Periodic Reporting for period 3 - LASSO (Learning, Analysis, SynthesiS and Optimization of Cyber-Physical Systems)
Reporting period: 2018-11-01 to 2020-04-30
Cyber-physical systems are emerging throughout society in numerous areas including intelligent traffic systems (cars, roads), air traffic control systems (airplanes, airports), smart grid (power plants, wind turbine farms, solar cells), smart buildings (alarm, heating/ventilation/air condition systems, infotainment), and smart cities (buildings, traffic, energy supply, waste water management) as well as medical devices (dialysis machines, pacemakers).
Cyber-physical systems brings the promise of providing better solutions in terms of adaptability, performance, efficiency, functionality and usability. However, cyber-physical systems are often highly safety critical (e.g. medical devices or airplanes), hence utmost care must be taken to ensure that optimization of such performance measures does not violate crucial safety constraints. Given the growth in complexity -- including the growing use of machine learning -- this task becomes increasingly challenging and important for society, with new and scalable methods and tools urgently needed.
The principle barrier for mastering the engineering of complex CPS being both trustworthy and efficient is the lack of a theoretical well-founded framework for CPS engineering supported by powerful tools, that will enable companies to timely meet increasing market demands. It is the objective of LASSO to provide the new generation of scalable tools for model-based learning, analysis, synthesis and optimization of cyber-physical systems based on a mathematical sound foundation, that enables trade-offs between functional safety and quantitative performance.
LASSO will achieve its objective by ground-breaking and extensive combinations of two different research areas: model checking and machine learning. The framework will develop a complete metric approximation theory for quantitative models, allowing properties to be inferred from reduced or learned models with metric guarantees of their validity in the original system. Further, the applicability of the framework will be validated through a number of CPS case studies, and the tools developed will be made generally available.