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Towards ILP-based LTLf passive learning
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DOI：10.1093/logcom/exaf069.png)
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Abstract
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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.
Keywords:
Answer set programming
linear temporal logic over finite traces
learning from answer sets
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