E-Mail: {lastname}m@in.tum.de
ORCID:
0009-0008-6512-8693Address:
Institut für Informatik (I7) Technische Universität München Boltzmannstr. 3 D-85748 Garching bei München / Germany
Maximilian Prokop
Office: 03.11.39
E-Mail: {lastname}m@in.tum.de
ORCID: 0009-0008-6512-8693
Address:
E-Mail: {lastname}m@in.tum.de
ORCID:
0009-0008-6512-8693Address:
I am a joint PhD student of Technische Universität München (TUM) and Masaryk University Brno (MUNI) since April 2023. My major interest of research is improving partial exploration of formal systems through learning-based heuristics. Currently, I am concerned with the development of exploration heuristics for LTL/LTLf Synthesis.
Research Areas
Machine Learning for LTL Synthesis
We apply machine learning to the LTL synthesis problem.
Tools
Contact
SemML
In this project we develop learning-based exploration heuristics for LTL Synthesis that exploit the semantic labelling of the underlying Automaton/Game.
Publications
|
2025 |
SemML: Enhancing Automata-Theoretic LTL Synthesis with Machine Learning
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
CAV 2023 |