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Complementing B\"uchi Automata with Ranker (Technical Report)

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Vojtěch Havlena, Ondřej Lengál, Barbora Šmahlíková

Formal Languages and Automata Theory · Submitted 4 Jun 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The optimizations significantly improve the usability of Ranker, as shown in an extensive experimental evaluation with real-world benchmarks, where Ranker produced in the majority of cases a strictly smaller complement than other state-of-the-art tools.

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arXiv ID2206.01946 (version 1)
Pith IDNVVJPV3O, Pith's permanent record of this paper and its review
CitationBarbora \v{S}mahl\'ikov\'a, Ond\v{r}ej Leng\'al, Vojt\v{e}ch Havlena. "Complementing B\"uchi Automata with Ranker (Technical Report)." arXiv:2206.01946 (2022). https://pith.science/paper/NVVJPV3O
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,671 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.06 · human participants 0.02 · controlled comparison 0.60 · randomized assignment 0.01 · states quantitative result 0.26 · compares with prior results 0.95 · mentions code or data availability 0.22.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present the tool Ranker for complementing B\"uchi automata (BAs). Ranker builds on our previous optimizations of rank-based BA complementation and pushes them even further using numerous heuristics to produce even smaller automata. Moreover, it contains novel optimizations of specialized constructions for complementing (i) inherently weak automata and (ii) semi-deterministic automata, all delivered in a robust tool. The optimizations significantly improve the usability of Ranker, as shown in an extensive experimental evaluation with real-world benchmarks, where Ranker produced in the majority of cases a strictly smaller complement than other state-of-the-art tools.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present the tool Ranker for complementing B\"uchi automata (BAs). contribution 1.00 0.91 0.14 asserted 0.99
s1 Ranker builds on our previous optimizations of rank-based BA complementation and pushes them even further using numerous heuristics to produce even smaller automata. method 0.59 0.74 0.11 asserted 1.00
s2 Moreover, it contains novel optimizations of specialized constructions for complementing (i) inherently weak automata and (ii) semi-deterministic automata, all delivered in a robust tool. contribution 0.94 0.88 0.10 asserted 1.00
s3 The optimizations significantly improve the usability of Ranker, as shown in an extensive experimental evaluation with real-world benchmarks, where Ranker produced in the majority of cases a strictly smaller complement than other state-of-the-art tools. result 0.96 0.95 0.68 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.95.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present the tool Ranker for complementing B\"uchi automata (BAs). Ranker builds on our previous optimizations of rank-based BA complementation and pushes them even further using numerous heuristics to produce even smaller automata. Moreover, it contains novel optimizations of specialized constructions for complementing (i) inherently weak automata and (ii) semi-deterministic automata, all delivered in a robust tool. The optimizations significantly improve the usability of Ranker, as shown in an extensive experimental evaluation with real-world benchmarks, where Ranker produced in the majority of cases a strictly smaller complement than other state-of-the-art tools.

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