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Record W3154645392

PENERAPAN LOGIKA FUZZY LOGIC PADA ENEMY AI GAME HOROR 3D THE GATE OF NIGHTMARE MENGGUNAKAN APLIKASI UNITY3D

2020· article· id· W3154645392 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

Venuenot available
Typearticle
Languageid
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsComputer scienceHumanitiesOperating systemArtificial intelligenceArt
DOInot available

Abstract

fetched live from OpenAlex

Artificial Intelligence merupakan salah satu bagian ilmu komputer yang dibuat agar Mesin (komputer) dapat melakukan pekerjaan seperti dan sebaik yang dilakukan oleh manusia. Aplikasi Unity 3D adalah game engine merupakan sebuah software pengolah gambar, grafik, suara, input, dan lain-lain yang ditujukan untuk membuat suatu game, meskipun tidak selamanya harus untuk game. Saat ini,  game banyak berkembang mengikut tradisi dan jaman. Maka dari itu banyak sekali perancangan game yang kurang menarik dikarenakan AI Enemy yang tidak responsive dan tersesan kaku, dan memerlukan Metode Fuzzy Logic. Fuzzy Logic adalah metodologi system control pemecahan masalah yang cocok untuk di implementasikan pada system, mulai dari sistem yang sederhana, sistem kecil, embedded system, jaringan PC, multi-channel atau workstation berbasis akuisisi data, dan system 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
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
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.045
GPT teacher head0.263
Teacher spread0.218 · 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

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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