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Record W2031066963 · doi:10.1109/cig.2014.6932867

Automatic generation of fantasy role-playing modules

2014· article· en· W2031066963 on OpenAlexaff
Daniel Ashlock, Cameron McGuinness

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFantasyAdventureComputer scienceMonsterTreasureRelation (database)Human–computer interactionGenerator (circuit theory)MultimediaArtificial intelligenceArtLiterature

Abstract

fetched live from OpenAlex

Fantasy role playing games are a type of pencil-and-paper game in which a group of players, moderated by a referee, engage in cooperative story telling. This study leverages earlier level map generation technology to substantially automate the production of modules for fantasy role playing games. A goblin outpost is used as an example type of module for demonstrating the system. Advantages of the system include being able to produce a large number of different but tactically similar adventure modules with substantial control of content and feel by the designer. In addition to presenting a modified version of the map generator, the system presented in this study also automatically identifies rooms, abstracts the room adjacency relation, and populates the rooms with monsters, traps, and treasure. A example module, complete except for monster descriptions, is presented demonstrating proof of concept. Multiple maps for the goblin outpost are presented for contrast and a number of future directions for improvement are outlined.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.221

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.276
Teacher spread0.236 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2014
Admission routes1
Has abstractyes

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