Data mining to support human-machine dialogue for autonomous agents

Susan L. Epstein, Rebecca Passonneau, Tiziana Ligorio, Joshua Gordon

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Next-generation autonomous agents will be expected to converse with people to achieve their mutual goals. Human-machine dialogue, however, is challenged by noisy acoustic data, and by people's preference for more natural interaction. This paper describes an ambitious project that embeds human subjects in a spoken dialogue system. It collects a rich and novel data set, including spoken dialogue, human behavior, and system features. During data collection, subjects were restricted to the same databases, action choices, and noisy automated speech recognition output as a spoken dialogue system. This paper mines that data to learn how people manage the problems that arise during dialogue under such restrictions. Two different approaches to successful, goal-directed dialogue are identified this way, from which supervised learning can predict appropriate dialogue choices. The resultant models can then be incorporated into an autonomous agent that seeks to assist its user.

Original languageEnglish (US)
Title of host publicationAgents and Data Mining Interaction - 7th International Workshop, ADMI 2011, Revised Selected Papers
Number of pages24
StatePublished - 2012
Event7th International Workshop on Agents and Data Mining Interaction, ADMI 2011 - Taipei, Taiwan, Province of China
Duration: May 2 2011May 6 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7103 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Workshop on Agents and Data Mining Interaction, ADMI 2011
Country/TerritoryTaiwan, Province of China

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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