Learning Robust Markov Models for Safe Runtime Monitoring

TL;DR

This paper introduces a formal framework and a novel learning method for robust runtime monitors using interval Hidden Markov Models, improving safety assurance in autonomous systems through model-based risk estimation.

Contribution

It formalizes the problem of learning robust runtime monitors, proposes a conformance-testing-based framework with convergence guarantees, and develops an efficient risk estimation algorithm.

Findings

Learned iHMMs outperform model-free approaches in safety monitoring.

The approach provides convergence guarantees for the learned models.

Empirical results validate the effectiveness of the proposed method.

Abstract

We present a model-based approach to learning robust runtime monitors for autonomous systems. Runtime monitors play a crucial role in raising the level of assurance by observing system behavior and predicting potential safety violations. In our approach, we propose to capture a system's (stochastic) behavior using interval Hidden Markov Models (iHMMs). The monitor then uses this learned iHMM to derive risk estimates for potential safety violations. The paper makes three key contributions: (1) it provides a formalization of the problem of learning robust runtime monitors, (2) introduces a novel framework that uses conformance-testing-based refinement for learning robust iHMMs with convergence guarantees, and (3) presents an efficient monitoring algorithm for computing risk estimates over iHMMs. Our empirical results demonstrate the efficacy of monitors learned using our approach, particularly when compared to model-free monitoring approaches that rely solely on collected data without access to a system model.