电子游戏可信性的挑战：定义、主体模型和模仿学习
The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning
摘要：
在本文中，我们解决了在视频游戏中创建可信代理（虚拟角色）的问题。我们只考虑可信度的一个含义，“给人一种被玩家控制的感觉”，并概述了它的评估问题。我们提出了几种模型，代理在游戏中，可以产生可信的行为，无论是从行业和研究。对于高水平的可信度，学习，特别是模仿学习似乎是要走的路。我们快速概述了不同的方法，使视频游戏的代理从玩家那里学习。最后，我们提出了一个两步的方法来开发新的可信代理模型。首先，我们必须为我们的应用程序找到可信度的标准，并定义一种评估方法。然后设计模型和学习算法。
英文摘要：
In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, ``giving the feeling of being controlled by a player'', and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed.
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。
