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Record W2134205293 · doi:10.1109/icsmc.1995.538054

Driving robots through natural language

2002· article· en· W2134205293 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsComputer sciencePlannerRobotNatural languageArtificial intelligenceContext (archaeology)RoboticsNatural language understandingRepresentation (politics)Natural language processingSpoken languageProgramming languageHuman–computer interaction

Abstract

fetched live from OpenAlex

Concerns a robot control system in which a high-level deductive engine generates an overall plan and dynamically consults low-level, distributed robotics programs which interface dynamically with the robot's actions and related information. We examine the particular problems of representing natural language in the context of a robots' world, and we present a concise logic system specifically designed to convey imperative sentences transmitted to a robot. This logic system is called CL or Command Language. We present as well an analyser which translates an English command into a CL formula. It is to be noted that CL serves not only as the internal representation language of a spoken or written command but is also the language in which a goal is directly expressed to the robot's high-level planner. In other words, the CL formula resulting from the analysis of a natural language command analysis is directly passed on to the planner as the goal to be attained. Details of the planner component of the approach are given.

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: Methods · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.459

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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

Citations9
Published2002
Admission routes2
Has abstractyes

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