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

TL;DR

This paper introduces a head-to-head inter-personal multimodal Dirichlet mixture model that improves flexibility in modeling symbol emergence between agents, maintaining performance in multimodal categorization and sign sharing.

Contribution

It proposes a novel head-to-head type Inter-MDM that extends the original model for better flexibility in symbol emergence modeling.

Findings

H2H-type Inter-MDM performs comparably to the conventional model.

The new model offers improved extensibility for complex symbol emergence.

Experimental results confirm the effectiveness of the proposed approach.

Abstract

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.