Machine Learning for LTL Synthesis

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Maximilian Prokop
SemML

SemML

In this project we develop learning-based exploration heuristics for LTL Synthesis that exploit the semantic labelling of the underlying Automaton/Game.

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Publications

2025
SemML: Enhancing Automata-Theoretic LTL Synthesis with Machine Learning
Jan Kretínský, Tobias Meggendorfer, Maximilian Prokop, Ashkan Zarkhah
Tools and Algorithms for the Construction and Analysis of Systems - 31st International Conference, TACAS 2025, Held as Part of the International Joint Conferences on Theory and Practice of Software, ETAPS 2025, Hamilton, ON, Canada, May 3-8, 2025, Proceedings, Part I
2023
Guessing Winning Policies in LTL Synthesis by Semantic Learning  
Jan Křetínský, Tobias Meggendorfer, Maximilian Prokop, Sabine Rieder
CAV 2023