Křetínský, Jan and Meggendorfer, Tobias and Prokop, Maximilian and Zarkhah, Ashkan (2025) SemML : Enhancing Automata-Theoretic LTL Synthesis with Machine Learning. In: 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, Proceedings :. Lecture Notes in Computer Science . Springer, CAN, pp. 233-253. ISBN 9783031906428
Full text not available from this repository.Abstract
Synthesizing a reactive system from specifications given in linear temporal logic (LTL) is a classical problem, finding its applications in safety-critical systems design. We present our tool SemML, which won this year’s LTL realizability tracks of SYNTCOMP, after years of domination by Strix. While both tools are based on the automata-theoretic approach, ours relies heavily on (i) Semantic labelling, additional information of logical nature, coming from recent LTL-to-automata translations and decorating the resulting parity game, and (ii) Machine-Learning approaches turning this information into a guidance oracle for on-the-fly exploration of the parity game (whence the name SemML). Our tool fills the missing gaps of previous suggestions to use such an oracle and provides an efficient implementation with additional algorithmic improvements. We evaluate SemML both on the entire set of SYNTCOMP as well as a synthetic data set, compare it to Strix, and analyze the advantages and limitations. As SemML solves more instances on SYNTCOMP and does so significantly faster on larger instances, this demonstrates for the first time that machine-learning-aided approaches can out-perform state-of-the-art tools in real LTL synthesis.