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Record W2115408911 · doi:10.1109/ccece.2007.389

Improving Project Performance with Role Driven Computational Support System

2007· article· en· W2115408911 on OpenAlexaff
Nora Houari, Behrouz H. Far

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDeliverableComputer scienceProcess managementKnowledge managementDistributed computingSoftware engineeringSystems engineeringEngineering

Abstract

fetched live from OpenAlex

This paper describes a role driven computational support for software project lifecycle development. For a role to achieve its goal it needs some support. In our system we build a group of support agents of different types around each abstract role (i.e. scheduler, document deliverable, bookkeeper, etc). These support agents interact among each other and with those of other roles that share tasks dependencies. Agents in the system are not restricted by predefined schemes they can join and/or leave the coalition. The objective of the system is threefold: first, to present individuals with awareness, precise views of who is doing what and when, and more specifically activities evolution; second, to provide grounding algorithms for conceptual role(activity) dynamics and consistencies in a distributed environment; and third, to facilitate the control and optimization of activities accomplishment in organizations.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.301

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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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
GenreEmpirical

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

Citations1
Published2007
Admission routes1
Has abstractyes

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