Optimization of Platform Game Levels for Player Experience
Optimization of Platform Game Levels for Player Experience
复制标题
平台游戏关卡优化，提升玩家体验
DOI：
10.1609/aiide.v5i1.12346
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发表时间：
2009
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：
--
作者：
Chris Pedersen;J. Togelius;Georgios N. Yannakakis
我们展示了一种方法来模拟平台游戏水平的某些参数对玩家游戏体验的影响。一个版本的超级马里奥兄弟已被改编为生成参数化的水平，并通过网络进行实验，以收集数据的水平设计参数和玩家体验方面之间的关系。这些关系已经使用神经网络的偏好学习来学习。所获得的模型将形成游戏水平的人工进化的基础，从而引发所需的玩家情绪。
We demonstrate an approach to modelling the effects of certain parameters of platform game levels on the players' experience of the game. A version of Super Mario Bros has been adapted for generation of parameterized levels, and experiments are conducted over the web to collect data on the relationship between level design parameters and aspects of player experience. These relationships have been learned using preference learning of neural networks. The acquired models will form the basis for artificial evolution of game levels that elicit desired player emotions.