论文标题
增强视频游戏测试的蒙特卡洛树搜索算法
Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing
论文作者
论文摘要
在本文中，我们研究了对视频游戏测试的几种蒙特卡洛树搜索（MCT）修改的效果。尽管MCT修改在游戏中进行了高度研究，但它们对寻找错误的影响是空白的。我们专注于在上一项研究中发现的错误查找，在该研究中我们引入了合成和类似人类的测试目标，并在SARSA和MCTS代理中使用了这些测试目标来查找错误。在这项研究中，我们将MCTS代理扩展了一些修改，以进行游戏测试。此外，我们提出了一种新颖的树木再利用策略。我们通过在三个测试台游戏中测试这些修改，每个级别四个级别，总共包含45个错误。我们使用通用视频游戏人工智能（GVG-AI）框架来创建测试床游戏并使用GVG-AI框架收集427个人类测试仪轨迹。我们分析了三个部分中提出的修改：我们评估了它们对虫子查找代理表现的影响，我们在两个不同的计算预算下衡量了它们的成功，并评估了它们对它们对类似人类剂的人的影响。我们的结果表明，MCTS的修改可改善试剂的错误查找性能。
In this paper, we study the effects of several Monte Carlo Tree Search (MCTS) modifications for video game testing. Although MCTS modifications are highly studied in game playing, their impacts on finding bugs are blank. We focused on bug finding in our previous study where we introduced synthetic and human-like test goals and we used these test goals in Sarsa and MCTS agents to find bugs. In this study, we extend the MCTS agent with several modifications for game testing purposes. Furthermore, we present a novel tree reuse strategy. We experiment with these modifications by testing them on three testbed games, four levels each, that contain 45 bugs in total. We use the General Video Game Artificial Intelligence (GVG-AI) framework to create the testbed games and collect 427 human tester trajectories using the GVG-AI framework. We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent. Our results show that MCTS modifications improve the bug finding performance of the agents.
