System-of-Systems Complexity

The global availability of communication services makes it possible to interconnect independently developed systems, called constituent systems, to provide new synergistic services and more efficient economic processes. The characteristics of these new Systems-of-Systems are qualitatively different from the classic monolithic systems. In the first part of this presentation we elaborate on these differences, particularly with respect to the autonomy of the constituent systems, to dependability, continuous evolution, and emergence. In the second part we look at a SoS from the point of view of cognitive complexity. Cognitive complexity is seen as a relation between a model of an SoS and the observer. In order to understand the behavior of a large SoS we have to generate models of adequate simplicity, i.e, of a cognitive complexity that can be handled by the limited capabilities of the human mind. We will discuss the importance of properly specifying and placing the relied-upon message interfaces between the constituent systems that form an open SoS and discuss simplification strategies that help to reduce the cognitive complexity.Comment: In Proceedings AiSoS 2013, arXiv:1311.319

Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

CPSDebug: Automatic failure explanation in CPS models

AbstractDebugging cyber-physical system (CPS) models is a cumbersome and costly activity. CPS models combine continuous and discrete dynamics—a fault in a physical component manifests itself in a very different way than a fault in a state machine. Furthermore, faults can propagate both in time and space before they can be detected at the observable interface of the model. As a consequence, explaining the reason of an observed failure is challenging and often requires domain-specific knowledge. In this paper, we propose approach, a novel CPSDebug that combines testing, specification mining, and failure analysis, to automatically explain failures in Simulink/Stateflow models. In particular, we address the hybrid nature of CPS models by using different methods to infer properties from continuous and discrete state variables of the model. We evaluate CPSDebug on two case studies, involving two main scenarios and several classes of faults, demonstrating the potential value of our approach