Interaction Considerations in Learning from Humans

Abstract

The ability to learn from large quantities of complex data has led to the development of intelligent agents such as self-driving cars and assistive devices. This data often comes from people via interactions such as labeling, providing rewards and punishments, and giving demonstrations or critiques. However, people's ability to provide high-quality data can be affected by human factors of an interaction, such as induced cognitive load and perceived usability. We show that these human factors differ significantly between interaction types. We first formalize interactions as a Markov Decision Process, and construct a taxonomy of these interactions to identify four archetypes: Showing, Categorizing, Sorting, and Evaluating. We then run a user study across two task domains. Our findings show that Evaluating interactions are more cognitively loading and less usable than the others, and Categorizing and Showing interactions are the least cognitively loading and most usable.