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Record W2085293560 · doi:10.3166/ria.15.143-172

Les concepts spatiaux dans la programmation du go

2001· article· fr· W2085293560 on OpenAlexvenueno aff
Bruno Bouzy

Bibliographic record

VenueRevue d intelligence artificielle · 2001
Typearticle
Languagefr
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Le niveau faible des programmes de go n'est pas seulement du a la complexite combinatoire du go, mais aussi a la difficulte de construire une fonction d'evaluation adequate et complete d'une position. Cet article presente les nombreux concepts spatiaux que fait intervenir une fonction d'evaluation au go; les principaux sont le regroupement, le fractionnement, l'encerclement, l'agregation. La programmation go est une excellente illustration des theories du raisonnement spatial en raison de cette richesse conceptuelle. Pour chaque concept spatial, les outils mathematiques utilises pour les simuler sur machine (morphologie mathematique, topologie, distance de Hausdorff, raisonnement spatial qualitatif) sont presentes. Cet article s'appuie sur une demarche experimentale, basee sur une validation informatique, qui a produit le programme Indigo classe sur l'echelle internationale des programmes de go.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.008

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 head0.080
GPT teacher head0.333
Teacher spread0.253 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2001
Admission routes1
Has abstractyes

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Same venueRevue d intelligence artificielleSame topicArtificial Intelligence in GamesFrench-language works237,207