Version 2 2023-12-11, 20:13
Version 1 2023-11-29, 18:23
conference contribution
posted on 2023-12-11, 20:13 authored by Frederik SchmittFrederik Schmitt, Christopher Hahn, Jens U. Kreber, Markus N. Rabe, Bernd FinkbeinerBernd FinkbeinerTemporal logics are a well established formal specification paradigm to specify the behavior of systems, and serve as inputs to industrial-strength verification tools. We report on current advances in applying deep learning to temporal logical reasoning tasks, showing that models can even solve instances where competitive classical algorithms timed out.<p></p>
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Preferred Citation
Frederik Schmitt, Christopher Hahn, Jens Kreber, Markus Rabe and Bernd Finkbeiner. Deep Learning for Temporal Logics. In: Conference on Artificial Intelligence and Theorem Proving (AITP). 2021.Primary Research Area
- Reliable Security Guarantees
Name of Conference
Conference on Artificial Intelligence and Theorem Proving (AITP)Legacy Posted Date
2022-05-06Open Access Type
- Gold
BibTeX
@inproceedings{cispa_all_3658, title = "Deep Learning for Temporal Logics", author = "Schmitt, Frederik and Hahn, Christopher and Kreber, Jens U. and Rabe, Markus N. and Finkbeiner, Bernd", booktitle="{Conference on Artificial Intelligence and Theorem Proving (AITP)}", year="2021", }Usage metrics
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