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

本研究的目标是开发一种技术，允许计算机或机器人从示例中学习，以代表用户执行用自然语言指定的多部分任务。它将单独研究这些组成部分中的每一个，但重点将是将它们整合成一个连贯的系统。该项目还将利用这项技术为非计算机科学专业或计算机科学预科的学生提供一个入门点，让他们了解计算机作为工具的能力和效用。我们的方法使用了三个主要的子组件，每一个都需要创新的研究来解决整个问题的一部分。此外，集成架构是这项工作的一个新颖贡献。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成任务规范，以及(3)使用创建和管理抽象的概率方法创建通用任务规范以匹配用户意图。这项工作的目标是开发技术，以提高人类用户与智能代理交互的能力，将新的人工智能研究见解和活动纳入教育和推广活动，并为人工智能教育者社区开发资源。除了允许智能代理在未来为广泛的复杂应用领域开发和培训之外，我们将开发的交互式代理将用于外展和学生学习。

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.