  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.
