Mining parametric temporal logic properties in model-based design for cyber-physical systems

Abstract

One of the advantages of adopting a Model Based Development (MBD) process is that it enables testing and verification at early stages of development. However, it is often desirable to not only verify/falsify certain formal system specifications, but also to automatically explore the properties that the system satisfies.In this work, we present a framework that enables property exploration for Cyber-Physical Systems. Namely, given a parametric specification with multiple parameters, our solution can automatically infer the ranges of parameters for which the property does not hold on the system. In this paper, we consider parametric specifications in Metric or Signal Temporal Logic (MTL or STL). Using robust semantics for MTL, the parameter mining problem can be converted into a Pareto optimization problem for which we can provide an approximate solution by utilizing stochastic optimization methods. We include algorithms for the exploration and visualization of multi-parametric specifications. The framework is demonstrated on an industrial size, high-fidelity engine model as well as examples from related literature.

Efficient Parametric Identification for STL

“…The works of [26], of [25,42], and of [29] are most related to ours. The problem they study can be stated as follows: given a system model M and a parameterized temporal formula φ[p], find the validity domain of φ relative to M, that is, the set of parameter valuations v such that w |= φ [v].…”

“…The robustness value ρ is expected to be continuous in u, and by definition is w ̸ |= φ when ρ(φ, w) < 0. This technique was indeed used in [25,27,29,42] precisely for the purpose of temporal logic parameter exploration.…”

Graph Temporal Logic Inference for Classification and Identification

“…Furthermore, such expressions are suitable for verification and controller synthesis. Over the past decade, there has been a growing interest in inferring temporal logic formulas from system trajectories [2,3,4,5,6,7,8,9]. However, to the best of our knowledge, there has been no work on inferring spatial or spatial temporal logic formulas from data.…”

A Robust Genetic Algorithm for Learning Temporal Specifications from Data

“…Most of the literature related to temporal logic inference from data focuses in particular on the problem of learning the optimal parameters given a specific template formula [5,7,14,16,20,26,27]. In [5], Asarin et al extend the Signal Temporal Logic (STL) [18] with the possibility to express the time bounds of the temporal operators and the constants of the inequalities as parameters.…”