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Record W2101135445 · doi:10.1109/etacom.1996.502507

Mobile intelligent agent systems: WAVE vs. JAVA

2002· article· en· W2101135445 on OpenAlexaff
Son T. Vuong, Ivan Ivanov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceJavaDistributed computingMobile agentCode (set theory)Variety (cybernetics)Operating systemProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

We examine and contrast two interesting systems which are at the two ends of the scale in their ability to support program mobility: JAVA and WAVE. JAVA offers a useful combination of some of the most attractive features in conventional programming languages and environments. It supports distributed computing and TCP/IP protocols (e.g. HTTP), and allows transparent access to objects across the net via URLs. New interactive code modules can be dynamically loaded and linked on demand from a variety of distributed sources, thus supporting to some extent the implementation of mobile intelligent agents. WAVE, on the other hand, offers a completely new programming paradigm, which directly supports dynamic creation and processing of arbitrary knowledge networks. In WAVE, programs ("waves") can be injected from arbitrary points in the distributed system, roam in the network in a virus-like mode, while replicating into parallel instances, and coordinating with each other, without any centralized control. Different waves can cooperate in a distributed space, thereby forming dynamic societies which may collectively perform complex knowledge processing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.007
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.034
GPT teacher head0.223
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations11
Published2002
Admission routes1
Has abstractyes

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