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Record W136561625 · doi:10.1609/aiide.v3i1.18800

A Demonstration of SQUEGE: A CRPG Sub-Quest Generator

2007· article· en· W136561625 on OpenAlexaff
Curtis Onuczko, Duane Szafron, Jonathan Schaeffer, Maria Cutumisu, Jeff Siegel, Kevin Waugh, Allan Schumacher

Bibliographic record

VenueProceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · 2007
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScripting languageComputer scienceGame designPlot (graphics)AdventureGame DeveloperGame design documentVideo game developmentGame art designMultimediaProcess (computing)Human–computer interactionWorld Wide WebArtificial intelligenceProgramming languageMathematics

Abstract

fetched live from OpenAlex

Scripting the plot in a computer role-playing game requires a large number of scripts that are difficult to program, track and maintain. Game adventures often include simple plots, called side-quests, that are independent from the main plot. Side-quests are important, as they add value to the open-world appeal of the game (e.g., for acquiring experience or resources), but they still need scripts. We have designed a tool to aid in the rapid creation of side-quests. The game designer provides the game setting and a list of objects in the setting. Our tool uses this information to create an outline for the side-quests. Then we use ScriptEase, a generative design pattern tool, to generate scripts from the side-quest outlines for the Neverwinter Nights game. A game designer can also adapt these outlines after the generation process, to add value such as humour to the side-quests.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.894

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 communication0.0000.001
Open science0.0010.000
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 head0.039
GPT teacher head0.290
Teacher spread0.251 · 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 designBench or experimental
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

Citations4
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital EntertainmentSame topicArtificial Intelligence in GamesFrench-language works237,207