Toward an argumentation-based dialogue framework for human-robot collaboration

  • Authors:

  • Mohammad Q. Azhar

  • Affiliations:

  • City University of New York, New York, NY, USA

  • Venue:
  • Proceedings of the 14th ACM international conference on Multimodal interaction
  • Year:
  • 2012

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Abstract

The research on human-robot dialogue to support fluent human robot interaction is still in its early stages. Current issues in the human-robot dialogue domain could be divided into two major categories, which are described in this proposal as the "what to say" problem and the "how to say it" problem. The "what to say" problem addresses ways to determine the content of plausible dialogue during human robot interaction, whereas the "how to say it" problem addresses the best ways for a robot to deliver that content (e.g., using text, gestures, speech or different modalities). Dialogue within the robotics domain also needs to address the "when to say it" problem that considers the timing of dialogue delivery (e.g., turn taking). Human-robot collaboration may fail for many reasons. This research focuses on conflicts and robot errors during human-robot collaboration. Conflicting beliefs occur when participating collaborators have two different views of the physical world due to inaccessible or non-static information. Intuitively, collaborators need to persuade each other to agree on the same beliefs about the state of the world in order to complete collaborative tasks successfully. Robot errors may happen due to miscommunication or simply lack of communication. Moreover, lack of dialogue support for a human to query a robot makes it even more difficult for error recovery. This may lead to failure of a collaborative plan or shared goal. Dialogue is the natural way to resolve errors due to miscommunication. This research explores the notion of an argumentation-based dialogue method for human-robot interaction (HRI). The proposed research aims to design and implement a logic-based dialogue framework grounded on argumentation theory to address the "what to say" problem of human robot communication during a collaborative task.