Bibliographic record
Abstract
ACORN (Agent-based Community Oriented Routing Network) is a distributed multi-agent architecture for the search, distribution and management of information across networks. ACORN utilises the concept of `information as agent' together with an application of Stanley Milgram's Small World Problem (the idea of Six Degrees of Separation) in order to route individual items of information around a network of people and agents. The ACORN ideal is to achieve a state where a web of users is created such that information distribution, queries and search, and browsing behaviour is encapsulated in a single adaptive architecture which learns community behaviour and knowledge in order to route agents to relevant destinations (users). This paper describes the ACORN architecture and its implementation. We introduce a novel idea of agent meeting places, or Cafes, to carry out community-based information sharing among mobile agents in ACORN. ACORN is compared with similar work, and evaluations of ACORN for information sharing among mobile agents are described. Applications of ACORN include Business to Business and Business to Consumer based e-Commerce solutions, virtual community creation and support systems, peer reviewing systems, and personalized directed information handling. Keywords: Multi-Agent information architectures, autonomous agents, mobile agents, keyphrase matching, multi-agent architecture, community based information handling, e-Commerce. 1.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".