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Record W1504993523

Reactive planning in a motivated behavioral architecture

2005· article· en· W1504993523 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 institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPlannerPlan (archaeology)RobotArchitectureHuman–computer interactionComputer scienceMobile robotContext (archaeology)PerceptionArtificial intelligencePsychology
DOInot available

Abstract

fetched live from OpenAlex

To operate in natural environmental settings, autonomous mobile robots need more than just the ability to navigate in the world, react to perceived situations or follow pre-determined strategies: they must be able to plan and to adapt those plans according to the robot’s capabilities and the situations encountered. Navigation, simultaneous localization and mapping, perception, motivations, planning, etc., are capabilities that contribute to the decision-making processes of an autonomous robot. How can they be integrated while preserving their underlying principles, and not make the planner or other capabilities a central element on which everything else relies on? In this paper, we address this question with an architectural methodology that uses a planner along with other independent motivational sources to influence the selection of behaviorproducing modules. Influences of the planner over other motivational sources are demonstrated in the context of the AAAI Challenge.

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 score0.480
Threshold uncertainty score0.393

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 head0.023
GPT teacher head0.280
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

Quick stats

Citations14
Published2005
Admission routes1
Has abstractyes

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