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Record W2114652610 · doi:10.1109/icsmc.2009.5346095

Improvement to rated group role assignment algorithms

2009· article· en· W2114652610 on OpenAlex
Haibin Zhu, Rob Alkins

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
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsNipissing University
Fundersnot available
KeywordsAssignment problemWeapon target assignment problemGeneralized assignment problemTask (project management)Computer scienceGroup (periodic table)Linear bottleneck assignment problemAlgorithmProcess (computing)Mathematical optimizationMathematicsEngineering

Abstract

fetched live from OpenAlex

Role assignment is a critical task in Role-Based Collaboration (RBC). It can be divided into three steps, i.e., agent evaluation, group role assignment, and role transfer, where group role assignment is a time-consuming process. This paper clarifies the problem of group role assignment; describes the general assignment problem; adapts the group role assignment problem to the general assignment problem; proposes an algorithm for group role assignment based on the Munkres algorithm; conducts an experiment by randomly creating agent qualifications, analyzes the solutions' performances, and points out the future work. The results show that the improved algorithm significantly improves the algorithm based on exhaustive search.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.785

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)0.0010.000

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 head0.017
GPT teacher head0.299
Teacher spread0.282 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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