This keynote addresses why and how software architecture plays a central role in building collective intelligence in a rising class of complex systems, enabled by pervasive connectivity. De facto, different enabling technologies have progressively made possible to interconnect software-intensive systems that were independently developed, operated, managed, and evolved, yielding a new kind of complex system, i.e., a system that is itself composed of systems, the so-called “system-of-systems”. By its very nature, a software-intensive system-of-systems is architected to exhibit “collective intelligence”, very often opportunistically while operating in open environments. This is the case of systems-of-systems found in different areas as diverse as automotive, aeronautics, energy, healthcare, manufacturing, and transportation. In this context, safety is of paramount necessity since various aspects of our lives and livelihoods are becoming progressively dependent on those systems-of-systems.

In this keynote, I will present the challenges facing research on software architecture to address the building of artificial collective intelligence in systems-of-systems, while enforcing safety. I will discuss the discriminating characteristics of systems-of-systems when compared with single (even very large) systems from the software architecture perspective. Especially, I will analyze why novel architectural approaches are needed to handle the complexity of software-intensive systems-of-systems in particular regarding the architectural challenges implied by self-organization and emergent behavior, the two key features for supporting collective intelligence. I will survey novel solutions for architecting systems-of-systems based on supervenience for achieving intelligent collective behavior and illustrate their applications in the field of driving automation for connected and automated vehicles. I will conclude by identifying the guarantees of correctness of the proposed approaches for architecting safe systems-of-systems, based on formal description and verification techniques dealing with dynamicity and uncertainty.