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Record W4313117368 · doi:10.1609/aiide.v9i6.12602

Improving Behaviour and Decision Making for Companions in Modern Digital Games

2013· article· en· W4313117368 on OpenAlexaff
Jonathan Tremblay

Bibliographic record

VenueProceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · 2013
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceTurns, rounds and time-keeping systems in gamesHuman–computer interactionCharacter (mathematics)Work (physics)Game mechanicsMultimediaVideo game designEngineeringMathematics

Abstract

fetched live from OpenAlex

Non-player character companions in video games should cooperate with players, understand them, and follow their lead during gameplay. In current games, however, companions tend to exhibit mainly static behaviours, and rarely live up to player expectations. In general, our work is aimed at improving this situation, developing both techniques and tools which allow companion NPCs to behave more appropriately, respecting player preferences and offering a more immersive gameplay for players.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.999

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 communication0.0020.003
Open science0.0010.001
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 head0.297
Teacher spread0.258 · 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.

Study designOther design
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

Citations1
Published2013
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