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Record W2114819182

Optimal Robot Recharging Strategies For Time Discounted Labour

2008· article· en· W2114819182 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
TopicReinforcement Learning in Robotics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWork (physics)RobotComputer scienceEnergy (signal processing)Selection (genetic algorithm)Value (mathematics)Action (physics)Class (philosophy)SimulationArtificial intelligenceMathematicsEngineeringMachine learning
DOInot available

Abstract

fetched live from OpenAlex

Abstract?? Energy is defined as the potential to perform work: ev-ery system that does some work must possess the required energy in advance. An interesting class of systems, including animals and recharging robots, has to actively choose when to obtain energy and when to dissipate energy in work. If work-ing and collecting energy are mutually exclusive, as is com-mon in many animal and robot scenarios, the system faces an essential two-phase action selection problem: (i) how much energy should be accumulated before starting work; (ii) at what remaining energy level should the agent switch back to feeding/recharging? This paper presents an abstract general model of a energy-managing agent that does time-discounted work. Analyzing the model, we find solutions to both ques-tions that optimise’s the value of the work done. This result is validated empirically by simulated robot experiments that agree closely with the model.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.339
Threshold uncertainty score0.469

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 science0.0010.000
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 head0.026
GPT teacher head0.263
Teacher spread0.237 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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