Synthesizing Movements for Computer Game Characters
Synthesizing Movements for Computer Game Characters
复制标题
合成电脑游戏角色的动作
DOI：
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复制
发表时间：
2004
期刊：
影响因子：
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通讯作者：
G. Sagerer
中科院分区：
文献类型：
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作者：
Christian Thurau;C. Bauckhage;G. Sagerer
生物神经科学的最新发现表明，大脑将身体动作作为运动原始序列来学习。与此同时，这一原理在机器人学、计算机图形学和计算机视觉中越来越受欢迎：运动基元被成功地应用于机器人控制任务以及渲染或识别人类行为。在这篇文章中，我们证明了运动基元也可以应用于实现逼真的计算机游戏角色的问题。我们提出了一种集成了多种模式识别和机器学习技术的行为建模和学习方法：用记录的多人计算机游戏数据训练神经网络，学习虚拟世界的拓扑表示；使用PCA来识别人类在比赛中重复执行的基本动作，并将复杂行为表示为将运动基元映射到游戏环境中的位置的概率函数。实验结果表明，该框架生成的游戏角色具有与人类相似的技能。
Recent findings in biological neuroscience suggest that the brain learns body movements as sequences of motor primitives. Simultaneously, this principle is gaining popularity in robotics, computer graphics and computer vision: movement primitives were successfully applied to robotic control tasks as well as to render or to recognize human behavior. In this paper, we demonstrate that movement primitives can also be applied to the problem of implementing lifelike computer game characters. We present an approach to behavior modeling and learning that integrates several pattern recognition and machine learning techniques: trained with data from recorded multiplayer computer games, neural gas networks learn topological representation of virtual worlds; PCA is used to identify elementary movements the human players repeatedly executed during a match and complex behaviors are represented as probability functions mapping movement primitives to locations in the game environment. Experimental results underline that this framework produces game characters with humanlike skills.