  Pragmatics studies how context can contribute to language meanings. In human
communication, language is never interpreted out of context, and sentences can
usually convey more information than their literal meanings. However, this
mechanism is missing in most multi-agent systems, restricting the communication
efficiency and the capability of human-agent interaction. In this paper, we
propose an algorithm, using which agents can spontaneously learn the ability to
"read between lines" without any explicit hand-designed rules. We integrate the
theory of mind (ToM) in a cooperative multi-agent pedagogical situation and
propose an adaptive reinforcement learning (RL) algorithm to develop a
communication protocol. ToM is a profound cognitive science concept, claiming
that people regularly reason about other's mental states, including beliefs,
goals, and intentions, to obtain performance advantage in competition,
cooperation or coalition. With this ability, agents consider language as not
only messages but also rational acts reflecting others' hidden states. Our
experiments demonstrate the advantage of pragmatic protocols over non-pragmatic
protocols. We also show the teaching complexity following the pragmatic
protocol empirically approximates to recursive teaching dimension (RTD).
