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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Veröffentlicht in:2020 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI) S. 619 - 627
Hauptverfasser: Stange, Sonja, Kopp, Stefan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 09.03.2020
Schriftenreihe:ACM Conferences
Schlagworte:
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
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Zusammenfassung: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.
ISBN:1450367461
9781450367462
ISSN:2167-2148
DOI:10.1145/3319502.3374802