Towards an Interaction-Centered and Dynamically Constructed Episodic Memory for Social Robots

Hassan, Teena; Kopp, Stefan

doi:10.1145/3371382.3378329

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Here, the robot's self model (see Figure 2) plays a central role in generating these explanations: Episodic information about internal processes as well as external events must be encoded and stored in memory. For example, in our architecture, interaction episodes are defined on the basis of (i) user-initiated dialog sessions or (ii) robot-initiated plans of actions (Hassan and Kopp, 2020). This information is then fed as input to a module for dynamically generating behavior explanations that suit the user's need for explanation.…”

Section: A Framework For Behavior Explanations In Social Human-robot ...mentioning

confidence: 99%

“…The Episodic Memory integrates events and actions into an interaction episode. It tracks and updates the ongoing interaction episode and persistently stores past interaction episodes (see Hassan and Kopp, 2020) for details). The ongoing interaction episode provides information about the active robot behaviors and actions, which can then be used by the robot for generating the action explanation.…”

Section: Memorymentioning

confidence: 99%

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

Stange

Hassan

Schröder

et al. 2022

Front. Artif. Intell.

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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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Section: A Framework For Behavior Explanations In Social Human-robot ...mentioning

confidence: 99%

Section: Memorymentioning

confidence: 99%

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

Stange

Hassan

Schröder

et al. 2022

Front. Artif. Intell.

Self Cite

View full text Add to dashboard Cite

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Never Trust Anything That Can Think for Itself, if You Can’t Control Its Privacy Settings: The Influence of a Robot’s Privacy Settings on Users’ Attitudes and Willingness to Self-disclose

Stapels,

Penner,

Diekmann

et al. 2023

Int J of Soc Robotics

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When encountering social robots, potential users are often facing a dilemma between privacy and utility. That is, high utility often comes at the cost of lenient privacy settings, allowing the robot to store personal data and to connect to the internet permanently, which brings in associated data security risks. However, to date, it still remains unclear how this dilemma affects attitudes and behavioral intentions towards the respective robot. To shed light on the influence of a social robot’s privacy settings on robot-related attitudes and behavioral intentions, we conducted two online experiments with a total sample of N = 320 German university students. We hypothesized that strict privacy settings compared to lenient privacy settings of a social robot would result in more favorable attitudes and behavioral intentions towards the robot in Experiment 1. For Experiment 2, we expected more favorable attitudes and behavioral intentions for choosing independently the robot’s privacy settings in comparison to evaluating preset privacy settings. However, those two manipulations seemed to influence attitudes towards the robot in diverging domains: While strict privacy settings increased trust, decreased subjective ambivalence and increased the willingness to self-disclose compared to lenient privacy settings, the choice of privacy settings seemed to primarily impact robot likeability, contact intentions and the depth of potential self-disclosure. Strict compared to lenient privacy settings might reduce the risk associated with robot contact and thereby also reduce risk-related attitudes and increase trust-dependent behavioral intentions. However, if allowed to choose, people make the robot ‘their own’, through making a privacy-utility tradeoff. This tradeoff is likely a compromise between full privacy and full utility and thus does not reduce risks of robot-contact as much as strict privacy settings do. Future experiments should replicate these results using real-life human robot interaction and different scenarios to further investigate the psychological mechanisms causing such divergences.

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Towards Designing Privacy-Compliant Social Robots for Use in Private Households: A Use Case Based Identification of Privacy Implications and Potential Technical Measures for Mitigation

Horstmann

Diekmann

Buschmeier

et al. 2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Social robots are expected to increasingly appear in private households. The deployment of social robots in the private spheres of humans raises concerns regarding privacy protection. This paper analyses some of the legal implications of using social robots in private households on the basis of four practical use cases. It identifies the privacy concerns associated with each use case and proposes potential technical measures in the form of an initial concept for a companion privacy-app that could resolve or mitigate these concerns, and thereby enhance privacy compliance. The proposed app concept was evaluated in an exploratory study with ten participants. The preliminary results are encouraging and show that this concept has the potential to support the maintenance of privacy and provide control over the user's personal data and the robot's functions.

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Towards an Interaction-Centered and Dynamically Constructed Episodic Memory for Social Robots

Cited by 5 publications

References 6 publications

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

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

Never Trust Anything That Can Think for Itself, if You Can’t Control Its Privacy Settings: The Influence of a Robot’s Privacy Settings on Users’ Attitudes and Willingness to Self-disclose

Towards Designing Privacy-Compliant Social Robots for Use in Private Households: A Use Case Based Identification of Privacy Implications and Potential Technical Measures for Mitigation

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