How People Intentionally Teach Agents in Interactive Settings

People intuitively teach other people, animals, and even machines. Psychologically, teaching involves multiple interacting processes including modeling the learning agent, predicting its future behavior, and deciding on how teaching actions can facilitate learning. This raises questions about social cognition and interaction, as well as questions of how to design systems that can best leverage people’s capacity for pedagogy.

In this talk, I will discuss two lines of research that seek to answer how people teach in interactive settings. The first is on teaching by evaluative feedback, in which a person provides rewards and punishments to teach a learner a behavior. Teaching rewards are often conceived as incentives that shape the behavior of a learner. However, I will present results showing that this does not come naturally to people. Instead, we find that people use rewards/punishments to communicate whether a course of action is correct, even in online, interactive settings where this strategy is visibly counterproductive. The second line of work is on teaching by demonstration, in which a person takes actions to intentionally show how to perform a task. When intentionally teaching by demonstration, we find that people modify their behavior in ways that are suboptimal for doing the activity, but optimal for communicating their intent based on context.

Together, these findings indicate that teaching rewards and demonstrations differ from those expressed non-communicatively. Moreover, this work suggests that in contexts where autonomous agents must collaborate with and adapt to humans, it will be especially important to design systems that can distinguish and benefit from intentional teaching.

Speaker: Mark Ho, Brown Univ.