RI: Medium: Collaborative Research: Teaching Computers to Follow Verbal Instructions

RI：媒介：协作研究：教计算机遵循口头指令

• 批准号: 1065195
• 负责人: Michael Littman
• 金额: $ 70.39万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065195&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Teaching

The goal of this research is to develop techniques that will permit a computer or robot to learn from examples to carry out multipart tasks specified in natural language on behalf of a user. It will study each of these components in isolation, but a significant focus will be on integrating them into a coherent system. The project will also leverage this technology to provide an entry point to educate non- or pre-computer science students about the capabilities and utility of computers as tools.Our approach uses three main subcomponents, each of which requires innovative research to solve its portion of the overall problem. In addition, the integrated architecture is a novel contribution of this work. The three components are (1) recognizing intention from observed behavior using extensions of inverse reinforcement learning, (2) translating instructions to task specifications using novel techniques in the area of natural language processing, and (3) creating generalized task specifications to match user intentions using probabilistic methods for creating and managing abstractions.The goal of the work is develop technology for an improved ability for human users to interact with intelligent agents, the incorporation of novel AI research insights and activities into education and outreach activities, and the development of resources for the AI educator community. In addition to permitting intelligent agents to be developed and trained in the future for a broad range of complex application domains, the interactive agents that we will develop will be used for outreach and student learning.

这项研究的目标是开发技术，允许计算机或机器人从示例中学习，以代表用户执行自然语言指定的多部分任务。 它将孤立地研究这些组成部分中的每一个，但一个重要的重点将是将它们纳入一个连贯的系统。 该项目还将利用这项技术提供一个切入点，教育非计算机科学或预计算机科学的学生有关的能力和效用的计算机作为工具。我们的方法使用三个主要的子组件，其中每一个都需要创新的研究，以解决其部分的整体问题。 此外，集成架构是这项工作的一个新的贡献。 这三个组成部分是（1）使用反向强化学习的扩展从观察到的行为中识别意图，（2）使用自然语言处理领域的新技术将指令翻译为任务规范，以及（3）使用概率方法创建和管理抽象，创建通用任务规范以匹配用户意图。工作的目标是开发技术，以提高人类用户与智能代理交互，将新的AI研究见解和活动纳入教育和推广活动，以及为AI教育者社区开发资源。 除了允许智能代理开发和培训，在未来广泛的复杂的应用领域，我们将开发的互动代理将用于推广和学生学习。