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Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to have a clear understanding and overview of the decisions an organization makes. A means to understanding and modeling decisions is the Decision Model and Notation (DMN) standard published by the Object Management Group in 2015. In this standard, it is possible to design and specify how a decision should be taken. However, DMN lacks elements to specify the actors that fulfil different roles in the decision-making process as well as not taking into account the autonomy of machines. In this paper, we re-address and-present our earlier work [1] that focuses on the construction of a framework that takes into account different roles in the decision-making process, and also includes the extent of the autonomy when machines are involved in the decision-making processes. Yet, we extended our previous research with more detailed discussion of the related literature, running cases, and results, which provides a grounded basis from which further research on the governance of (semi) automated decision-making can be conducted. The contributions of this paper are twofold; 1) a framework that combines both autonomy and separation of concerns aspects for decision-making in practice while 2) the proposed theory forms a grounded argument to enrich the current DMN standard.
Within our research on robotic gas detection, we have focused on making a prototype based on Boston Dynamics SPOT, because it takes a lot of difficulties out of prototyping. For instance, it has its own obstacle avoidance algorithm, good drivers are available for ROS2, and SPOT is meant for outdoor navigation. Being a legged robot means that it can easily traverse curbs, shrubberies, unstable soil and even stairs. For this document, we are going to use the insights that we used when looking for a solution for SPOT.
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The rapidly evolving aviation environment, driven by the Fourth Industrial Revolution, encompasses smart operations, communication technology, and automation. Airports are increasingly developing new autonomous innovation strategies to meet sustainability goals and address future challenges, such as shifting labor markets, working conditions, and digitalization (ACI World, 2019). This paper explores high-level governance strategies, a benchmarking study, that facilitates this transition. It aims to identify the key characteristics and features of the benchmarking study applicable to the development of autonomous airside operations. It also examines areas for improvement in operations, focusing on Key Performance Areas (KPAs) and strategic objectives related to airside automation. The findings highlight several essential performance areas and formulate it to a tailored benchmarking study that airports or aviation stakeholders can adopt to develop automation in airside operations. These criteria and features are summarized into a benchmarking framework that reflects strategy objectives. This paper contributes a valuable benchmarking methodology, supporting the growing global aviation demand for improvements toward more sustainable and smart autonomous airside operations. This outcome motivates aviation stakeholders to innovate to meet environmental and social sustainability goals.