MétaCan
Menu
Back to cohort
Record W2161420200 · doi:10.1109/ca.1994.324003

NSAIL PLAN: an experience with constraint-based reasoning in planning and animation

2002· article· en· W2161420200 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
TopicAI-based Problem Solving and Planning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer sciencePlan (archaeology)Constraint (computer-aided design)AnimationHuman–computer interactionArtificial intelligenceComputer graphics (images)Engineering

Abstract

fetched live from OpenAlex

A constraint-based reasoning system is used for knowledge representation and reasoning in behavioural animation, specifically in the animation of sailing behaviour. The object-oriented ECHIDNA reasoning and constraint logic programming shell handles the constraints for formulation and execution of plans for intelligent entities. At higher levels of control, the observed motion of an object is a reflection of the reasoning process of an intelligent entity as it reacts to its environment. The environment, internal knowledge and physical structure serve as constraints in developing a plan, which in turn provides additional constraints in the animation of reactive behaviour. An animation approach using constraint-based reasoning is presented focusing on details of the planning process adapted in the approach. The implementation of the NSAIL program reveals further insight into applying this approach towards the development of a high-level intuitive interface for behavioural animation. >

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.415

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 science0.0000.000
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 head0.034
GPT teacher head0.246
Teacher spread0.211 · 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

Citations5
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAI-based Problem Solving and PlanningFrench-language works237,207