Deep Learning for Temporal Logics

Schmitt, Frederik and Hahn, Christopher and Kreber, Jens U. and Rabe, Markus N. and Finkbeiner, Bernd
(2021) Deep Learning for Temporal Logics.
Unpublished
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(Unpublished)

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Official URL: http://aitp-conference.org/2021/

Abstract

Temporal 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.

Item Type: Other
Divisions: Bernd Finkbeiner (Reactive Systems Group, RSG)
Depositing User: Christopher Hahn
Date Deposited: 06 May 2022 09:04
Last Modified: 06 May 2022 09:04
Primary Research Area: NRA2: Reliable Security Guarantees
URI: https://publications.cispa.saarland/id/eprint/3658

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