Partners: UOXF, University of Southampton, STIFTELSEN SINTEF, ATC A.E.
Increasingly, activities in work and social life are conducted within human-machine networks, where collaboration involves many different actors; governments and organisations, individuals and machines such as smart devices, sensors and computing infrastructure. The targets of these networks can be for policy making, commercial innovation, education, improved quality of life, information exchange or resource organisation. As networks become more complex and include more connections between humans and machines, so the characteristics of those networks become important in determining the effectiveness and successful evolution of the collaborations which they support. Emerging challenges are: understanding the processes necessary for developing and maintaining human-machine networks such that they are able to deliver their intended outcomes; and applying this knowledge to support emerging networks in public, commercial and civil domains to more readily achieve key European goals. In HUMANE we will develop a typology of human-machine networks focused on characteristics of relationships between networked humans and machines such as trust, motivation, reputation, responsibility, privacy and security. We will consider health indicators for networks and create prototype tools that can be exploited through a community of stakeholders to create and enrich human-machine networks. We will propose a roadmap and methodology for the evolution of such networks, appropriate to the needs of ICT developers, building on in-depth case studies taken from R&I projects relevant to the societal DAE pillars to form a supporting framework for future thinking and ICT policy-making in Europe. The project partners in HUMANE have wide and complementary experience in social sciences and ICT R&I, essential for bridging the technological, societal, industrial and human-centric components necessary to achieve improved understanding of emerging hyper-connected human-machine networks.
Partners: Utrecht University, Deep Blue (Italy), UNITN, STIFTELSEN SINTEF, SINTEF
Air Traffic Management (ATM) systems are large systems-of-systems that are managed via multiple layers (e.g., operational, organizational, technical) to better handle their complexity. Due to their tight interdependencies, any change introduced in either of these layers triggers changes in other layers. As such, change management in ATM systems is a difficult task and requires to know the full implications of change(s) over the whole system and support decision-making so that the ATM system does not suffer any issues with respect to functionality, safety, security, performance, cost efficiency, or other desired characteristics for a well-functioning ATM system. The main objective of PACAS is to better understand, model and analyze changes at different layers of the ATM system to support change management, while capturing how architectural and design choices influence the overall system. PACAS will deliver an innovative participatory change management process where stakeholders will actively participate to the architectural evolution of the ATM system. The key elements of PACAS are: (i) domain-specific modeling languages to express heterogeneous perspectives of ATM domain experts; (ii) impact propagation techniques to align multiple perspectives; (iii) a gamified platform as key driver for collaboration. The PACAS consortium will leverage state-of-art multi-view modeling methods, multi-objective reasoning techniques, and gamification approaches to develop and evaluate an innovative ATM participatory change management process. The validation will be assisted an external advisory board, composed of ATM domain experts, focusing on a limited number of strategic objectives concerning economical, organizational, security and safety aspects. The validation aims to demonstrate the generality of the PACAS concept and the potential for extended versions that support additional strategic perspectives that affect ATM change management.