SEMANTIC BASED LEARNING OF SYNTAX IN AN AUTONOMOUS ROBOT

Abstract

It is the goal of the Language Acquisition Group at the University of Illinois at Urbana-Champaign (LAR-UIUC) to build a robot that is able to learn language as well as humans through embodied sensori-motor interaction with the physical world. This paper proposes cognitive structures to enable an autonomous robot to learn the syntax of two-word sentences using its understanding of lexical semantics. A production rule of syntax in Chomsky Normal Form will be explicitly represented using a hidden Markov Model. Results of robotic experiments show that these models can learn representations of syntax in this form and that they can be used to produce novel sentences.