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Record W2290133885 · doi:10.7939/r3bb8f

Fuegito: an Educational Software Package for Game Tree Search

2013· article· en· W2290133885 on OpenAlexaff
Colin Hunt, Martin Müller

Bibliographic record

VenueUniversity of Alberta Library · 2013
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceMonte Carlo tree searchTree (set theory)Game programmingSoftwareSimple (philosophy)Game treeTheoretical computer scienceSoftware engineeringArtificial intelligenceMachine learningSequential gameProgramming languageGame designGame theoryGame DeveloperMonte Carlo methodGame design documentMathematics

Abstract

fetched live from OpenAlex

Fuegito is an educational software package for learning about programming two player games. The package provides a simple, yet flexible and extensible framework which allows students to study the core search algorithms of computer game-playing, and extend them easily in projects. The current version of Fuegito supports alphabeta and Monte Carlo Tree Search as main game tree search methods, as well as a number of simple playing algorithms.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.111
Threshold uncertainty score0.371

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1110.042

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 head0.233
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreSoftware

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
Published2013
Admission routes1
Has abstractyes

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