Interactive Machine Learning for More Expressive Game Interactions
Interactive Machine Learning for More Expressive Game Interactions
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DOI：
10.1109/cig.2019.8848007
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
C. Diaz;Phoenix Perry;R. Fiebrink
中科院分区：
文献类型：
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作者：
C. Diaz;Phoenix Perry;R. Fiebrink
视频游戏系统包含各种传感器，以增加玩家交互的范围并改善玩家体验。然而，实现具有传感器的玩家动作的鲁棒识别器对开发人员提出了重大挑战。此外，与传统的输入接口（游戏手柄、键盘和鼠标）相比，基于传感器的控制几乎不提供玩家定制。过去对运动驱动音乐系统的研究已经成功地使用交互式机器学习（IML）技术来促进开发人员和最终用户对基于传感器的界面的开发和定制。然而，现有的IML独立软件工具并不适合用于游戏开发和发行。为了支持游戏开发者和玩家更有效、更灵活地使用传感器，我们以可视化节点系统的形式为Unity3D开发了一个集成的IML解决方案，支持传感器数据的分类、回归和时间序列分析。
Videogame systems incorporate varied sensors to increase the range of player interactions and improve player experience. However, implementing robust recognisers for player actions with sensors presents significant challenges to developers. Further, sensor-based controls offer little player customisation compared to traditional input interfaces (gamepads, keyboards and joysticks). Past research on motion-driven music systems has successfully used interactive machine learning (IML) techniques to facilitate the development and customisation of sensor-based interfaces, both by developers and end users. However, existing standalone software tools for IML are not ideal for use in game development and distribution. In order to support more effective and flexible use of sensors by game developers and players, we developed an integrated IML solution for Unity3D in the form of a visual node system supporting classification, regression and time series analysis of sensor data.