论文标题
学习教学：具有内在状态的代理人的顺序教学
Teaching to Learn: Sequential Teaching of Agents with Inner States
论文作者
论文摘要
在顺序的机器教学中，教师的目标是为顺序学习者提供最佳的投入顺序，以指导他们迈向最佳模型。在本文中，我们将此设置从当前的静态一数据集分析扩展到学习者到改变其学习算法或潜在状态以在学习过程中改进并推广到新数据集的学习者。我们介绍了一种多代理公式，其中学习者的内在状态可能会随教学互动而变化，这会影响未来任务中的学习绩效。为了教导这些学习者，我们提出了一种最佳控制方法，该方法在教学后会遵循学习者的未来表现。这为建模具有内在状态的学习者和元学习算法的机器教学提供了建模工具。此外，我们区分了操纵性教学，这可以通过有效隐藏数据并用于灌输的教学来实现更多的通识教育，旨在帮助学习者在没有老师的情况下在新数据集中更好地在新数据集中变得更好。
In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model. In this paper we extend this setting from current static one-data-set analyses to learners which change their learning algorithm or latent state to improve during learning, and to generalize to new datasets. We introduce a multi-agent formulation in which learners' inner state may change with the teaching interaction, which affects the learning performance in future tasks. In order to teach such learners, we propose an optimal control approach that takes the future performance of the learner after teaching into account. This provides tools for modelling learners having inner states, and machine teaching of meta-learning algorithms. Furthermore, we distinguish manipulative teaching, which can be done by effectively hiding data and also used for indoctrination, from more general education which aims to help the learner become better at generalization and learning in new datasets in the absence of a teacher.
