Multi-Agent Knowledge Based Reinforcement Learning

最近的工作已经将人工智能（AI）扩大到围棋世界冠军的表现，并将其广泛应用于各种早期数字游戏。然而，这一领域的大多数工作的重点是使用AI来击败（或超越）人类玩家，而不是人类的进步。我们认为，人工智能应该增强（而不是自动化）人类的能力。这个项目的重点是在多人游戏的背景下为人类游戏玩家开发智能助手。本研究的主要目的是应用强化学习来生成一个智能助手，即一个协作AI，使非玩家角色（npc）能够增强人类玩家在多人游戏中尝试一系列任务的体验。智能玩家助手的表现将通过人类玩家的乐趣、沉浸感、粘性和表现来衡量。为此，我们将研究(1)模仿学习，以提供一个可预测的伴侣；(2)奖励功能，鼓励对共同目标的贡献，而不是个人绩效；(3)在没有明确外部奖励的领域中识别人类目标的玩家行为预测模型。

Recent work has scaled up game playing Artificial Intelligence (AI) to world champion performances in Go and generalised applicability to a wide range of early digital-games. However,the focus of most work in this area has been on using AI to beat (or outperform) human players and not on the betterment of humans. It is our view that AI should augment (not automate) humanabilities. This project focuses on the development of intelligent assistants for human game players in the context of multi-player games.The main aim of this research is to apply reinforcement learning to genrate an intelligent assistant, i.e., a collaborative AI that enables non-player characters (NPCs) to enhance the experience of human players attempting a range of tasks in multi-player games. The performance of the intelligent player assistant will be measured by human player enjoyment, immersion, engagement and performance. To do so we will research (1) imitation learning to provide a predictable companion; (2) reward functions for encouraging contribution towards a shared goal instead of individual performance; and (3) predictive models of player behaviour to identify human goals in domains with no explicit extrinsic reward.