Abstract Inferring linear temporal logic over finite traces (LTL₅) formulas from a set of example traces, known as passive learning, presents significant challenges due to its combinatorial nature. In this paper, we introduce a novel approach to LTL₅ passive learning based on inductive logic programming (ILP), leveraging the inductive learning of answer set programs framework. Our ILP-based method effectively exploits the set of example traces to guide the learning process, and experimental results demonstrate that it o ffers a more efficient solution compared to traditional techniques based on propositional satisfiability.
Ielo et al. (Sat,) studied this question.