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Record W2792416229 · doi:10.1109/tac.2018.2811785

Decentralized Supervisory Control of Discrete Event Systems: An Arborescent Architecture to Realize Inference-Based Control

2018· article· en· W2792416229 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Automatic Control · 2018
Typearticle
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsInferenceSupervisory controlControl (management)Computer scienceTree (set theory)Event (particle physics)Control systemEvent treeControl theory (sociology)Artificial intelligenceMathematicsFault tree analysisEngineeringReliability engineering

Abstract

fetched live from OpenAlex

The two simplest language-based decentralized controls of discrete event systems are called conjunctive and permissive (C&P) and disjunctive and antipermissive (D&A) controls. On the other side, inference-based control is the most general language-based decentralized control. In this paper, we propose a decentralized control method, called arborescent control, which constructs and uses a tree-like control architecture that depends on the control objective. Each node π of the tree is a disjunction or conjunction of the enabling/disabling decisions of the two children of π. We show that if inference-based control is applicable to the control objective, then every leaf of the obtained tree is a C&P or D&A control. This means that by combining adequately C&P and D&A controls, we can realize every control objective that is realizable by inference-based control.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.273
Teacher spread0.252 · 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