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Record W1933383986 · doi:10.1109/ccece.2003.1226138

IMAGO Prolog and its compilation

2004· article· en· W1933383986 on OpenAlexaff
Hua Liang, Xining Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Agent-Based Network Management
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPrologComputer scienceImagoProgramming languageLogic programmingCompiler

Abstract

fetched live from OpenAlex

In this paper, we briefly introduce our IMAGO (intelligent mobile agent gliding on-line) system - an infrastructure for mobile agent applications. Then we focus on the IMAGO Prolog-an agent application development kit based on Prolog, and its compilation techniques. IMAGO Prolog is a simplified Prolog with an extended application-programming interface (API) to support mobile agent applications. The implementation of IMAGO Prolog is based on a multithreading logic virtual machine (MLVM). The most significant differences from traditional Prolog virtual machines are that MLVM adopts a novel memory management approach, such as single stack scheme and CGC, and fully supports distributed as well as mobile agent applications. To translate an IMAGO program to a set of high efficient MLVM code, the design of IMAGO Prolog compiler is concentrated on five aspects: compatibility, memory management, separate compilation, two stream coding and optimization.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.199

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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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 designTheoretical or conceptual
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

Citations0
Published2004
Admission routes1
Has abstractyes

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