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Record W1724114674 · doi:10.5555/1226781.1226792

On Fuzzy Reasoning Using Matrix Representation of Extended Fuzzy Petri Nets

2003· article· en· W1724114674 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

VenueFundamenta Informaticae · 2003
Typearticle
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPetri netComputer scienceFuzzy logicRepresentation (politics)Knowledge representation and reasoningTheoretical computer scienceMatrix representationFuzzy set operationsMATLABNeuro-fuzzyArtificial intelligenceProgramming languageAlgebra over a fieldFuzzy setMathematicsFuzzy control system

Abstract

fetched live from OpenAlex

In 1990 Shyi-Ming Chen et al. presented a new approach to knowledge representation using fuzzy Petri nets (FPN). A fuzzy Petri net model allows a structural representation of knowledge and has a systematic procedure for supporting fuzzy reasoning. In this paper we propose an algebraic (matrix) representation of FPNs. We use this representation in a fuzzy reasoning algorithm which is simple to implement in modern programming languages such as C++, Cn or Java. Furthermore, there exists MATLAB - a computer system which makes it possible to solve many computing problems, especially those with matrix and vector formulations. We present also an approach enabling us to carry out a fuzzy reasoning process using the MATLAB environment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.038
GPT teacher head0.317
Teacher spread0.278 · 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