SemML: Enhancing Automata-Theoretic LTL Synthesis with Machine Learning

TL;DR

SemML is a novel tool that combines automata-theoretic LTL synthesis with machine learning guidance, outperforming traditional methods in efficiency and success rate on benchmark problems.

Contribution

This paper introduces SemML, integrating semantic labeling and machine learning to enhance automata-based LTL synthesis, achieving superior performance over existing tools.

Findings

SemML solves more SYNTCOMP instances than Strix.

SemML is significantly faster on larger instances.

Machine learning guidance improves synthesis efficiency.

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 efficeint 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.