Shield Synthesis for LTL Modulo Theories

TL;DR

This paper extends the concept of shielding in reactive synthesis to LTL modulo theories, enabling safety guarantees for complex, data-rich systems with temporal dynamics, which was previously limited to propositional logics.

Contribution

It introduces a novel approach for synthesizing shields in LTL modulo theories, broadening shielding applicability to richer logics and complex safety specifications.

Findings

Successfully synthesized shields for expressive logics

Demonstrated handling of rich data with temporal dynamics

First approach for shields in LTL modulo theories

Abstract

In recent years, Machine Learning (ML) models have achieved remarkable success in various domains. However, these models also tend to demonstrate unsafe behaviors, precluding their deployment in safety-critical systems. To cope with this issue, ample research focuses on developing methods that guarantee the safe behaviour of a given ML model. A prominent example is shielding which incorporates an external component (a ``shield'') that blocks unwanted behavior. Despite significant progress, shielding suffers from a main setback: it is currently geared towards properties encoded solely in propositional logics (e.g., LTL) and is unsuitable for richer logics. This, in turn, limits the widespread applicability of shielding in many real-world systems. In this work, we address this gap, and extend shielding to LTL modulo theories, by building upon recent advances in reactive synthesis modulo theories. This allowed us to develop a novel approach for generating shields conforming to complex safety specifications in these more expressive, logics. We evaluated our shields and demonstrate their ability to handle rich data with temporal dynamics. To the best of our knowledge, this is the first approach for synthesizing shields for such expressivity.