Multi-Agent Knowledge Based Reinforcement Learning

最近的工作已经将游戏人工智能（AI）扩展到围棋世界冠军的表现，并将其广泛适用于早期的数字游戏。然而，这一领域大多数工作的重点是使用AI击败（或超越）人类玩家，而不是改善人类。我们认为，人工智能应该增强（而不是自动化）人类能力。该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智能助手，即，一种协作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。智能玩家助手的性能将通过人类玩家的享受，沉浸感，参与度和表现来衡量。为了做到这一点，我们将研究（1）模仿学习，以提供一个可预测的同伴;（2）奖励功能，以鼓励对共同目标的贡献，而不是个人的表现;（3）玩家行为的预测模型，以确定人类目标的领域没有明确的外在奖励。

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.