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In this study, we propose a head-to-head type (H2H-type) inter-personalmultimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM,which is a probabilistic generative model that represents the symbol emergencebetween two agents as multiagent multimodal categorization. AMetropolis--Hastings method-based naming game based on the Inter-MDM enablestwo agents to collaboratively perform multimodal categorization and share signswith a solid mathematical foundation of convergence. However, the conventionalInter-MDM presumes a tail-to-tail connection across a latent word variable,causing inflexibility of the further extension of Inter-MDM for modeling a morecomplex symbol emergence. Therefore, we propose herein a head-to-head type(H2H-type) Inter-MDM that treats a latent word variable as a child node of aninternal variable of each agent in the same way as many prior studies ofmultimodal categorization. On the basis of the H2H-type Inter-MDM, we propose anaming game in the same way as the conventional Inter-MDM. The experimentalresults show that the H2H-type Inter-MDM yields almost the same performance asthe conventional Inter-MDM from the viewpoint of multimodal categorization andsign sharing.

Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word