用模仿算法学习可信虚拟角色环境的表示
Learning a Representation of a Believable Virtual Character's Environment with an Imitation Algorithm
摘要：
在电子游戏中，虚拟角色的决策系统通常使用简化的世界表示。为了提高他们的自主性和可信度，我们希望这些角色能够从人类玩家那里学习这种表示。我们建议使用一种称为生长神经气体的模型，通过模仿环境的拓扑结构来学习。详细介绍了模型的实现、修改和使用的参数。然后，学习表示的质量和它们在学习过程中的演变使用不同的措施进行了研究。在结论部分，对神经元气体模型进行了改进，使其能为人物模型提供更多的信息。
英文摘要：
In video games, virtual characters' decision systems often use a simplified representation of the world. To increase both their autonomy and believability we want those characters to be able to learn this representation from human players. We propose to use a model called growing neural gas to learn by imitation the topology of the environment. The implementation of the model, the modifications and the parameters we used are detailed. Then, the quality of the learned representations and their evolution during the learning are studied using different measures. Improvements for the growing neural gas to give more information to the character's model are given in the conclusion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
