Practical Synthesis of Reactive Systems from LTL Specifications via Parity Games
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Abstract (arXiv)
The synthesis of reactive systems from linear temporal logic (LTL) specifications is an important aspect in the design of reliable software and hardware. We present our adaption of the classic automata-theoretic approach to LTL synthesis, implemented in the tool Strix which has won the two last synthesis competitions (Syntcomp2018/2019). The presented approach is (1) structured, meaning that the states used in the construction have a semantic structure that is exploited in several ways, it performs a (2) forward exploration such that it often constructs only a small subset of the reachable states, and it is (3) incremental in the sense that it reuses results from previous inconclusive solution attempts. Further, we present and study different guiding heuristics that determine where to expand the on-demand constructed arena. Moreover, we show several techniques for extracting an implementation (Mealy machine or circuit) from the witness of the tree-automaton emptiness check. Lastly, the chosen constructions use a symbolic representation of the transition functions to reduce runtime and memory consumption. We evaluate the proposed techniques on the Syntcomp2019 benchmark set and show in more detail how the proposed techniques compare to the techniques implemented in other leading LTL synthesis tools.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2021dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Expertscited by 14
- 2021Flexible FOND Planning with Explicit Fairness Assumptionscited by 6
- 2023Guessing Winning Policies in LTL Synthesis by Semantic Learningcited by 4
- 2026SemML 2.0: Synthesizing Controllers for LTLcited by 1
- 2022Predicting Winning Regions in Parity Games via Graph Neural Networks (Extended Abstract)cited by 0
- 2026SemML 2.0: Synthesizing Controllers for LTL27 Apr 2026
- 2023Guessing Winning Policies in LTL Synthesis by Semantic Learning24 May 2023
- 2022Predicting Winning Regions in Parity Games via Graph Neural Networks (Extended Abstract)18 Oct 2022
- 2021Flexible FOND Planning with Explicit Fairness Assumptions15 Mar 2021
- 2021dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts15 Jan 2021