NeuroSymbolic Robustness Analysis for Discrete Systems with Respect to Transition Deviations

Abstract

Supervisory control of discrete-event systems provides formal guarantees of correctness with respect to a plant model and specification. However, these guarantees heavily rely on the plant model, which could deviate from nominal behavior due to modeling errors or faults. Recent notions of discrete robustness model deviations as a set of additional transitions that are added to the plant. The discrete robustness is defined as all sets of extra transitions for which the supervised system still guarantees a desired specification. However, this notion suffers from scalability due to the large solution space and conservatism since most deviations are infeasible in practice. This paper proposes to address these two issues using a neurosymbolic computing framework for discrete robustness analysis of safety properties. First, a neural reasoning layer based on Large Language Models infers a set of feasible deviation transitions from system models, specifications, and domain knowledge. Next, a symbolic layer computes the discrete robustness guarantees over the inferred deviation set. We evaluate our framework on three case studies, demonstrating that our method identifies a smaller set of feasible deviations while preserving robustness guarantees comparable to those of full transition-based analysis.