Learning-Based Approaches to Predictive Monitoring with Conformal
  Statistical Guarantees
Francesca Cairoli Luca Bortolussi Nicola Paoletti
This tutorial focuses on efficient methods to predictive monitoring (PM), the
problem of detecting at runtime future violations of a given requirement from
the current state of a system. While performing model checking at runtime would
offer a precise solution to the PM problem, it is generally computationally
expensive. To address this scalability issue, several lightweight approaches
based on machine learning have recently been proposed. These approaches work by
learning an approximate yet efficient surrogate (deep learning) model of the
expensive model checker. A key challenge remains to ensure reliable
predictions, especially in safety-critical applications. We review our recent
work on predictive monitoring, one of the first to propose learning-based
approximations for CPS verification of temporal logic specifications and the
first in this context to apply conformal prediction (CP) for rigorous
uncertainty quantification. These CP-based uncertainty estimators offer
statistical guarantees regarding the generalization error of the learning
model, and they can be used to determine unreliable predictions that should be
rejected. In this tutorial, we present a general and comprehensive framework
summarizing our approach to the predictive monitoring of CPSs, examining in
detail several variants determined by three main dimensions: system dynamics
(deterministic, non-deterministic, stochastic), state observability, and
semantics of requirements' satisfaction (Boolean or quantitative).