ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
By Yuanfei Wang, Fangwei Zhong, Jing Xu, Yizhou Wang. 2021
113 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Sociology and Political Science, Statistical and Nonlinear Physics, LLM Agents & Tool Use
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Citations in the corpus
- Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs (2022, background): “In fact, promising research has shown that using an interactive learning or multi-agent communication paradigm can enable some Theory of Mind capabilities of models (Hawkins et al., 2019; Lazaridou et al., 2020; Zhu et al., 2021; Wang et al., 2022).”
- Building Cooperative Embodied Agents Modularly with Large Language Models (2023, background): “Although LLMs still face challenges when solving complex reasoning problems (Bubeck et al., 2023), a substantial body of work demonstrates their capacity to make plans (Sharma et al., 2021; Raman et al., 2022; Pallagani et al., 2022; Gramopadhye & Szafir, 2022; Yuan et al., 2023; Li et al., 2022;…”