This thesis presents a new methodology named CASADEMA (CApturing Semi-Automated DEcision MAking) which captures the interaction between humans and the technology they use to support their decision-making within the domain of Information Fusion. We are particularly interested in characterising the interaction between human decision makers and artefacts in semi-automated fusion processes. In our investigation we found that the existing approaches are limited in their ability to capture such interactions in sufficient detail. The presented method is built upon a distributed-cognition perspective. The use of this particular theoretical framework from cognitive science enables the method to take into account not only the role of the data captured in the physical and digital artefacts of the fusion system (e.g., radar readings, information from a fax or database, a piece of paper, etc.), but also the cognitive support function of the artefacts themselves (e.g., as an external memory) as part of the fusion process. That is, the interdependencies between the fusion process and decision-making can be captured. This thesis thus contributes to two main fields. Firstly, it enables, through CASADEMA, a distributed-cognition perspective of fusion processes in the, otherwise, rather technology-oriented field of Information Fusion. This has important conceptual implications, since it views fusion processes as extending beyond the boundary of physical/computer systems, to include humans, technology, and tools, as well as the interactions between them. It is argued that a better understanding of these interactions can lead to a better design of fusion processes, making CASADEMA an important contribution to the information fusion field. Secondly, the thesis provides, again in the form of CASADEMA, a practical application of the distributed-cognition theoretical framework. Importantly, the notations and definitions introduced in CASADEMA structure the otherwise currently rather loosely defined concepts and approaches in distributed cognition research. Hence, the work presented here also contributes to the fields of cognitive science and human-computer interaction. / <p>Examining Committee: Henrik Artman, Docent (Kungliga tekniska högskolan), Nils Dahlbäck, Professor (Linköpings universitet), Anna-Lisa Osvalder, Professor (Chalmers tekniska högskola)</p>

This is a case study made in collaboration with Proton Finishing AB to investigate the usage of Automated Guided Vehicles (AGV) in a surface treatment company. AGVs is a self-driving unit used for material transport in workplaces and logistics settings. In this study, the following research questions have been explored: ● What are the benefits of using AGVs trucks in a surface treatment company? ● What are the disadvantages of using AGVs in a surface treatment company? ● How can 5G impact the use of AGVs in the warehouse of a surface treatment company compared to AGVs with a traditional solution? ● Which model of AGV meets the requirements for warehouse management at Proton Finishing AB compared to other models? The theory was collected from relevant literature collected by the researchers at the library of Linnéuniversitetet and papers on scientific research. The empirical material was collected through semi-structured interviews with different suppliers of AGVs, a Cyber Security consultant, a Project Manager specialized in Private Networks and employees from Proton Finishing AB. There were several findings discovered with AGVs in a surface treatment company. Benefits and disadvantages were identified. The researchers discovered what model of AGV was most suitable for Proton Finishing AB’s warehouse. With help from the collected theory and empirical data, the researchers were able to suggest the most suitable AGV and propose how the implementation of AGVs can be done with 5G.