Combining chat and task-based multimodal dialogue for more engaging HRI: A scalable method using reinforcement learning

Ioannis Papaioannou, Oliver Lemon

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

7 Citations (Scopus)

Abstract

We develop the first system to combine task-based and chatbot-style dialogue in a multimodal system for Human-Robot Interaction. We show that Reinforcement Learning is beneficial for training dialogue management (DM) in such systems - providing a scalable method for training from data and/or simulated users. We first train in simulation, and evaluate the benefits of a combined chat/task policy over systems which can only perform chat or task-based conversation. In a real user evaluation, we then show that a trained combined chat/task multimodal dialogue policy results in longer dialogue interactions than a rule-based approach, suggesting that the learned dialogue policy provides a more engaging mixture of chat and task interaction than a rule-based DM method.

Original languageEnglish
Title of host publicationProceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE
Pages365-366
Number of pages2
ISBN (Electronic)9781450348850
DOIs
Publication statusPublished - 6 Mar 2017
Event12th Annual ACM/IEEE International Conference on Human-Robot Interaction 2017 - Vienna, Austria
Duration: 6 Mar 20179 Mar 2017

Conference

Conference12th Annual ACM/IEEE International Conference on Human-Robot Interaction 2017
Abbreviated titleHRI 2017
CountryAustria
CityVienna
Period6/03/179/03/17

Keywords

  • chat bots
  • dialogue
  • evaluation
  • human-robot interaction
  • multimodal
  • reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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  • Cite this

    Papaioannou, I., & Lemon, O. (2017). Combining chat and task-based multimodal dialogue for more engaging HRI: A scalable method using reinforcement learning. In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 365-366). IEEE. https://doi.org/10.1145/3029798.3034820