We present a computational model for a symbol emergence system that enables
the emergence of lexical knowledge with combinatoriality among agents through a
Metropolis-Hastings naming game and cross-situational learning. Many
computational models have been proposed to investigate combinatoriality in
emergent communication and symbol emergence in cognitive and developmental
robotics. However, existing models do not sufficiently address category
formation based on sensory-motor information and semiotic communication through
the exchange of word sequences within a single integrated model. Our proposed
model facilitates the emergence of lexical knowledge with combinatoriality by
performing category formation using multimodal sensory-motor information and
enabling semiotic communication through the exchange of word sequences among
agents in a unified model. Furthermore, the model enables an agent to predict
sensory-motor information for unobserved situations by combining words
associated with categories in each modality. We conducted two experiments with
two humanoid robots in a simulated environment to evaluate our proposed model.
The results demonstrated that the agents can acquire lexical knowledge with
combinatoriality through interpersonal cross-situational learning based on the
Metropolis-Hastings naming game and cross-situational learning. Furthermore,
our results indicate that the lexical knowledge developed using our proposed
model exhibits generalization performance for novel situations through
interpersonal cross-modal inference.

本文提出了一种计算模型，该模型通过 Metropolis-Hastings 命名游戏和跨情景学习，使代理之间的组合性出现，并使语词知识生成，从而实现了类别形成和交际式通信。

符号出现作为人际情境跨越学习：组合词汇知识的出现

Symbol emergence as interpersonal cross-situational learning: the  emergence of lexical knowledge with combinatoriality

Allowing humans to communicate through natural language with robots requires
connections between words and percepts. The process of creating these
connections is called symbol grounding and has been studied for nearly three
decades. Although many studies have been conducted, not many considered
grounding of synonyms and the employed algorithms either work only offline or
in a supervised manner. In this paper, a cross-situational learning based
grounding framework is proposed that allows grounding of words and phrases
through corresponding percepts without human supervision and online, i.e. it
does not require any explicit training phase, but instead updates the obtained
mappings for every new encountered situation. The proposed framework is
evaluated through an interaction experiment between a human tutor and a robot,
and compared to an existing unsupervised grounding framework. The results show
that the proposed framework is able to ground words through their corresponding
percepts online and in an unsupervised manner, while outperforming the baseline
framework.

本文提出了一种基于交叉情景学习的符号接地框架，用于自然语言沟通机器人与人类交互，并在机器人与人类之间的交互实验中表现优异。