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Towards ILP-Based \(\text {LTL}_\text {f}\) Passive Learning

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Inductive Logic Programming (ILP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14363))

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Abstract

Inferring a \(\text {LTL}_\text {f}\) formula from a set of example traces, also known as passive learning, is a challenging task for model-based techniques. Despite the combinatorial nature of the problem, current state-of-the-art solutions are based on exhaustive search. They use an example at the time to discard a single candidate formula at the time, instead of exploiting the full set of examples to prune the search space. This hinders their applicability when examples involve many atomic propositions or when the target formula is not small. This short paper proposes the first ILP-based approach for learning \(\text {LTL}_\text {f}\) formula from a set of example traces, using a learning from answer sets system called ILASP. It compares it to both pure SAT-based techniques and the exhaustive search method. Preliminary experimental results show that our approach improves on previous SAT-based techniques and that has the potential to overcome the limitation of an exhaustive search by optimizing over the full set of examples. Further research directions for the ILP-based \(\text {LTL}_\text {f}\) passive learning problem are also discussed.

This work was partially supported by MUR under PRIN project PINPOINT Prot. 2020FNEB27, CUP H23C22000280006; PRIN project HypeKG Prot. 2022Y34XNM, CUP H53D23003710006; PNRR MUR project PE0000013-FAIR, Spoke 9 - WP9.1 and WP9.2; Spoke 5 - WP5.1 and PNRR project Tech4You, CUP H23C22000370006, ERC Advanced Grant WhiteMech (No. 834228), and EU ICT-48 2020 project TAILOR (No. 952215).

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Notes

  1. 1.

    We use the simplified definitions of the reduct for choice rules presented in [22].

  2. 2.

    The set of constants of each type is assumed to be given with a task, together with the maximum number of variables in a rule, giving a set of variables \(V_1,\ldots , V_{max}\) that can occur in a hypothesis. Whenever a variable V of type t occurs in a rule, the atom t(V) is added to the body of the rule to enforce the type.

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Author information

Authors and Affiliations

  1. University of Calabria, Rende, Italy

    Antonio Ielo, Valeria Fionda & Francesco Ricca

  2. ILASP Limited, Grantham, UK

    Mark Law

  3. University of Oxford, Oxford, UK

    Giuseppe De Giacomo

  4. Imperial College, London, UK

    Mark Law & Alessandra Russo

Authors
  1. Antonio Ielo
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  2. Mark Law
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  3. Valeria Fionda
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  4. Francesco Ricca
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  5. Giuseppe De Giacomo
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  6. Alessandra Russo
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Correspondence to Antonio Ielo .

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Editors and Affiliations

  1. Università degli Studi di Ferrara, Ferrara, Italy

    Elena Bellodi

  2. Università degli Studi di Bari "Aldo Moro", Bari, Italy

    Francesca Alessandra Lisi

  3. Università degli Studi di Ferrara, Ferrara, Italy

    Riccardo Zese

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Ielo, A., Law, M., Fionda, V., Ricca, F., De Giacomo, G., Russo, A. (2023). Towards ILP-Based \(\text {LTL}_\text {f}\) Passive Learning. In: Bellodi, E., Lisi, F.A., Zese, R. (eds) Inductive Logic Programming. ILP 2023. Lecture Notes in Computer Science(), vol 14363. Springer, Cham. https://doi.org/10.1007/978-3-031-49299-0_3

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  • DOI: https://doi.org/10.1007/978-3-031-49299-0_3

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