Concept and Language Acquisition by Robots through Probabilistic Models

Abstract

 In this study, we define concepts as categories into which a robot classifies perceptual
information obtained through interaction with others and the environment, and the
inference of unobserved information through the concepts is defined as understanding.
Furthermore, a robot can infer unobserved perceptual information from words by con-
necting concepts and words. This inference is the understanding of word meanings.
We propose probabilistic models that enable robots to learn concepts and language. In
this paper, we present an overview of the proposed models.