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Ice management is essential for maintaining the safety of offshore operations in Arctic regions. We present the combined results of three experiments conducted in a full-mission bridge simulator specially designed for ice management. From a quantitative analysis of the results, we infer the effect of three variables on performance: (1) experience, (2) training, and (3) Decision Support System (DSS). The results confirm that experience and training improve performance for untrained and inexperienced simulator participants. The DSS also improves performance, but with a smaller effect. Qualitative observations using vessel position heat-map diagrams and exit interviews suggested that novice participants using the DSS adopted expert strategies but carried out their tasks more slowly and with less precision. This has important consequences for the design of a future DSS used in training simulators or onboard ships. Potential improvements to the DSS design might include real-time feedback to the user, a redesign of the human-machine interface (HMI), and increasing user input and customization with a human factors focus. [ABSTRACT FROM AUTHOR]

The last decade has seen a growing interest in the benefits of applying project management (PM) and system engineering (SE) in an integrated way toward complex projects and programs. The concept of project complexity dimensions, with roots in both disciplines, is suggested as a component of an integrated project complexity narrative. This paper investigates how such a project complexity narrative is reflected when informants talk about the role of PM and SE in two academic organizations. Most informants address uncertainty and social‐political risks as part of their work, but any consistent use of a project complexity narrative is related to environmental and technical systems. The findings also indicate difficulty differentiating between the concepts of complicated and complex. The paper further contemplates how these findings inform efforts to manage complex research projects and programs. [ABSTRACT FROM AUTHOR]

There is an increased complexity in applied research projects that demand more researcher skills, especially in managing the research project and interdisciplinary work. Researchers receive little training in how to manage such projects, yet most manage to deliver project results. There is a tradition of project management and systems engineering which benefits complex development projects in industrial settings. Despite the apparent benefits, we found limited application of either project management or systems engineering practices in academia. Furthermore, we found barriers to applying these practices in the first place, such as a lack of clear guidance or tools for their execution. A case study based on 18 semi‐structured interviews provides a perspective on academic research projects, and how the application of project management and systems engineering in an academic setting shows promise to improve the realization of concept design. [ABSTRACT FROM AUTHOR]

The research investigates the influence of human expertise on the effectiveness of ice management operations. The key contribution is an experimental method for investigating human factor issues in an operational setting. Ice management is defined as a systematic operation that enables a marine operation to proceed safely in the presence of sea ice. In this study, the effectiveness of ice management operations was assessed in terms of ability to modify the presence of pack ice around an offshore structure. This was accomplished in a full-mission marine simulator as the venue for a systematic investigation. In the simulator, volunteer participants from a range of seafaring experience levels were tasked with individually completing ice management tasks. Recorded from 36 individuals' simulations, we compared ice management effectiveness metrics against two independent variables: (i) experience level of the participant, categorized as either cadet or seafarer and (ii) ice severity, measured in ice concentration. The results showed a significant difference in ice management effectiveness between experience categories. We examined what the seafarers did that made them more effective and characterized their operational tactics. The research provides insight into the relative importance of vessel operator skills in contributing to effective ice management, as well as how this relative importance changes as ice conditions vary from mild to severe. This may have implications for training in the nautical sciences and could help to inform good practices in ice management. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) for Autonomous Surface Vehicles (ASVs) addresses developers' needs for model interpretation, understandability, and trust. As ASVs approach wide-scale deployment, these needs are expanded to include end user interactions in real-world contexts. Despite recent successes of technology-centered XAI for enhancing the explainability of AI techniques to expert users, these approaches do not necessarily carry over to non-expert end users. Passengers, other vessels, and remote operators will have XAI needs distinct from those of expert users targeted in a traditional technology-centered approach. We formulate a concept called 'human-centered XAI' to address emerging end user interaction needs for ASVs. To structure the concept, we adopt a model-based reasoning method for concept formation consisting of three processes: analogy, visualization, and mental simulation, drawing from examples of recent ASV research at the Norwegian University of Science and Technology (NTNU). The examples show how current research activities point to novel ways of addressing XAI needs for distinct end user interactions and underpin the human-centered XAI approach. Findings show how representations of (1) usability, (2) trust, and (3) safety make up the main processes in human-centered XAI. The contribution is the formation of human-centered XAI to help advance the research community's efforts to expand the agenda of interpretability, understandability, and trust to include end user ASV interactions. [ABSTRACT FROM AUTHOR]

In navigation applications, Artificial Intelligence (AI) can improve efficiency and decision making. It is not clear, however, how designers should account for human cooperation when integrating AI systems in navigation work. In a novel empirical study, we examine the transition in the maritime domain towards higher levels of machine autonomy. Our method involved interviewing technology designers (n = 9) and navigators aboard two partially automated ferries (n = 5), as well as collecting field observations aboard one of the ferries. The results indicated a discrepancy between how designers construed human-AI collaboration compared to navigators' own accounts in the field. Navigators reflected upon their role as one of 'backup,' defined by ad-hoc control takeovers from the automation. Designers positioned navigators 'in the loop' of a larger control system but discounted the role of in-situ skills and heuristic decision making in all but the most controlled takeover actions. The discrepancy shed light on how integration of AI systems may be better aligned to human cooperation in navigation. This included designing AI systems that render computational activities more visible and that incorporate social cues that articulate human work in its natural setting. Positioned within the field of AI alignment research, the main contribution is a formulation of human-AI interaction design insights for future navigation and control room work., Competing Interests: Competing InterestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© The Author(s) 2022.)