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.
翻译：能够预测他人的精神状态是有效社会互动的一个关键因素。 对于分布式多试剂系统来说,它也是关键,需要代理商进行沟通与合作。在本文中,我们引入了这样一个重要的社会认知技能,即“思想理论”(ToM),以培养社会智能的代理商,能够有效地沟通与合作以完成具有挑战性的任务。与ToM相比,每个代理商都能够根据其(当地)观测结果推断他人的精神状态和意图。根据推断的州,代理商决定“何时”和“何人”分享其意图。根据观察、推断和接收的信息,代理商决定其次级目标并在团队中达成共识。最后,低级执行官独立地采取原始行动来完成次级目标。我们在两个典型的目标导向性多试剂任务中展示了理念:合作导航和多传感器目标覆盖。实验表明,拟议的模型不仅超越了有关奖赏和通信效率的状态方法,而且还展示了不同规模的环境的良好总体化。
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