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Record W2358424195

Research on Man-Computer Harmony Creative Design System Based on the Multi-Agent Technology and TRIZ Conflict Resolving Theories

2007· article· en· W2358424195 on OpenAlexvenueno aff
Qiu Jianchuan

Bibliographic record

VenueMicrocomputer applications · 2007
Typearticle
Languageen
FieldEngineering
TopicSimulation and Modeling Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTRIZComputer scienceHarmony (color)Key (lock)Knowledge managementManagement scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The basic concepts, principles and resolving idea of TRIZ conflict resolving theories were briefly introduced. And then, the problems and shortcomings of current innovation design platform were proposed. Besides high prices, low professional performance, it can not support to discover conflict, can not deduce innovation solution from TRIZ generic solution, it is hard to integrating legacy CAD system, it is not adaptive to work in the environment of distributed network and so on. Based on JADE development tool, the Man-Computer harmony design platform applying TRIZ conflict resolving theories was studied and designed, which support to discover conflict. At the same time, the key technology to develop and implement the system was discussed.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.072
GPT teacher head0.327
Teacher spread0.255 · 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
GenreMethods

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

Citations0
Published2007
Admission routes1
Has abstractyes

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