Exploring Multi-Agent Systems for Intermodal Freight Fleets: Literature-based Justification of a New Concept

Freight transportation is increasingly connected with the automation of fleet assets by intelligent systems. This study emerges from the consortium research project Gaia-X 4 ROMS, which aims to develop a comprehensive approach for smart freight fleets in the field of parcel deliveries. Within this project, a novel type of multi-agent system (MAS) applied to freight fleet management is being developed. To determine the state of prior research and recent developments in this field, a systematic literature review was conducted and presented herein. The findings of the review demonstrate a significant lack of applied solutions in this topic, highlighting the need for a novel approach. Accordingly, a first framework for the envisioned multi-agent system, as conceptualized within the consortium research project, is presented in this work, serving as a basis for subsequent design phases

Investigating the Use of Augmented Reality and Machine Learning in Electrical Engineering Courses

The use of augmented reality (AR) in education and training is growing increasingly. However, applications to integrate augmented reality learning content into training for basic electrical engineering courses are scarce. The individual learning objectives of trainees complicate the digitalization of learning content, particularly for the drawing of circuit diagrams. To increase trainees\u27 learning outcome while simultaneously relieving instructors of classroom supervision, we designed and developed an AR-based prototype, to enhance hand-drawn circuit diagrams in vocational training. The context sensitivity is achieved by combining AR with image recognition. In an experiment with twelve participants, a positive impact of the prototype on trainees’ learning outcomes was observed, in comparison to a control group that received instructions without the prototype

Linking Augmented Reality with Peer Tutoring in Vocational Learning Environments: A Multi-Agent-Based Approach

During the elicitation of vocational training processes in a consortium research project, it became clear that augmented reality (AR) is a useful learning environment. However, using AR glasses may isolate trainees in training workshops and reduce the level of collaboration. In this study, we address these potential adverse side effects with peer tutoring. More exactly, we want to initiate tutor-tutee pairs among trainees during AR-based vocational training processes. We derive design features for a multi-agent-based approach to build tutor-tutee pairs based on concepts from the literature, a technical perspective, and the consortium within a design science research approach. To the best of our knowledge, we are the first to align AR with peer tutoring. With our first results, we provide a novel IT artifact in the realm of computer-supported collaborative learning in the information systems discipline. Future work can build upon these results even outside the scope of AR

The Truck Buddy: Towards a Mood-Based Truck Driver Assistance System

Road freight transportation presents a vital element of our economies while heavy-duty truck drivers (HTDs) make use of emerging technologies for operations. However, the work environment of HTDs is yet characterized by time pressure, social isolation, and safety concerns. Surprisingly, scientific knowledge about driver’s well-being and mood enhancement scarcely exists in the context of IS. This paper addresses the research gap by the exploration of a mood-based truck driver assistance system (MTDAS) – the “Truck Buddy”. We establish a design science research (DSR) project to explore the requirements and design objectives of an MTDAS based on data from a comprehensive literature review and expert interviews. Our results indicate that a context-sensitive MTDAS can assist HTDs well-being by five design objectives enabling system integration, communication, mood-detection, automated driver support, and the provision of driver feedback. This first iteration step constitutes a foundation for further evaluations and developments within a continuous DSR process