Representation of Symbol Emergence System by Head-to-Head Multi-Agent Multimodal Categorization

Abstract

Multi-agent multimodal categorization method for modeling symbol emergence was proposed in a previous study. However, the model regarded signs, i.e., words, as priors of latent variables representing object categories and made the model incompatible with pre-existing multimodal categorization models, which regarded signs as observations, i.e., leaf nodes of the probabilistic graphical models. This study proposes a new model by modifying the tail-to-tail connection for the variable corresponding to signs in the previous model to the head-to-head connection. In the experiment, we compared the previously proposed model and ours. Experimental results show that the performance of the modified model is equivalent to that of the original model.