MétaCan
Menu
Back to cohort
Record W38091009 · doi:10.1609/aiide.v2i1.18766

A Demonstration of ScriptEase Ambient and PC-Interactive Behavior Generation for Computer Role-Playing Games

2006· article· en· W38091009 on OpenAlexaff
Maria Cutumisu, Duane Szafron, Jonathan Schaeffer, Kevin Waugh, Curtis Onuczko, Jeff Siegel, Allan Schumacher

Bibliographic record

VenueProceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · 2006
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScripting languageComputer scienceHuman–computer interactionGuard (computer science)Set (abstract data type)Character (mathematics)Code (set theory)MultimediaGame programmingGame designGame DeveloperProgramming languageGame design document

Abstract

fetched live from OpenAlex

ScriptEase is a visual tool that enables game designers to create complex interactive stories for computer role-playing games, without programming. In particular, ScriptEase automatically generates the scripting code for ambient and PC-interactive non-player character (NPC) behaviors from a set of behavior patterns. Without ScriptEase, a game designer would have to write scripting code manually to specify NPC behaviors. This demonstration describes the steps of generating complex and non-repetitive ambient and PC-interactive behavior scripts using generative behavior patterns with ScriptEase. We show how ambient behavior patterns are used to re-generate and improve the behaviors of all ambient NPCs in the Prelude module of the BioWare Corporation's Neverwinter Nights official campaign. We also demonstrate PC-interactive behaviors for a guard NPC in a custom Neverwinter Nights game module. With ScriptEase behavior patterns, game designers can easily and quickly populate a story with an engaging group of NPCs.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.933

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 communication0.0010.002
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 head0.042
GPT teacher head0.281
Teacher spread0.240 · 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

Citations3
Published2006
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