Mobile agents in distributed meeting scheduling: A case study for distributed applications
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes solutions based on mobile agents using mono-agent and multi-agent strategies for distributed meeting scheduling problem. Mobile agents are used in a typical decision-making scenario where they migrate to a calendar server to schedule a meeting between participants having time-co nflicts. The mobile agent will either find a schedule matching all constraints or propose the best relaxed schedule violating some low-priority constraints, if no schedule respecting all constraints is found. The performance comparison is based on two metrics: the execution time and the total network load generated. Performance study results identify the cost of many operations used by the agent-based paradigm, such as inter-agent communications and agent migration, in relation to the nature and size of information exchanged. The overall experimental results provide a glimpse of the possibilities that software agents offer to solve distributed problems.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it