化游戏测试方法，通过智能体控制游戏角色探测关卡中的潜在漏洞。利用贝叶斯优化（BO）实现高效的样本搜索，根据已有数据动态确定下一采样点，以最大化信息获取。为此，我们构建了一个基于网格地图的游戏测试专用模型，具备平滑性与不确定性估计能力，同时避免了传统模型的可扩展性问题。实验表明，该方法在时间效率和探索分布上均显著提升了地图覆盖率。
原文摘要 · Abstract (English)
This work introduces an automated testing approach that employs agents controlling game characters to detect potential bugs within a game level. Harnessing the power of Bayesian Optimization (BO) to execute sample-efficient search, the method determines the next sampling point by analyzing the data collected so far and calculates the data point that will maximize information acquisition. To support the BO process, we introduce a game testing-specific model built on top of a grid map, that features the smoothness and uncertainty estimation required by BO, however and most importantly, it does not suffer the scalability issues that traditional models carry. The experiments demonstrate that the approach significantly improves map coverage capabilities in both time efficiency and exploration distribution.
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