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DOI: 10.4230/LIPIcs.TIME.2025.6

GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning

Authors: Mark Chevallier, Filip Smola, Richard Schmoetten, and Jacques D. Fleuriot

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract

We present GradSTL, the first fully comprehensive implementation of signal temporal logic (STL) suitable for integration with neurosymbolic learning. In particular, GradSTL can successfully evaluate any STL constraint over any signal, regardless of how it is sampled. Our formally verified approach specifies smooth STL semantics over tensors, with formal proofs of soundness and of correctness of its derivative function. Our implementation is generated automatically from this formalisation, without manual coding, guaranteeing correctness by construction. We show via a case study that using our implementation, a neurosymbolic process learns to satisfy a pre-specified STL constraint. Our approach offers a highly rigorous foundation for integrating signal temporal logic and learning by gradient descent.

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Mark Chevallier, Filip Smola, Richard Schmoetten, and Jacques D. Fleuriot. GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning. In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{chevallier_et_al:LIPIcs.TIME.2025.6,
  author =	{Chevallier, Mark and Smola, Filip and Schmoetten, Richard and Fleuriot, Jacques D.},
  title =	{{GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{6:1--6:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.6},
  URN =		{urn:nbn:de:0030-drops-244528},
  doi =		{10.4230/LIPIcs.TIME.2025.6},
  annote =	{Keywords: Signal temporal logic, spatio-temporal reasoning, neurosymbolic learning}
}

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GradSTL: Comprehensive Signal Temporal Logic for Neurosymbolic Reasoning and Learning

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