  Teaching an agent to perform new tasks using natural language can easily be
hindered by ambiguities in interpretation. When a teacher provides an
instruction to a learner about an object by referring to its features, the
learner can misunderstand the teacher's intentions, for instance if the
instruction ambiguously refer to features of the object, a phenomenon called
referential ambiguity. We study how two concepts derived from cognitive
sciences can help resolve those referential ambiguities: pedagogy (selecting
the right instructions) and pragmatism (learning the preferences of the other
agents using inductive reasoning). We apply those ideas to a teacher/learner
setup with two artificial agents on a simulated robotic task (block-stacking).
We show that these concepts improve sample efficiency for training the learner.
