Answer me this: Constructing Disambiguation Queries for Explanation Generation in Robotics

Tiago Mota, Mohan Sridharan

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

Abstract / Description of output

Our architecture seeks to enable robots collaborating with humans to describe their decisions and evolution of beliefs. To achieve the desired transparency in integrated robot systems that support knowledge-based reasoning and data-driven learning, we build on a baseline system that supports non-monotonic logical reasoning with incomplete commonsense domain knowledge, data-driven learning from a limited set of examples, and inductive learning of previously unknown axioms governing domain dynamics. In the context of a simulated robot providing on-demand, relational descriptions as explanations of its decisions and beliefs, we introduce an interactive system that automatically traces beliefs, and addresses ambiguity in the human queries by constructing and posing suitable disambiguation queries. We present results of evaluation in scene understanding and planning tasks to demonstrate our architecture's abilities.

Original languageEnglish
Title of host publicationIEEE International Conference on Development and Learning, ICDL 2021
PublisherIEEE
Number of pages8
ISBN (Electronic)9781728162423
DOIs
Publication statusPublished - 23 Aug 2021
Event2021 IEEE International Conference on Development and Learning, ICDL 2021 - Virtual, Beijing, China
Duration: 23 Aug 202126 Aug 2021

Conference

Conference2021 IEEE International Conference on Development and Learning, ICDL 2021
Country/TerritoryChina
CityVirtual, Beijing
Period23/08/2126/08/21

Keywords / Materials (for Non-textual outputs)

  • Deep learning
  • Explainable reasoning and learning
  • HRI
  • Non-monotonic logical reasoning

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