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Record W4230892244 · doi:10.32920/ryerson.14655639.v1

Scatter Search on a Disk

2021· preprint· en· W4230892244 on OpenAlexaff
Nisha Chopra

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicOptimization and Search Problems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRobotUpper and lower boundsCarry (investment)Unit diskObject (grammar)Computer scienceCombinatoricsArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Consider a unit disk with two objects at unidentified locations. We examine the problem of two or more robots in search of both objects in the wireless communication model. We begin with two robots and both are needed to carry an object. Subsequently, we design several algorithms that describe robots trajectories in search of the objects. We were able to achieve a minimum worst-case search time of 6.7518 and a lower bound of 3 + π 2 . Additionally, we define two general cases and bound the worst-case search time for both. The first of the cases is for n ≥ 3 robots and an object can be moved by one robot. The second case is where we have n ≥ 3 robots and two robots are needed to carry an object. We achieve an upper bound of 1 + 2π n + sin (⌊n 2 ⌋ π n ) for the first case and an upper bound of 3 + 2π n + sin π n for the second case, with lower bounds of 2 + π n and 3 + π n respectively.

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 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 score0.669
Threshold uncertainty score0.878

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 communication0.0010.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.306
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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