Strix: Explicit Reactive Synthesis Strikes Back!

Meyer, Philipp J.; Sickert, Salomon; Luttenberger, Michael

doi:10.1007/978-3-319-96145-3_31

Cited by 78 publications

(61 citation statements)

References 22 publications

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“…Independent Evaluations. Since the first release of Strix [33] independent researchers used, evaluated, and compared it to other tools. At Syntcomp2019, Strix in its submitted version (19.07) again made first place in all categories in the LTL synthesis track.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…We implement and test the outlined ideas within Strix 2 [33], which relies on [28] for automata translations and [32] for parity game solving. An older version of Strix won in the TLSF/LTL track in all six categories of Syntcomp2018 against other mature tools such as ltlsynt [23], which also implements synthesis using parity games, and BoSy [14], which implements several bounded synthesis approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Editorial Note. This paper is an extended version of the preliminary report published in [33]. The synthesis approach is the same, but we give a much more detailed explanation of the techniques used, e.g., the decomposition and the product automaton construction.…”

Section: Introductionmentioning

confidence: 99%

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Practical synthesis of reactive systems from LTL specifications via parity games

2019

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You can teach an old dog new tricks: making a classic approach structured, forward-explorative, and incremental. Abstract 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.

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Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Practical synthesis of reactive systems from LTL specifications via parity games

2019

Self Cite

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“…The goal of the empirical evaluation is to study the performance of the version of DFI that computes winning strategies and to compare it with its closest cousins, the attractor-based algorithms Zielonka (ZLK), priority promotion (PP) and tangle learning (TL). We also compare with strategy iteration (SI) since this algorithm is used as a backend by the LTL synthesis tool STRIX [19].…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…These are 313 model-checking and 216 equivalence checking games. Furthermore, we consider a new category of 223 "reactive synthesis" benchmarks obtained via the LTL synthesis tool STRIX [19] from the synthesis competition [12]. See also Table 1.…”

Section: Empirical Evaluationmentioning

confidence: 99%

Simple Fixpoint Iteration To Solve Parity Games

Dijk

Rubbens

2019

Electron. Proc. Theor. Comput. Sci.

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A naive way to solve the model-checking problem of the mu-calculus uses fixpoint iteration. Traditionally however mu-calculus model-checking is solved by a reduction in linear time to a parity game, which is then solved using one of the many algorithms for parity games.We now consider a method of solving parity games by means of a naive fixpoint iteration. Several fixpoint algorithms for parity games have been proposed in the literature. In this work, we introduce an algorithm that relies on the notion of a distraction. The idea is that this offers a novel perspective for understanding parity games. We then show that this algorithm is in fact identical to two earlier published fixpoint algorithms for parity games and thus that these earlier algorithms are the same.Furthermore, we modify our algorithm to only partially recompute deeper fixpoints after updating a higher set and show that this modification enables a simple method to obtain winning strategies.We show that the resulting algorithm is simple to implement and offers good performance on practical parity games. We empirically demonstrate this using games derived from model-checking, equivalence checking and reactive synthesis and show that our fixpoint algorithm is the fastest solution for model-checking games.

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