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Record W3199168874 · doi:10.1016/j.ifacol.2021.08.470

ROCS 2.0: An Integrated Temporal Logic Control Synthesis Tool for Nonlinear Dynamical Systems

2021· article· en· W3199168874 on OpenAlexaff
Yinan Li, Zhibing Sun, Jun Liu

Bibliographic record

VenueIFAC-PapersOnLine · 2021
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReachabilityLinear temporal logicComputer scienceAbstractionTemporal logicKernel (algebra)AutomatonNonlinear systemControl logicControl (management)Theoretical computer scienceAlgorithmMathematicsArtificial intelligenceDiscrete mathematics

Abstract

fetched live from OpenAlex

This paper introduces ROCS 2.0, a control synthesis tool for nonlinear systems with control objectives given as temporal logic formulas. In addition to the basic invariance, reachability, Büchi, and co-Büchi specifications that can be handled in the previous version of ROCS, ROCS 2.0 provides a major upgrade to support the general class of linear temporal logic formulas that can be translated to deterministic Büchi automata. Moreover, ROCS 2.0 not only maintains and accelerates the kernel of its previous version—the engine based on the specification-guided control method—by more efficient implementation, but also integrates a second engine that implements the abstraction-based control method, which is optimized to gain time and memory efficiency. Such a feature gives the user the freedom to choose the control synthesis method that is more suitable for a specific control problem.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.300
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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Same venueIFAC-PapersOnLineSame topicFormal Methods in VerificationFrench-language works237,207