Probabilistic reference and grounding with PRAGR for dialogues with robots

In this paper, we present a system for effective referential human-robot communication in the face of perceptual deviation using the Probabilistic Reference And GRounding mechanism PRAGR and vague feature models based on prototypes. PRAGR can handle descriptions of arbitrary complexity including spa...

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Bibliographic Details
Published inJournal of experimental & theoretical artificial intelligence Vol. 28; no. 5; pp. 889 - 911
Main Authors Mast, Vivien, Falomir, Zoe, Wolter, Diedrich
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.09.2016
Taylor & Francis Ltd
Subjects
Artificial intelligence
Crisps
Descriptions
Deviation
dialogue
Expert systems
human-machine interaction
perceptual deviation
Probabilistic methods
Probability theory
Reference handling
Robots
Robustness
symbol grounding
vagueness
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ISSN0952-813X
1362-3079
DOI10.1080/0952813X.2016.1154611

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Summary:In this paper, we present a system for effective referential human-robot communication in the face of perceptual deviation using the Probabilistic Reference And GRounding mechanism PRAGR and vague feature models based on prototypes. PRAGR can handle descriptions of arbitrary complexity including spatial relations and uses flexible concept assignment in generation and resolution of referring expressions for bridging conceptual gaps in referential robot-robot or human-robot interaction. We evaluate the benefit of using vague as compared to crisp properties regarding referential success and robustness towards perspective alignment error in referential robot-robot and human-robot communication.
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ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2016.1154611