Understanding referring expressions in a person-machine spoken dialogue

Abstract

In the domain of mobile robotic task execution under dialogue control, a primary goal is to identify the task target which is specified by a natural language description. A number of concepts are expressed in the user spoken language by vague terms like "the big box" and "very close to the door". We use fuzzy logic to map these vague terms onto the quantitative data collected by system sensors. Fuzziness may cause uncertainty in interpretation and, in particular, in understanding references. This uncertainty is abated by collecting additional information through queries to the user and autonomous sensing. Entropy is used to select the queries having the greatest discriminatory power among referent candidates. In addition, we examine the trade-off between querying, sensing and uncertainty. A framework to deal with each of these issues has been developed and is presented.