Designing the Impression of Social Agents' Real-time Interruption Handling

Gebhard, Patrick; Schneeberger, Tanja; Mehlmann, Gregor; Baur, Tobias; André, Elisabeth

doi:10.1145/3308532.3329435

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…These relationships between speech timing and personality were confirmed by ter Maat et al [2011], by Cafaro et al [2016], as well as by . Additionally, Gebhard et al [2019] showed that the same is true for interactive human-agent conversation. term adaptation is used for manipulating an agent's behaviors in order to express a personality which is best for the individual user.…”

Section: Expressing Personality With Siasmentioning

confidence: 80%

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Adaptive Artificial Personalities

Janowski¹,

Ritschel²,

André³

2022

The Handbook on Socially Interactive Agents

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Section: Expressing Personality With Siasmentioning

confidence: 80%

“…One of the few real-time interaction examples is found in the work of Gebhard et al [2019]. However, their study relied on a limited domain with pre-scripted dialogue and pre-defined user actions, which is still common practice.…”

Section: Autonomous Interactive Systemsmentioning

confidence: 99%

Adaptive Artificial Personalities

Janowski¹,

Ritschel²,

André³

2022

The Handbook on Socially Interactive Agents

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“…There are many attempts to recognize human emotions in the field of Affective Computing [15]- [17], as well as to model them in computational emotion models (see [18], [19] for an overview). Recently, interdisciplinary approaches are aiming to combine both [20], [21].…”

Section: Emotion Recognition and Emotion Modelingmentioning

confidence: 99%

“…The MARSSI model [21] relates appraisal rules and emotion regulation rules with social signal interpretation. It differentiates three functional dimensions of emotions: communicative, situative, and structural emotions.…”

Section: Emotion Recognition and Emotion Modelingmentioning

confidence: 99%

Towards a Deeper Modeling of Emotions: The Deep Method and its Application on Shame

Schneeberger

Hladky

Thurner

et al. 2021

2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)

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Understanding emotions is key to Affective Computing. Emotion recognition focuses on the communicative component of emotions encoded in social signals. This view alone is insufficient for deeper understanding and computational representation of the internal, subjectively experienced component of emotions. This paper presents the DEEP method as a starting point for a deeper computational modeling of internal emotions. The method includes how to query individual internal emotional experiences, and it shows an approach to represent such information computationally. It combines social signals, verbalized introspection information, context information, and theory-driven knowledge. We apply the DEEP method exemplary on the emotion shame and present a schematic dynamic Bayesian network for modeling it.

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“…Yielding the turn as soon as an overlap was detected led to lower dominance ratings than continuing. Gebhard et al [19] later varied the interruption response timing for a virtual agent in an interactive dialogue system. Their study confirmed that the agent was perceived as more dominant when it continued talking for a longer time after the user had tried to interrupt it.…”

Section: Socially-aware Behavior Synthesismentioning

confidence: 99%

Human-Inspired Socially-Aware Interfaces

Schiller

Weitz

Janowski

et al. 2019

Theory and Practice of Natural Computing

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Social interactions shape our human life and are inherently emotional. Human conversational partners usually try to interpret-consciously or unconsciously-the speaker's or listener's affective cues and respond to them accordingly. With the objective to contribute to more natural and intuitive ways of communicating with machines, an increasing number of research projects has started to investigate how to simulate similar affective behaviors in socially-interactive agents. In this paper we present an overview of the state of the art in social-interactive agents that expose a socially-aware interface including mechanisms to recognize a user's emotional state, to respond to it appropriately and to continuously learn how to adapt to the needs and preferences of a human user. To this end, we focus on three essential properties of socially-aware interfaces: Social Perception, Socially-Aware Behavior Synthesis, and Learning Socially-Aware Behaviors. We also analyze the limitations of current approaches and discuss directions for future development.

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Designing the Impression of Social Agents' Real-time Interruption Handling

Cited by 6 publications

References 9 publications

Adaptive Artificial Personalities

Adaptive Artificial Personalities

Towards a Deeper Modeling of Emotions: The Deep Method and its Application on Shame

Human-Inspired Socially-Aware Interfaces

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