CAREER: A Multiagent Teacher/Student Framework for Sequential Decision Making Tasks

职业：用于顺序决策任务的多智能体教师/学生框架

• 批准号: 1149917
• 负责人: Matthew Taylor
• 金额: $ 40.21万
• 依托单位: Lafayette College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149917&HistoricalAwards=false
• 关键词: CAREER; Multiagent; Teacher; Student; Framework

Physical (robotic) agents and virtual (software) agents are becoming increasingly common in industry, education, and domestic environments. Although recent research advances have enabled agents to learn how to complete tasks without human intervention, little is known about how best to have humans teach agents or agents teach other agents or even how agents might teach humans. Considering the full matrix of agent/human learning, in which either an agent or a human can play the role of teacher or student, would increase the potential benefits of leveraging human and agent expertise and knowledge. This project aims to study agent/human learning in the context of sequential decision-making problems, a class of central importance for real-world agent systems. This project aims to develop a novel teacher/student framework that integrates autonomous learning with teaching by another agent or a human. The project plans to develop and evaluate a set of core algorithms to allow: (1) agents to teach agents, thus enabling robust knowledge sharing among agents; (2) humans to teach agents, thus allowing humans to share or transfer common sense or domain-specific knowledge with agents; and (3) agents to teach humans, thus helping humans better understand how to perform or recast sequential decision-making tasks already understood or performed by autonomous agents. In all cases, the goal is to develop methods that significantly improve learning performance relative to learning without guidance from a teacher. Issues to be explored include mismatch between teacher/student abilities, learning from multiple teachers, and shared knowledge representation between teacher/student. The PI plans to focus on several scenarios, each with different sets of assumptions about the knowledge or skill of the student or teacher and the kind of interaction possible between them (e.g., whether the teacher can tell the student what action to take). The techniques developed in the project will be evaluated in a variety of tests domains and will involve simulations as well as actual robots.The teacher/student framework will enable agents to teach other agents and humans, as well as integrate autonomous learning with agent and human teaching. Understanding how to best teach agents is of key importance in developing deployable agent systems. The platform- and domain-independent approach incorporates ideas from multiagent systems, machine learning, human-computer interaction, and human-robot interaction communities, and has the potential to impact each of these areas. This work takes a step towards transitioning agents from specialized systems usable only by experts into useful tools and teammates for people without programming expertise. This project has a strong educational component. The PI teaches at an undergraduate college and undergraduate students will play a crucial role throughout the project. Furthermore, the research produced by this project will be incorporated into five of the PI's courses, providing exciting new material to attract and retain computer science majors. The PI will also continue outreach to secondary school students as well as to underrepresented groups via Lafayette College's S-STEM and Higher Achievement programs.

物理（机器人）代理和虚拟（软件）代理在工业，教育和家庭环境中变得越来越普遍。虽然最近的研究进展已经使智能体能够学习如何在没有人类干预的情况下完成任务，但人们对如何最好地让人类教智能体或智能体教其他智能体，甚至智能体如何教人类知之甚少。考虑代理/人类学习的完整矩阵，其中代理或人类可以扮演教师或学生的角色，将增加利用人类和代理专业知识和知识的潜在好处。这个项目的目的是在顺序决策问题的背景下研究智能体/人类学习，这是现实世界智能体系统的一类重要问题。该项目旨在开发一种新颖的教师/学生框架，将自主学习与另一个代理或人类的教学相结合。该项目计划开发和评估一套核心算法，以允许：（1）智能体教智能体，从而实现智能体之间的强大知识共享;（2）人类教智能体，从而允许人类与智能体共享或传递常识或特定领域的知识;以及（3）教导人类的代理，从而帮助人类更好地理解如何执行或重铸已经由自主代理理解或执行的顺序决策任务。在所有情况下，目标是开发相对于没有教师指导的学习显著提高学习成绩的方法。有待探讨的问题包括教师/学生能力之间的不匹配，从多个教师学习，以及教师/学生之间的共享知识表示。PI计划重点关注几个场景，每个场景都有关于学生或教师的知识或技能以及他们之间可能的互动类型的不同假设（例如，教师是否可以告诉学生采取什么行动）。在该项目中开发的技术将在各种测试领域进行评估，并将涉及模拟以及实际的robots.The教师/学生框架将使代理教其他代理和人类，以及集成自主学习与代理和人类教学。了解如何最好地教代理是开发可部署代理系统的关键重要性。平台和领域独立的方法结合了多智能体系统，机器学习，人机交互和人机交互社区的想法，并有可能影响这些领域。这项工作朝着将代理从仅由专家使用的专用系统转变为没有编程专业知识的人的有用工具和队友迈出了一步。这个项目有很强的教育成分。PI在本科学院任教，本科生将在整个项目中发挥关键作用。此外，该项目产生的研究将被纳入PI的五门课程，提供令人兴奋的新材料，以吸引和留住计算机科学专业的学生。PI还将继续通过拉斐特学院的S-STEM和更高成就计划向中学生以及代表性不足的群体进行宣传。