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Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction

In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributio...

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Autores principales: Stange, Sonja, Hassan, Teena, Schröder, Florian, Konkol, Jacqueline, Kopp, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106388/
https://www.ncbi.nlm.nih.gov/pubmed/35573901
http://dx.doi.org/10.3389/frai.2022.866920
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author Stange, Sonja
Hassan, Teena
Schröder, Florian
Konkol, Jacqueline
Kopp, Stefan
author_facet Stange, Sonja
Hassan, Teena
Schröder, Florian
Konkol, Jacqueline
Kopp, Stefan
author_sort Stange, Sonja
collection PubMed
description In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests.
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spelling pubmed-91063882022-05-14 Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction Stange, Sonja Hassan, Teena Schröder, Florian Konkol, Jacqueline Kopp, Stefan Front Artif Intell Artificial Intelligence In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests. Frontiers Media S.A. 2022-04-29 /pmc/articles/PMC9106388/ /pubmed/35573901 http://dx.doi.org/10.3389/frai.2022.866920 Text en Copyright © 2022 Stange, Hassan, Schröder, Konkol and Kopp. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Stange, Sonja
Hassan, Teena
Schröder, Florian
Konkol, Jacqueline
Kopp, Stefan
Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction
title Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction
title_full Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction
title_fullStr Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction
title_full_unstemmed Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction
title_short Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction
title_sort self-explaining social robots: an explainable behavior generation architecture for human-robot interaction
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106388/
https://www.ncbi.nlm.nih.gov/pubmed/35573901
http://dx.doi.org/10.3389/frai.2022.866920
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