Effects of a Social Robot's Self-Explanations on How Humans Understand and Evaluate Its Behavior

Social robots interacting with users in real-life environments will often show surprising or even undesirable behavior. In this paper we investigate whether a robot's ability to self-explain its behavior affects the users' perception and assessment of this behavior. We propose an explanati...

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Published in:2020 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 619 - 627
Main Authors: Stange, Sonja, Kopp, Stefan
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 09.03.2020
Series:ACM Conferences
Subjects:
Behavior Explanations
Human computer interaction
Human-centered computing > Human computer interaction (HCI) > Empirical studies in HCI
Human-centered computing > Human computer interaction (HCI) > HCI design and evaluation methods > User studies
Human-centered computing > Interaction design > Empirical studies in interaction design
Human-centered computing > Interaction design > Interaction design process and methods > Scenario-based design
Human-robot interaction
Perception Study
Robots
human-robot interaction
behavior explanations
perception study
ISBN:1450367461, 9781450367462
ISSN:2167-2148
Online Access:Get full text
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Abstract Social robots interacting with users in real-life environments will often show surprising or even undesirable behavior. In this paper we investigate whether a robot's ability to self-explain its behavior affects the users' perception and assessment of this behavior. We propose an explanation model based on humans' folk-psychological concepts and test different explanation strategies in specifically designed HRI scenarios with robot behaviors perceived as intentional, but differently surprising or desirable. All types of explanation strategies increased the understandability and desirability of the behaviors. While merely stating an action had similar effects as giving a reason for it (an intention or need), combining both in a causal explanation helped the robot to better justify its behavior and to increase its understandability and desirability to a larger extent.
AbstractList Social robots interacting with users in real-life environments will often show surprising or even undesirable behavior. In this paper we investigate whether a robot's ability to self-explain its behavior affects the users' perception and assessment of this behavior. We propose an explanation model based on humans' folk-psychological concepts and test different explanation strategies in specifically designed HRI scenarios with robot behaviors perceived as intentional, but differently surprising or desirable. All types of explanation strategies increased the understandability and desirability of the behaviors. While merely stating an action had similar effects as giving a reason for it (an intention or need), combining both in a causal explanation helped the robot to better justify its behavior and to increase its understandability and desirability to a larger extent.
Social robots interacting with users in real-life environments will often show surprising or even undesirable behavior. In this paper we investigate whether a robot's ability to self-explain its behavior affects the users' perception and assessment of this behavior. We propose an explanation model based on humans' folk-psychological concepts and test different explanation strategies in specifically designed HRI scenarios with robot behaviors perceived as intentional, but differently surprising or desirable. All types of explanation strategies increased the understandability and desirability of the behaviors. While merely stating an action had similar effects as giving a reason for it (an intention or need), combining both in a causal explanation helped the robot to better justify its behavior and to increase its understandability and desirability to a larger extent. CCS CONCEPTS * Human-centered computing → Empirical studies in HCI; User studies; Scenario-based design; Empirical studies in interaction design. ACM Reference Format: Sonja Stange and Stefan Kopp. 2020. Effects of a Social Robot's Self-Explanations on How Humans Understand and Evaluate Its Behavior. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374802
Author Kopp, Stefan
Stange, Sonja
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Keywords human-robot interaction
behavior explanations
perception study
Language English
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Snippet Social robots interacting with users in real-life environments will often show surprising or even undesirable behavior. In this paper we investigate whether a...
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SubjectTerms Behavior Explanations
Human computer interaction
Human-centered computing -- Human computer interaction (HCI) -- Empirical studies in HCI
Human-centered computing -- Human computer interaction (HCI) -- HCI design and evaluation methods -- User studies
Human-centered computing -- Interaction design -- Empirical studies in interaction design
Human-centered computing -- Interaction design -- Interaction design process and methods -- Scenario-based design
Human-robot interaction
Perception Study
Robots
Title Effects of a Social Robot's Self-Explanations on How Humans Understand and Evaluate Its Behavior
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