Bibliographic record
Abstract
From sophisticated single agent in complex environments to multi-agent system (MAS) organizations, intelligent software agent research has come a long way in just under two decades. Many new branches of research in this field have emerged over the years which have enabled today's agents to perform a wide variety of human-like tasks such as learning, reasoning, negotiating, self-organizing and trusting each other, etc. Unfortunately, very few practical MASs have been deployed after such a long period of intensive research and development. For MASs to achieve higher popularity among end-users, we believe that agent oriented software engineering (AOSE) should adopt a new paradigm as has been done in Web 2.0 - to allow end-users to actively participate in developing or modifying features in agents at various stages of the agent's lifecycle. In this paper, we propose a vision for democratizing AOSE. We discuss what potential new researches need to be carried out in the areas of AOSE and agent learning in order to realize such a vision of moving from MASs to mass end-user agent development, and discuss potential challenges facing various aspects of this vision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".