Automated Playtesting With Procedural Personas Through MCTS With Evolved Heuristics
Automated Playtesting With Procedural Personas Through MCTS With Evolved Heuristics
复制标题
通过具有进化启发式的 MCTS 使用程序角色进行自动游戏测试
DOI：
10.1109/tg.2018.2808198
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发表时间：
2018
影响因子：
2.3
通讯作者：
J. Togelius
中科院分区：
文献类型：
--
作者：
Christoffer Holmgård;M. Green;Antonios Liapis;J. Togelius
本文描述了一种生成式玩家建模方法，并将其应用于使用称为过程性角色的原型玩家模型对游戏内容进行自动测试。从理论上讲，基于心理决策理论，过程性人物角色使用蒙特卡罗树搜索(MCTS)的一种变体来实现，其中节点选择标准是使用进化计算来制定的，取代了MCTS的标准UCB1标准。使用这些角色，我们演示了如何将生成性玩家模型应用于不同的游戏级别语料库，并演示如何在每个级别执行不同的游戏风格。简而言之，我们使用人工智能人物角色来构建合成的游戏测试器。当人类反馈不容易获得或需要快速可视化潜在交互时，所提出的方法可以用作自动游戏测试的工具。可能的应用包括游戏开发期间的交互式工具或程序性内容生成系统，其中许多评估必须在短时间内进行。
This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo tree search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas, we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different playstyles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.