00 Informatik, Wissen, Systeme
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Despite the wide body of literature on motion planning for autonomous robots targeting structured agricultural environments, there remains a need for more efficient and reliable approaches for unstructured terrains. This work is motivated by a cooperation with the startup Paltech, which develops a weed-removal robot for grasslands. In this work, we employ a typical layered approach for robot navigation. However, in contrast to standard navigation tasks, such as point-to-point navigation, the global planner must solve the more challenging problem of visiting multiple targets in an optimal way while considering the kinematic constraints of the vehicle.
End-user development,where non-programmers create or adapt their own digital tools, can play a key role in driving digital transformation within organizations. Currently, low-code/no-code platforms are widely used to enable end-user development through visual programming, minimizing the need for manual coding.
Recent advancements in generative AI, particularly large language model-based assistants and “copilots”, open new possibilities, as they may enable end users to generate and refine programming code and build apps directly from natural language prompts. This approach, here referred to as AI-assisted end-user coding, promises greater flexibility, broader applicability, faster development, improved reusability, and reduced vendor lock-in compared to the established visual LCNC platforms.
This paper investigates whether AI-assisted end-user coding is a feasible paradigm for end-user development, which may complement or even replace the LCNC model in the future. To explore this, we conducted a case study in which non-programmers were asked to develop a basic web app through interaction with AI assistants.The majority of study participants successfully completed the task in reasonable time and also expressed support for AI-assisted end-user coding as a viable approach for end-user development. The paper presents the study design, analyzes the outcomes, and discusses potential implications for practice, future research, and academic teaching.
Aging workforces in western industrialized countries are leading to a growing loss of experiential knowledge, particularly on the shop floor where expertise is largely tacit and informally acquired. This HICSS presentation reports on a study that examines current challenges in industrial knowledge management and identifies promising application areas for large language models (LLMs) within a broader effort to develop human-centered, GenAI-supported KM solutions. Based on two structured workshops with 23 experts from 14 German manufacturing companies, the study reveals key barriers to knowledge retention and highlights two prioritized LLM use cases. The findings provide concrete design implications for LLM-enabled KM systems and contribute practical insights into addressing demographic change in industrial contexts.
Künstliche Intelligenz (KI) hält Einzug in die Sozialwirtschaft – langsam, aber spürbar. In Einrichtungen der Pflege, Jugendhilfe oder Behindertenarbeit eröffnen algorithmische Systeme neue Möglichkeiten: von der Optimierung von Dienstplänen über automatisierte Verwaltungsprozesse bis hin zu KI-gestützter Spracherkennung oder Übersetzungshilfen für die Klient:innenkommunikation (Fraunhofer IAO o. J.). Viele Organisationen experimentieren bereits mit Anwendungen generativer KI wie ChatGPT, meist für unterstützende Aufgaben wie Textgenerierung oder visuelle Gestaltung. Tiefgreifende, systemische KI-Implementierungen sind hingegen noch die Ausnahme.
Crowding is a frequently discussed issue in tourism, impacting both visitors and residents of touristic city centers and beyond. Initially, crowding as a subjective construct is examined in detail by taking into account influencing factors as well as negative and positive crowding phenomena. Subsequently, this study examines the relationship between subjective crowding and actual visitor numbers in the town of Füssen in the Allgäu region by investigating the interplay between objective counting data and subjective crowding perceptions with on-site field research. The research employs a mixed-methods approach, combining subjective feedback from visitors via terminals with objective counts from camera sensors. In addition, external factors such as weather and public holidays are taken into account in order to investigate their influence on the perception of crowding using SHAP values. By identifying patterns in crowding perceptions, this study aims to enrich research in this field with new insights from an innovative research approach and to advance the management of visitor flows. Thereby, both tourist experiences and residents’ quality of life in touristic city centers should be enhanced.
The increasing strain of overtourism on popular destinations calls for data-driven strategies that can subtly influence tourist behavior without limiting freedom of choice. This study investigates the use of digital nudges, particularly the decoy effect, within recommender systems to guide visitor flows spatially or temporally. Through a series of focus group-based experiments, this study examines how introducing asymmetrically dominated options can alter destination preferences. The results suggest that spatial steering benefits more consistently from decoy-based interventions than temporal steering. Furthermore, the findings reveal that while nudging has a measurable impact, individual factors and personal preferences play a substantial role in shaping decisions. The study underscores the value of behaviorally informed recommender systems as tools for promoting sustainable tourism by facilitating more balanced visitor flows. It concludes with a call for further development of adaptive, user-aware digital strategies to address the multifaceted nature of tourist decision-making.
Due to their inherent socio-technical nature, the engineering of information systems creates quite distinctive challenges. Addressing them requires not only a deep understanding of technical capabilities, but also of the respective application area, and the behavior of the intended users. Being interdisciplinary by design, the Information Systems field provides a broad tool-set to address these challenges. We are thus delighted to present the proceedings of the 2nd European Symposium on Information Systems Engineering (ESISE), held from September 09 to 11, 2025, in Clervaux, Luxembourg. This symposium brought together researchers and thought leaders to explore the latest advancements and challenges in the field of information systems engineering.
Working with troubles and failures in conversation between humans and robots: workshop report
(2023)
This paper summarizes the structure and findings from the first Workshop on Troubles and Failures in Conversations between Humans and Robots. The workshop was organized to bring together a small, interdisciplinary group of researchers working on miscommunication from two complementary perspectives. One group of technology-oriented researchers was made up of roboticists, Human-Robot Interaction (HRI) researchers and dialogue system experts. The second group involved experts from conversation analysis, cognitive science, and linguistics. Uniting both groups of researchers is the belief that communication failures between humans and machines need to be taken seriously and that a systematic analysis of such failures may open fruitful avenues in research beyond current practices to improve such systems, including both speech-centric and multimodal interfaces. This workshop represents a starting point for this endeavour. The aim of the workshop was threefold: Firstly, to establish an interdisciplinary network of researchers that share a common interest in investigating communicative failures with a particular view towards robotic speech interfaces; secondly, to gain a partial overview of the “failure landscape” as experienced by roboticists and HRI researchers; and thirdly, to determine the potential for creating a robotic benchmark scenario for testing future speech interfaces with respect to the identified failures. The present article summarizes both the “failure landscape” surveyed during the workshop as well as the outcomes of the attempt to define a benchmark scenario.
Background
Conventional wearable electrocardiogram (ECG) validation excludes inconclusive results and assumes single-attempt testing, which inflates reported diagnostic performance for atrial fibrillation (AF) detection. Current reporting frameworks do not reflect real-world clinical use.
Objective
The study aimed to introduce and evaluate an intention-to-diagnose (ITD) framework incorporating inconclusive outputs and repeat testing for realistic assessment of wearable ECG diagnostic performance in AF detection.
Additive manufacturing enables the creation of complex geometries for various applications, such as dental, medical, prototyping, and aerospace components. Despite its advantages, the printing process can encounter errors due to its complexity and numerous influencing factors, necessitating real-time anomaly detection and classification. This work introduces a non-destructive method for defect detection by monitoring layer-wise image data of the L-PBF process using a YOLOv11 object detection model to ensure component quality. A novel approach combines multiple grayscale images from a single print sequence into a 3-channel image, incorporating information from the build area in the powder bed to enhance defect classification reliability. The determination of various defect classes is also a key aspect of this work, crucial for informing subsequent interventions and decisions. Tested on a dataset generated with a Trumpf TruPrint 1000 machine, this method achieved visually promising results.