Další formáty:
BibTeX
LaTeX
RIS
@inproceedings{2321221,
author = {Křetínský, Jan and Meggendorfer, Tobias and Prokop, Maximilian and Rieder, Sabine},
address = {Cham},
booktitle = {Computer Aided Verification - 35th International Conference, CAV 2023, Proceedings, Part I},
doi = {http://dx.doi.org/10.1007/978-3-031-37706-8_20},
editor = {Constantin Enea and Akash Lal},
keywords = {verification; synthesis; temporal logic; machine learning},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham},
isbn = {978-3-031-37705-1},
pages = {390-414},
publisher = {Springer},
title = {Guessing Winning Policies in LTL Synthesis by Semantic Learning},
year = {2023}
}
TY - JOUR
ID - 2321221
AU - Křetínský, Jan - Meggendorfer, Tobias - Prokop, Maximilian - Rieder, Sabine
PY - 2023
TI - Guessing Winning Policies in LTL Synthesis by Semantic Learning
PB - Springer
CY - Cham
SN - 9783031377051
KW - verification
KW - synthesis
KW - temporal logic
KW - machine learning
N2 - We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several applications. Not only can the guessed strategy be applied as best-effort in cases where the game’s huge size prohibits rigorous approaches, but it can also increase the scalability of rigorous LTL synthesis in several ways. Firstly, checking whether a guessed strategy is winning is easier than constructing one. Secondly, even if the guess is wrong in some places, it can be fixed by strategy iteration faster than constructing one from scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize exploration in the most fruitful directions. In contrast to previous works, we (i) reflect the highly structured logical information in game’s states, the so-called semantic labelling, coming from the recent LTL-to-automata translations, and (ii) learn to reflect it properly by learning from previously solved games, bringing the solving process closer to human-like reasoning.
ER -
KŘETÍNSKÝ, Jan, Tobias MEGGENDORFER, Maximilian PROKOP a Sabine RIEDER. Guessing Winning Policies in LTL Synthesis by Semantic Learning. In Constantin Enea and Akash Lal. \textit{Computer Aided Verification - 35th International Conference, CAV 2023, Proceedings, Part I}. Cham: Springer, 2023, s.~390-414. ISBN~978-3-031-37705-1. Dostupné z: https://dx.doi.org/10.1007/978-3-031-37706-8\_{}20.
|
