Abstract

Abstract — In this paper we present results from a user evaluation of a robot bartender system which handles state uncertainty derived from speech input by using belief tracking and generating appropriate clarification questions. We present a combination of state estimation and action selection components in which state uncertainty is tracked and exploited, and compare it to a baseline version that uses standard speech recognition confidence score thresholds instead of belief track-ing. The results suggest that users are served fewer incorrect drinks when the uncertainty is retained in the state. I.