权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: Mutual Learning: A Systems Theoretic Investigation

协作研究：相互学习：系统理论研究

基本信息

批准号：
1930601
负责人：
Kumpati Narendra
金额：
$ 44.63万
依托单位：
Yale University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1930601&HistoricalAwards=false
关键词：
Collaborative Research Mutual Learning Systems

项目摘要

Mutual learning can happen between two humans, a human and machine, or between two machines. The first class is of interest to researchers in the field of social psychology. The importance of human machine interactions is being felt in many situations and most recently in the interaction between the human driven and completely autonomous vehicles. The proposed research deals with machine-machine learning to investigate efficient cooperation between machines, but also reveal the limitations of this cooperation. In particular, the research will attempt to answer questions such as whether two agents, although individually using schemes that will result in the desired behavior, may arrive at wrong conclusion using mutual learning.While the term mutual learning has been used by other investigators in the past, our objective is to investigate it in a quantitative sense within the framework of mathematical systems theory. The problems proposed for investigation include deterministic optimization in high dimensional spaces, stochastic reinforcement learning in static/stationary environments (learning automata) using both deterministic and stochastic schemes, learning in dynamic environments such as the ones described by Markov Decision Processes, and learning/adaptation by multiple agents in dynamic environments described by deterministic or stochastic difference and differential equations. The results on mutual learning obtained during the proposed project will be widely disseminated at national and international conferences as well as the bi-annual Yale Workshops on Adaptive and Learning Systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

相互学习可以发生在两个人之间，人与机器之间，或者两台机器之间。第一节课是社会心理学研究者感兴趣的。人机交互的重要性在许多情况下都可以感受到，最近在人类驾驶和完全自动驾驶汽车之间的交互中。提出的研究涉及机器-机器学习来研究机器之间的有效合作，但也揭示了这种合作的局限性。特别是，该研究将试图回答这样的问题，即两个代理，尽管单独使用将导致期望行为的方案，但是否可能通过相互学习得出错误的结论。虽然相互学习这个术语在过去已经被其他研究者使用，但我们的目标是在数学系统理论的框架内从定量的意义上研究它。提出的研究问题包括高维空间中的确定性优化，静态/静态环境中的随机强化学习（学习自动机），使用确定性和随机方案，动态环境中的学习，例如由马尔可夫决策过程描述的环境，以及由确定性或随机差分和微分方程描述的动态环境中的多代理的学习/适应。在拟议的项目中获得的相互学习的结果将在国内和国际会议以及两年一度的耶鲁适应性和学习系统研讨会上广泛传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。