Human-AI interactions in real-world complex uncertain environments using a comprehensive reinforcement learning framework

使用综合强化学习框架在现实世界复杂的不确定环境中进行人机交互

• 批准号: 554164-2020
• 负责人: Taylor, Matthew
• 金额: $ 4.74万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705388
• 关键词: Human; AI; interactions; real; world

The advent of Artificial Intelligence (AI) as a core technological approach to solve specific problems is both undeniable and remarkable. However, AI still operates primarily as a tool to execute narrow-focus tasks, rather than a supporting partner in a relationship with human users. Considering human and AI respective strengths and weaknesses, such a man-machine partnership has the potential to become more than the sum of its parts, leveraging complementary abilities to achieve results that would be otherwise impossible or very difficult to achieve with only one or the other. However, for AI agents to work as synergistically as possible with human users/operators, specific methods, approaches and technologies are warranted. The object of the proposed research is to advance those technologies and approaches, as well as to demonstrate their benefits in a real-life setting through practical application in a complex and sensitive environment. This project will combine the efforts of researchers from the University of Alberta and the JACOBB center with resources provided by AIR and Thales. Within this collaboration, we will investigate how human knowledge can be used to train AI agents in complex and real environments. It will also allow us to understand where and when the interaction of agents and humans allows us to achieve higher performance than agents alone or humans alone. To achieve this, different frameworks, interfaces and types of collaboration will first be tested. Subsequently, the feedback systems will be evaluated and compared with each other, sometimes involving humans and AI agents working alone, and sometimes a hybrid approach. The expected results will make the COGMENT platform, developed by AIR and at the heart of the methodology used for this project, more widely accessible and usable by the community.

人工智能（AI）作为解决特定问题的核心技术方法的出现是不可否认的和显着的。然而，人工智能仍然主要作为一种工具来执行狭隘的任务，而不是与人类用户建立关系的支持伙伴。考虑到人类和人工智能各自的优势和劣势，这种人机合作伙伴关系有可能超越其各部分的总和，利用互补的能力来实现只有一个或另一个不可能或很难实现的结果。然而，为了使人工智能代理尽可能与人类用户/操作员协同工作，需要特定的方法，方法和技术。拟议研究的目的是推进这些技术和方法，并通过在复杂和敏感环境中的实际应用来展示它们在现实生活中的好处。该项目将联合收割机的努力，研究人员从阿尔伯塔大学和JACOBB中心与资源提供的空气和泰利斯。在这次合作中，我们将研究如何利用人类知识在复杂和真实的环境中训练AI代理。它还将使我们能够了解智能体和人类的交互在何时何地使我们能够实现比单独的智能体或单独的人类更高的性能。为此，将首先测试不同的框架、接口和协作类型。随后，将对反馈系统进行评估和相互比较，有时涉及人类和人工智能代理单独工作，有时采用混合方法。预期的结果将使由AIR开发的COGMENT平台成为该项目所用方法的核心，更广泛地为社区所使用。