Characterizing tradeoffs between teaching via language and
  demonstrations in multi-agent systems
Dhara Yu Noah D. Goodman Jesse Mu
Humans teach others about the world through language and demonstration. When
might one of these modalities be more effective than the other? In this work,
we study the factors that modulate the effectiveness of language vs.
demonstration using multi-agent systems to model human communication.
Specifically, we train neural network agents to teach via language or
demonstration in a grounded communication task, manipulating 1) the inherent
difficulty of the task and 2) the competence of the teacher. We find that
teaching by demonstration is more effective in the simplest settings, but
language is more effective as task difficulty increases, due to its ability to
generalize more effectively to unseen scenarios. Overall, these results provide
converging evidence for a tradeoff between language and demonstration as
teaching modalities in humans, and make the novel predictions that
demonstration may be optimal for easy tasks, while language enables
generalization in more challenging settings.