How Do Humans Teach: On Curriculum Design for Machine Learners

Existing machine-learning work has shown that algorithms can benefit from curricula—learning first on simple examples before moving to more difficult examples. While most existing work on curriculum learning focuses on developing automatic methods to iteratively select training examples with increasing difficulty tailored to the current ability of the learner, relatively little attention has been paid to the ways in which humans design curricula. This thesis aims to better understand the curriculum-design strategies followed by non-experts when teaching the agent, and leverage the findings to develop new machine-learning algorithms and interfaces that better accommodate natural tendencies of human trainers. We discuss completed work on this topic, including the definition of a curriculum-design problem in the context of sequential decision tasks, analysis of how different curricula affect agent learning in a Sokoban-like domain, and results of a user study that explores whether non-experts generate such curricula. Finally, we also present directions for future work.