Learning Communicative Meanings of Utterances by Robots

Abstract

This paper describes a computational mechanism that enables a robot to return suitable utterances to a human or perform actions by learning the meanings of interrogative words, such as “what” and “which.” Previous studies of language acquisition by robots have proposed methods to learn words, such as “box” and “blue,” that indicate objects or events in the world. However, the robots could not learn and understand interrogative words by those methods because the words do not directly indicate objects or events. The meanings of those words are grounded in communication and stimulate specific responses by a listener. These are called communicative meanings. Our proposed method learns the relationship between human utterances and robot responses that have communicative meanings on the basis of a graphical model of the human-robot interaction.