Using a Surrogate Model of Gameplay for Automated Level Design
Using a Surrogate Model of Gameplay for Automated Level Design
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使用游戏替代模型进行自动化关卡设计
DOI：
10.1109/cig.2018.8490425
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Georgios N. Yannakakis
中科院分区：
文献类型：
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作者：
Daniel Karavolos;Antonios Liapis;Georgios N. Yannakakis
本文介绍了如何在同一个游戏中的不同类型的内容之间的相互关系的代理模型可以用于水平生成。具体而言，该模型将射击游戏中的级别结构和游戏规则与游戏结果相关联。我们使用深度学习方法来训练一个模型，模拟两人死亡竞赛游戏的游戏，在不同的级别和每个玩家不同的角色类别。本文的研究结果表明，该模型可以预测比赛的持续时间和胜利者给定的水平和两个球员的性格类的参数自上而下的赢家。有了这个代理模型，我们调查哪一级结构会导致一个给定的字符类集的短，中，长持续时间的平衡匹配。使用进化计算，我们能够发现改善不同类别之间平衡的水平。这为设计者工具打开了潜在的应用，该设计者工具可以调整人类创作的地图以适应设计者期望的游戏结果，同时考虑游戏规则。
This paper describes how a surrogate model of the interrelations between different types of content in the same game can be used for level generation. Specifically, the model associates level structure and game rules with gameplay outcomes in a shooter game. We use a deep learning approach to train a model on simulated playthroughs of two-player deathmatch games, in diverse levels and with different character classes per player. Findings in this paper show that the model can predict the duration and winner of the match given a top-down map of the level and the parameters of the two players’ character classes. With this surrogate model in place, we investigate which level structures would result in a balanced match of short, medium or long duration for a given set of character classes. Using evolutionary computation, we are able to discover levels which improve the balance between different classes. This opens up potential applications for a designer tool which can adapt a human authored map to fit the designer’s desired gameplay outcomes, taking account of the game’s rules.