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.

这项研究的目的是开发将允许计算机或机器人从示例中学习的技术，以代表用户执行自然语言指定的多部分任务。 它将分别研究这些组件中的每一个，但重点将是将它们整合到连贯的系统中。 该项目还将利用这项技术提供一个入口点，以教育非计算机科学专业的学生作为工具的功能和实用性。我们的方法使用三个主要子组件，每个子组件都需要创新研究来解决整体问题的部分。 此外，综合体系结构是这项工作的新颖贡献。 这三个组成部分是（1）使用逆增强学习的扩展来认识到观察到的行为的意图，（2）将指示转化为使用自然语言处理领域的新技术，将指示转化为任务规格，以及（3）创建通用任务规格，以使用概率的方法来创建和管理型的智力技术，以匹配用户的概率。对教育和外展活动的见解和活动，以及为AI教育者社区开发资源。 除了允许将来开发和培训智能代理商的各种复杂应用领域外，我们将开发的交互式代理还将用于外展和学生学习。