Pragmatics studies how context can contribute to language meanings. In human communication, language is never interpreted out of context, and sentences can usually convey more information than their literal meanings. However, this mechanism is missing in most multi-agent systems, restricting the communication efficiency and the capability of human-agent interaction. In this paper, we propose an algorithm, using which agents can spontaneously learn the ability to "read between lines" without any explicit hand-designed rules. We integrate the theory of mind (ToM) in a cooperative multi-agent pedagogical situation and propose an adaptive reinforcement learning (RL) algorithm to develop a communication protocol. ToM is a profound cognitive science concept, claiming that people regularly reason about other's mental states, including beliefs, goals, and intentions, to obtain performance advantage in competition, cooperation or coalition. With this ability, agents consider language as not only messages but also rational acts reflecting others' hidden states. Our experiments demonstrate the advantage of pragmatic protocols over non-pragmatic protocols. We also show the teaching complexity following the pragmatic protocol empirically approximates to recursive teaching dimension (RTD).
Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds
Center for Brain-Inspired Computing Research, Department of Precision Instrument, Tsinghua University, Beijing, China（脑启发计算研究中心，精密仪器系，清华大学，北京，中国）；College of Computer Science and Technology, Zhejiang University, Hangzhou, China（计算机科学与技术学院，浙江大学，杭州，中国）；Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, China（彭途萨微纳米科学与技术研究院，厦门大学，厦门，中国）
MEWL: Few-shot multimodal word learning with referential uncertainty
Peking University（北京大学）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；Beijing Institute of Technology（北京理工大学）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）
与目前最先进的语言模型不同，幼儿通过与周围环境和照顾者的互动来主动习得语言。一种被认为对语言学习至关重要的机制是推断社会环境中其他主体的心理状态的能力，由Premack和Woodruff（1978）创造的心理理论（ToM）。从Rabinowitz et al.（2018）和Zhu et al.（2021）实现的ToM的现代操作化版本中汲取灵感，我们构建了配备ToM的语言学习代理，并测量其对学习过程的影响。我们给说话者代理一个内部的听者模型，该模型与说话者一起训练，并用于对潜在的话语进行重新排序。我们用不同的任务难度进行实验，假设模型将获得更复杂的语言来适应更强的环境压力。我们发现，训练扬声器与一个高度加权的ToM听众组件导致性能增益在我们的图像参考游戏设置。我们还发现一些证据表明，在训练过程中增加任务难度会导致更流畅和准确的话语评价。这表明进一步将心理理论以及儿童语言习得的其他见解纳入语言习得的计算模型的潜在效用。
