  In this study, we propose a head-to-head type (H2H-type) inter-personal
multimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM,
which is a probabilistic generative model that represents the symbol emergence
between two agents as multiagent multimodal categorization. A
Metropolis--Hastings method-based naming game based on the Inter-MDM enables
two agents to collaboratively perform multimodal categorization and share signs
with a solid mathematical foundation of convergence. However, the conventional
Inter-MDM presumes a tail-to-tail connection across a latent word variable,
causing inflexibility of the further extension of Inter-MDM for modeling a more
complex 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 an
internal variable of each agent in the same way as many prior studies of
multimodal categorization. On the basis of the H2H-type Inter-MDM, we propose a
naming game in the same way as the conventional Inter-MDM. The experimental
results show that the H2H-type Inter-MDM yields almost the same performance as
the conventional Inter-MDM from the viewpoint of multimodal categorization and
sign sharing.
