4 research outputs found
Conformal Prediction for STL Runtime Verification
We are interested in predicting failures of cyber-physical systems during
their operation. Particularly, we consider stochastic systems and signal
temporal logic specifications, and we want to calculate the probability that
the current system trajectory violates the specification. The paper presents
two predictive runtime verification algorithms that predict future system
states from the current observed system trajectory. As these predictions may
not be accurate, we construct prediction regions that quantify prediction
uncertainty by using conformal prediction, a statistical tool for uncertainty
quantification. Our first algorithm directly constructs a prediction region for
the satisfaction measure of the specification so that we can predict
specification violations with a desired confidence. The second algorithm
constructs prediction regions for future system states first, and uses these to
obtain a prediction region for the satisfaction measure. To the best of our
knowledge, these are the first formal guarantees for a predictive runtime
verification algorithm that applies to widely used trajectory predictors such
as RNNs and LSTMs, while being computationally simple and making no assumptions
on the underlying distribution. We present numerical experiments of an F-16
aircraft and a self-driving car
Deep Learning for Abstraction, Control and Monitoring of Complex Cyber-Physical Systems
Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative
to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep
learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant
amount of uncertainty.Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative
to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep
learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant
amount of uncertainty
CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship
This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship