Louisa Pragst

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The development of conversational agents with human interaction capabilities requires advanced affective state recognition integrating non-verbal cues from the different modalities constituting what in human communication we perceive as an overall affective state. Each of the modalities is often handled by a different subsystem that conveys only a partial(More)
We present work in progress on an intelligent embodied conversation agent in the basic care and healthcare domain. In contrast to most of the existing agents, the presented agent is aimed to have linguistic cultural, social and emotional competence needed to interact with elderly and migrants. It is composed of an ontology-based and reasoning-driven(More)
In this work, we investigate whether the cultural idiosyncrasies found in humanhuman interaction may be transferred to human-computer interaction. With the aim of designing a culture-sensitive dialogue system, we designed a user study creating a dialogue in a domain that has the potential capacity to reveal cultural differences. The dialogue contains(More)
Hierarchical planning approaches are often pursued when it comes to a real-world application scenario, because they allow for incorporating additional expert knowledge into the domain. That knowledge can be used both for improving plan explanations and for reducing the explored search space. In case a non-hierarchical planning model is already available,(More)
In this paper, we describe the principles and technologies that underpin the development of an adaptive dialogue manager framework, tailored to carrying out human-agent conversations in a natural, robust and flexible manner. Our research focus is twofold. First, the investigation of dialogue strategies that can handle dynamically created user and system(More)
In task-oriented dialogues, there is often only one right answer the system can give. However, a lack of variation can seem repetitive and unnatural. Humans change the way they express something, e.g. by being more or less concise. We aim to approximate this ability by automatically varying the level of verbosity and directness of a given system action. In(More)
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