Handling Failures in Human-Computer Conversation

Uncertainty plays a central role in the handling of failures in conversation. People manage uncertainty by establishing and maintaining the mutual belief that their utterances have been understood well enough for current purposes, a process referred to as grounding. In this paper, we propose a computational architecture for supporting grounding in human-computer spoken dialog. We delineate four levels of analysis and describe representations and inference procedures for resolving key uncertainties that may lead to communication failures. We illustrate the architecture by reviewing interactions between a user and a prototype spoken dialog system called the Bayesian Receptionist.