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We present a computational model for a symbol emergence system that enablesthe emergence of lexical knowledge with combinatoriality among agents through aMetropolis-Hastings naming game and cross-situational learning. Manycomputational models have been proposed to investigate combinatoriality inemergent communication and symbol emergence in cognitive and developmentalrobotics. However, existing models do not sufficiently address categoryformation based on sensory-motor information and semiotic communication throughthe exchange of word sequences within a single integrated model. Our proposedmodel facilitates the emergence of lexical knowledge with combinatoriality byperforming category formation using multimodal sensory-motor information andenabling semiotic communication through the exchange of word sequences amongagents in a unified model. Furthermore, the model enables an agent to predictsensory-motor information for unobserved situations by combining wordsassociated with categories in each modality. We conducted two experiments withtwo humanoid robots in a simulated environment to evaluate our proposed model.The results demonstrated that the agents can acquire lexical knowledge withcombinatoriality through interpersonal cross-situational learning based on theMetropolis-Hastings naming game and cross-situational learning. Furthermore,our results indicate that the lexical knowledge developed using our proposedmodel exhibits generalization performance for novel situations throughinterpersonal cross-modal inference.

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