RI: Small: Collaborative Research: Speeding Up Learning through Modeling the Pragmatics of Training

RI：小型：协作研究：通过培训语用建模加速学习

• 批准号: 1319618
• 负责人: Michael Littman
• 金额: $ 14.8万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319618&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Speeding

Years of effort to develop algorithms capable of learning from reward signals have resulted in a plethora of techniques that can leverage numerical signals that vary in value based on performance. Recent efforts to use these techniques to learn from humans providing rewards have been slower to progress, in part, because humans give feedback discretely rather than numerically. This project contributes new learning algorithms designed specifically to leverage the information contained in the choices humans make to provide such discrete feedbacks. The algorithms are inspired by the human-canine partnership, and the incredible things that humans are able to teach dogs using only discrete feedback and carefully constructed sequences of tasks. The Bayesian learning framework being developed in this project will leverage the pragmatic implicatures contained in the feedbacks and tasks sequences to learn more quickly from human feedback. The ultimate goal of this work is to provide a more natural paradigm for humans to tell computers what they would like for them to do. To that end, project efforts will result in a teaching module for Brown University?s Learning Exchange (LE). The LE involves undergraduates working with underserved minority middle school students to engage them in STEM. They are a perfect audience to demonstrate the broader impacts of this work. LE participants learn to instruct computers using a combination of programming with the Scratch environment and the feedback paradigm, which shows how powerful the algorithms are.

开发能够从奖励信号中学习的算法的多年努力导致了许多技术，可以利用根据性能根据性能而变化的数值信号。使用这些技术向人类学习的最新努力是进步的速度较慢，因为人类离散地而不是数字地提供反馈。该项目贡献了专门设计的新学习算法，旨在利用人类提供此类离散反馈的选择中包含的信息。这些算法的灵感来自人类典型的伙伴关系，以及人类只能使用离散反馈和精心构造的任务序列来教狗的令人难以置信的事情。该项目中开发的贝叶斯学习框架将利用反馈和任务序列中包含的实用含义，以从人类反馈中更快地学习。这项工作的最终目标是为人类告诉计算机他们想要做什么。为此，项目努力将为布朗大学的学习交流（LE）提供教学模块。 LE涉及与服务不足的少数族裔中学生合作的大学生，以使他们参与STEM。他们是展示这项工作的更广泛影响的理想受众。 LE参与者学会使用编程与刮擦环境和反馈范式的组合来指导计算机，这显示了算法的功能。