Companion players for immersive computer games: How to learn behavioural responses from visual effects

拟议的研究将探索人工智能技术创新与开发沉浸式电脑游戏的适应性伴侣的相关性。考虑到游戏的视觉方面，如角色，物体，表面和纹理，同伴将通过解决游戏中的困难挑战（如强化学习或其他AI和机器/深度学习技术）而自动开发。由此产生的探索结果将以视觉交互和学习行为反应的形式反馈到设计过程中，以改善用户的整体沉浸式体验。特别是，该项目将解决计算机程序如何通过智能和学习智能响应或同伴行为向人类学习的问题。最近一项很有前途的研究是发现了生成对抗网络（GANs）和逆强化学习（IRL）之间的数学对应关系。生成对抗网络是一种最近开发的学习方法，用于生成与现有语料库中类似的新数据示例，而逆强化学习（IRL）是一种通过推断教师行为的目标和成本来学习模仿“教师”行为的技术。这一行的工作将允许美术师和程序员找到新的方法来研究代理如何与他们所创造的环境/世界互动，甚至自动化一些需要花费大量时间开发的角色动画。

The proposed research will explore the relevance of AI technological innovation to develop adaptivecompanions for immersive computer games. Given the visual aspects of a game such as characters, objects,surfaces and texture, companions will be developed automatically by tackling hard challenges in gaming suchas Reinforcement learning or other AI and machine/deep learning techniques. The outcomes of the resultingexploration will be fed back to the design process in the form of visual interactions and learned behaviouralresponses to improve the overall immersive experience of the user(s).In particular, the project will address how a computer program can learn from people, both by intelligentimitation and by learning intelligent responses or companion actions. A promising recent line of research is thediscovery of a mathematical correspondence between Generative Adversarial Networks (GANs) - a recentlydeveloped approach to learning to generate new examples of data similar to those in an existing corpus - andInverse Reinforcement Learning (IRL), a technique for learning to imitate the behaviour of a 'teacher' byinferring the teacher's goals and costs from its behaviour. This line of work will allow artists and programmersto find new ways on how agents interact with the environment/world they are creating and even automatesome of the animation of characters that takes a lot of time to develop.