Parameter Invariant Monitoring for Signal Temporal Logic

Abstract

Signal Temporal Logic (STL) is a prominent specification formalism for real-time systems, and monitoring these specifications, specially when (for different reasons such as learning) behavior of systems can change over time, is quite important. There are three main challenges in this area: (1) full observation of system state is not possible due to noise or nuisance parameters, (2) the whole execution is not available during the monitoring, and (3) computational complexity of monitoring continuous time signals is very high. Although, each of these challenges has been addressed by different works, to the best of our knowledge, no one has addressed them all together. In this paper, we show how to extend any parameter invariant test procedure for single points in time to a parameter invariant test procedure for efficiently monitoring continuous time executions of a system against STL properties. We also show, how to extend probabilistic error guarantee of the input test procedure to a probabilistic error guarantee for the constructed test procedure.

Shield Synthesis for Real: Enforcing Safety in Cyber-Physical Systems

“…We evaluated our tool on seven sets of benchmarks, including automotive powertrain control [19], autonomous driving [35], adaptive cruise control [29], multidrone fleet control [30], generic control [20], blood glucose control [39], and water tank control [2]. In all benchmarks, the original specification was given in STL, which has both timing and real-valued constraints.…”

“…We have evaluated the method on a number of applications, including automotive powertrain control [19], autonomous driving [35], adaptive cruise control [29], multi-drone fleet control [30], generic control [20], blood glucose control [39], and water tank control [2]. Our results show that, in all cases, the shield can quickly produce real-valued correction signals at run time.…”

A Retrospective Look at the Monitoring and Checking (MaC) Framework

“…In such cases, a clear separation between properties, checked in a conventional way using logics, and statistics-based detection of primitive events is even more useful. Preliminary investigation of such a setting has been explored in [20].…”

STLmc: Robust STL Model Checking of Hybrid Systems Using SMT

“…Because hybrid systems exhibit both discrete and continuous behaviors, STL provides a convenient and expressive way to specify important requirements of hybrid systems. STL has a vast range of applications on hybrid systems, including automotive systems [26], robotics [24,40], medical systems [36], IoT [7], smart cities [30], etc.…”