Towards ILP-based LTLf passive learning.
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| Title: | Towards ILP-based LTLf passive learning. |
|---|---|
| Authors: | Ielo, Antonio1 (AUTHOR), Law, Mark2 (AUTHOR), Fionda, Valeria1 (AUTHOR), Ricca, Francesco1 (AUTHOR), Giacomo, Giuseppe De3 (AUTHOR), Russo, Alessandra4 (AUTHOR) |
| Source: | Journal of Logic & Computation. Mar2026, Vol. 36 Issue 2, p1-30. 30p. |
| Subjects: | Induction (Logic), Satisfiability (Computer science), Machine learning, Inference (Logic), Nonmonotonic logic |
| Abstract: | Inferring linear temporal logic over finite traces (|$\text{LTL}_{\text{f}}$|) 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 |$\text{LTL}_{\text{f}}$| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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