ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
This addresses the problem of inefficient coordination in distributed multi-agent systems for tasks requiring social intelligence, representing an incremental improvement with a novel method.
The paper tackles the challenge of enabling effective communication and cooperation in multi-agent systems by integrating Theory of Mind (ToM) to infer mental states and intentions, resulting in outperforming state-of-the-art methods in reward and communication efficiency across tasks like cooperative navigation and multi-sensor target coverage.
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.