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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]
Copyright of Journal of Logic & Computation is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192182663
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Towards ILP-based LTLf passive learning.
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  Data: <searchLink fieldCode="DE" term="%22Induction+%28Logic%29%22">Induction (Logic)</searchLink><br /><searchLink fieldCode="DE" term="%22Satisfiability+%28Computer+science%29%22">Satisfiability (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Inference+%28Logic%29%22">Inference (Logic)</searchLink><br /><searchLink fieldCode="DE" term="%22Nonmonotonic+logic%22">Nonmonotonic logic</searchLink>
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  Data: 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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  Data: <i>Copyright of Journal of Logic & Computation is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1093/logcom/exaf069
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      – Code: eng
        Text: English
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        PageCount: 30
        StartPage: 1
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      – SubjectFull: Induction (Logic)
        Type: general
      – SubjectFull: Satisfiability (Computer science)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Inference (Logic)
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      – SubjectFull: Nonmonotonic logic
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      – TitleFull: Towards ILP-based LTLf passive learning.
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              Text: Mar2026
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              Y: 2026
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