  Being able to predict the mental states of others is a key factor to
effective social interaction. It is also crucial for distributed multi-agent
systems, where agents are required to communicate and cooperate. In this paper,
we introduce such an important social-cognitive skill, i.e. Theory of Mind
(ToM), to build socially intelligent agents who are able to communicate and
cooperate effectively to accomplish challenging tasks. With ToM, each agent is
capable of inferring the mental states and intentions of others according to
its (local) observation. Based on the inferred states, the agents decide "when"
and with "whom" to share their intentions. With the information observed,
inferred, and received, the agents decide their sub-goals and reach a consensus
among the team. In the end, the low-level executors independently take
primitive actions to accomplish the sub-goals. We demonstrate the idea in two
typical target-oriented multi-agent tasks: cooperative navigation and
multi-sensor target coverage. The experiments show that the proposed model not
only outperforms the state-of-the-art methods on reward and communication
efficiency, but also shows good generalization across different scales of the
environment.
