Neuro-Symbolic Learning from Temporal Sequences in Safety-Critical Systems

Safety-critical systems have seldom been used as an application domain for neuro-symbolic AI, despite their inherent characteristics that combine generation and processing of raw data, coming from heterogeneous devices, with enforcement or discovery of properties, usually encoded as rules or constraints. In this paper, we consider the task of classifying sequences of perceptual stimuli collected from a safety-critical system, where safety-related properties are represented in the form of linear temporal logic formulae. Our preliminary results on a benchmarking framework for temporal reasoning show that this kind of problem can be extremely challenging, for both neural-only and temporal neuro-symbolic approaches.