http://dl.gi.de/handle/20.500.12116/6257 Beiträge im Fachbereich Künstliche Intelligenz (KI) Tue, 21 Jul 2026 13:58:57 GMT 2026-07-21T13:58:57Z http://dl.gi.de/handle/20.500.12116/40065 Exploring Monological and Dialogical Phases in Naturally Occurring Explanations Fisher, Josephine B.; Lohmer, Vivien; Kern, Friederike; Barthlen, Winfried; Gaus, Sebastian; Rohlfing, Katharina J. Recent approaches to Explainable AI (XAI) promise to satisfy diverse user expectations by allowing them to steer the interaction in order to elicit content relevant to them. However, little is known about how and to what extent the explainee takes part actively in the process of explaining. To tackle this empirical gap, we exploratively examined naturally occurring everyday explanations in doctor–patient interactions ( N  = 11). Following the social design of XAI, we view explanations as emerging in interactions: first, we identified the verbal behavior of both the explainer and the explainee in the sequential context, which we could assign to phases that were either monological or dialogical; second, we investigated in particular who was responsible for the initiation of the different phases. Finally, we took a closer look at the global conversational structure of explanations by applying a context-sensitive model of organizational jobs, thus adding a third layer of analysis. Results show that in our small sample of conversational explanations, both monological and dialogical phases varied in their length, timing of occurrence (at the early or later stages of the interaction) and their initiation (by the explainer or the explainee). They alternated several times in the course of the interaction. However, we also found some patterns suggesting that all interactions started with a monological phase initiated by the explainer. Both conversational partners contributed to the core organizational job that constitutes an explanation. We interpret the results as an indication for naturally occurring everyday explanations in doctor–patient interactions to be co-constructed on three levels of linguistic description: (1) by switching back and forth between monological to dialogical phases that (2) can be initiated by both partners and (3) by the mutual accomplishment and thus responsibility for an explanation’s core job that is crucial for the success of the explanation. Because of the explorative nature of our study, these results need to be investigated (a) with a larger sample and (b) in other contexts. However, our results suggest that future designs of artificial explainable systems should design the explanatory dialogue in such a way that it includes monological and dialogical phases that can be initiated not only by the explainer but also by the explainee, as both contribute to the core job of explicating procedural, clausal, or conceptual relations in explanations. Sat, 01 Jan 2022 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40065 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40066 What’s on Your Mind, NICO? Kerzel, Matthias; Ambsdorf, Jakob; Becker, Dennis; Lu, Wenhao; Strahl, Erik; Spisak, Josua; Gäde, Connor; Weber, Tom; Wermter, Stefan Explainable AI has become an important field of research on neural machine learning models. However, most existing methods are designed as tools that provide expert users with additional insights into their models. In contrast, in human-robot interaction scenarios, non-expert users are frequently confronted with complex, embodied AI systems whose inner workings are unknown. Therefore, eXplainable Human-Robot Interaction (XHRI) should leverage the user’s intuitive ability to collaborate and to use efficient communication. Using NICO, the Neuro-Inspired COmpanion, as a use-case study, we propose an XHRI framework and show how different types of explanations enhance the interaction experience. These explanations range from (a) non-verbal cues for simple and intuitive feedback of inner states via (b) comprehensive verbal explanations of the robot’s intentions, knowledge and reasoning to (c) multimodal explanations using visualizations, speech and text. We revisit past HRI-related studies conducted with NICO and analyze them with the proposed framework. Furthermore, we present two novel XHRI approaches to extract suitable verbal and multimodal explanations from neural network modules in an HRI scenario. Sat, 01 Jan 2022 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40066 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40067 Agnostic Explanation of Model Change based on Feature Importance Muschalik, Maximilian; Fumagalli, Fabian; Hammer, Barbara; Hüllermeier, Eyke Explainable Artificial Intelligence (XAI) has mainly focused on static learning tasks so far. In this paper, we consider XAI in the context of online learning in dynamic environments, such as learning from real-time data streams, where models are learned incrementally and continuously adapted over the course of time. More specifically, we motivate the problem of explaining model change , i.e. explaining the difference between models before and after adaptation, instead of the models themselves. In this regard, we provide the first efficient model-agnostic approach to dynamically detecting, quantifying, and explaining significant model changes. Our approach is based on an adaptation of the well-known Permutation Feature Importance (PFI) measure. It includes two hyperparameters that control the sensitivity and directly influence explanation frequency, so that a human user can adjust the method to individual requirements and application needs. We assess and validate our method’s efficacy on illustrative synthetic data streams with three popular model classes. Sat, 01 Jan 2022 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40067 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/40062 Human-Robot Body Experience: An Artificial Intelligence Perspective Beckerle, Philipp Human body experience is remarkably flexible, which enables us to integrate passive tools as well as intelligent robotic devices into our body representation. Accordingly, it can serve as a role model to make (assistive) robots interact seamlessly with their users or to provide (humanoid) robots with a human-like self-perception and behavior generation. This article discusses the potential of understanding human body experience and applying it to robotics. Particular focus is set on how to use artificial intelligence techniques and create intelligent artificial agents from insights about human body experience. The discussion is based on a summary of the author’s habilitation thesis and combines theoretical and experimental perspectives from psychology, cognitive science and neuroscience as well as computer science, engineering, and artificial intelligence. From this, it derives directions for future developments towards creating artificial body intelligence with human-like capabilities. Sat, 01 Jan 2022 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/40062 2022-01-01T00:00:00Z