Effective communication requires the ability to refer to specific parts of an
observation in relation to others. While emergent communication literature
shows success in developing various language properties, no research has shown
the emergence of such positional references. This paper demonstrates how agents
can communicate about spatial relationships within their observations. The
results indicate that agents can develop a language capable of expressing the
relationships between parts of their observation, achieving over 90% accuracy
when trained in a referential game which requires such communication. Using a
collocation measure, we demonstrate how the agents create such references. This
analysis suggests that agents use a mixture of non-compositional and
compositional messages to convey spatial relationships. We also show that the
emergent language is interpretable by humans. The translation accuracy is
tested by communicating with the receiver agent, where the receiver achieves
over 78% accuracy using parts of this lexicon, confirming that the
interpretation of the emergent language was successful.

该论文展示了代理可以在其观察中进行空间关系的沟通，结果表明代理能够发展出能够表达其观察各部分之间关系的语言，并在需要这种沟通的参考游戏中实现 90% 以上的准确性。该论文还展示了这种新兴语言是可解释的，而且与人类的交流和解释成功完成的测试结果证实了这一点。

以您的语言沟通：可解释性新兴交流中的空间关系

Speaking Your Language: Spatial Relationships in Interpretable Emergent  Communication

The remote wireless control of industrial systems is one of the major use
cases for 5G and beyond systems: in these cases, the massive amounts of sensory
information that need to be shared over the wireless medium may overload even
high-capacity connections. Consequently, solving the effective communication
problem by optimizing the transmission strategy to discard irrelevant
information can provide a significant advantage, but is often a very complex
task. In this work, we consider a prototypal system in which an observer must
communicate its sensory data to a robot controlling a task (e.g., a mobile
robot in a factory). We then model it as a remote Partially Observable Markov
Decision Process (POMDP), considering the effect of adopting semantic and
effective communication-oriented solutions on the overall system performance.
We split the communication problem by considering an ensemble Vector Quantized
Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement
Learning (DRL) agent to dynamically adapt the quantization level, considering
both the current state of the environment and the memory of past messages. We
tested the proposed approach on the well-known CartPole reference control
problem, obtaining a significant performance increase over traditional
approaches.

基于远程无线控制和 5G 及更高级别系统，本研究研究了优化传输策略以实现有效沟通的方法，提出了采用集合向量量化变分自编码器进行编码，并训练深度强化学习代理动态调整量化水平，通过在基准控制问题上的测试显示其具有显著的性能提升。

动态特征压缩下的有效通信

Effective Communication with Dynamic Feature Compression

Effective communication requires adapting to the idiosyncratic common ground
shared with each communicative partner. We study a particularly challenging
instantiation of this problem: the popular game Dixit. We formulate a round of
Dixit as a multi-agent image reference game where a (trained) speaker model is
rewarded for describing a target image such that one (pretrained) listener
model can correctly identify it from a pool of distractors, but another
listener cannot. To adapt to this setting, the speaker must exploit differences
in the common ground it shares with the different listeners. We show that
finetuning an attention-based adapter between a CLIP vision encoder and a large
language model in this contrastive, multi-agent setting gives rise to
context-dependent natural language specialization from rewards only, without
direct supervision. In a series of controlled experiments, we show that the
speaker can adapt according to the idiosyncratic strengths and weaknesses of
various pairs of different listeners. Furthermore, we show zero-shot transfer
of the speaker's specialization to unseen real-world data. Our experiments
offer a step towards adaptive communication in complex multi-partner settings
and highlight the interesting research challenges posed by games like Dixit. We
hope that our work will inspire creative new approaches to adapting pretrained
models.

研究表明，在多方参与者的复杂情境下，利用深度强化学习，适应目标听众的共性和个性从而适应性传达信息是成功的，该项研究还为人们探索被称为 Dixit 的游戏中的技术挑战提供了启示。