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Record W1977476763 · doi:10.1017/s0269888901000200

Holonic and multi-agent systems in industry

2001· article· en· W1977476763 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

VenueThe Knowledge Engineering Review · 2001
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKey (lock)Computer scienceNatural (archaeology)Autonomous agentFace (sociological concept)Social systemMulti-agent systemKnowledge managementComputer securityArtificial intelligenceSociology

Abstract

fetched live from OpenAlex

The concept of holonic systems has its roots in the desire to understand the structure of natural systems (e.g. living organisms and social organisations) and in particular their ability to behave in a stable yet flexible manner in the face of change. It is not surprising that the lessons learned from these natural systems could help with the design and control of complex man-made systems. However, a key issue is, how can one translate holonic concepts to real industrial environments? For example, one of the key holonic concepts, the holon, can be described as a self-contained autonomous and cooperative entity; when deciding how to implement holons, software agents appear to be the logical choice. In this paper, we summarise the presentations and discussions from a workshop held at the recent International Conference on Autonomous Agents that focused on this issue and brought together researchers from both the holonic systems and the multi-agents systems communities.

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: Review · Consensus signal: none
Teacher disagreement score0.542
Threshold uncertainty score0.461

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)0.0000.000

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 head0.260
Teacher spread0.234 · 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