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Record W2069547117 · doi:10.1145/2347583.2347587

Evolving dungeon crawler levels with relative placement

2012· article· en· W2069547117 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
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsWeb crawlerComputer scienceProcess (computing)Genetic programmingFitness functionSet (abstract data type)Focus (optics)Video gameFunction (biology)World Wide WebGenetic algorithmMultimediaArtificial intelligenceMachine learningProgramming language

Abstract

fetched live from OpenAlex

Procedural Content Generation (PCG) is the process of automating the construction of media types for use in game development, the movie industry, and other creative fields. By approaching the process of media creation as a search for content which is evaluated to express desirable features in a well-defined manner, we are able to apply evolutionary techniques such as genetic programming. This can greatly decrease the effort required to bring a project to completion by allowing artists and developers to focus on guiding the creation process. The specific generation process addressed is that of map creation for dungeon crawler video games. The search method proposed allows artists and developers to guide the generation process by specifying a set of tiles that define the composition of each map, and a fitness function that defines its structure.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.058
GPT teacher head0.296
Teacher spread0.239 · 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

Citations53
Published2012
Admission routes1
Has abstractyes

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