ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
Yuanfei Wang, Fangwei Zhong, Jing Xu, Yizhou Wang
摘要
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 multisensor 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. * indicates equal contribution 1
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper29
- Building Cooperative Embodied Agents Modularly with Large Language ModelsHongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou 等ICLR 2024 · 被引用 303 次
- ProAgent: Building Proactive Cooperative Agents with Large Language ModelsCeyao Zhang, Kaijie Yang, Siyi Hu, Zihao Wang 等AAAI 2024 · 被引用 141 次
- Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMsMaarten Sap, Ronan Le Bras, Daniel Fried, Yejin ChoiEMNLP 2022 · 被引用 92 次
- Towards a Standardised Performance Evaluation Protocol for Cooperative MARLRihab Gorsane, Omayma Mahjoub, Ruan de Kock, Roland Dubb 等NeurIPS 2022 · 被引用 79 次
- Learning Multi-Agent Communication from Graph Modeling PerspectiveShengchao Hu, Li Shen, Ya Zhang, Dacheng TaoICLR 2024 · 被引用 65 次
它引用的顶会 Paper9
- Watch-And-Help: A Challenge for Social Perception and Human-AI CollaborationXavier Puig, Tianmin Shu, Shuang Li, Zilin Wang 等ICLR 2021 · 被引用 170 次
- Learning Individually Inferred Communication for Multi-Agent CooperationZiluo Ding, Tiejun Huang, Zongqing LuNeurIPS 2020 · 被引用 146 次
- Causal Discovery in Physical Systems from VideosYunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox 等NeurIPS 2020 · 被引用 133 次
- AGENT: A Benchmark for Core Psychological ReasoningTianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin A. Smith 等ICML 2021 · 被引用 79 次
- Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor NetworksJing Xu, Fangwei Zhong, Yizhou WangNeurIPS 2020 · 被引用 71 次
相关 Paper
- Symmetric Machine Theory of MindMelanie Sclar, Graham Neubig, Yonatan BiskICML 2022 · 被引用 22 次
- Inverse Attention Agents for Multi-Agent SystemsQian Long, Ruoyan Li, Minglu Zhao, Tao Gao 等ICLR 2025
- Few-shot Language Coordination by Modeling Theory of MindHao Zhu, Graham Neubig, Yonatan BiskICML 2021 · 被引用 43 次
- Adaptive Theory of Mind for LLM-based Multi-Agent CoordinationChunjiang Mu, Ya Zeng, Qiaosheng Zhang, Kun Shao 等AAAI 2026
- Memory-Augmented Theory of Mind NetworkDung Nguyen, Phuoc Nguyen, Hung Le, Kien Do 等AAAI 2023 · 被引用 6 次
