Just-in-time information sharing architectures in multiagent systems
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 the Six Degrees of Separation) in order to route individual items of information around a network of people and agents. This paper describes additions made to the ACORN architecture and the implementation. A directory server that facilitates real-time communication between a client and corresponding agent is implemented. This server allows for instant feedback and modification to the agent by the client. The concept of an anonymous service provider is introduced to allow clients to generate anonymous agents that cannot be traced back to the original creator of the agent. This service is vital for maintaining some privacy aspects of the user. ACORN consists of a set of information-sharing locations referred to as Cafés. A dynamic café clustering method is developed. Using the proposed clustering method, cafés are dynamically created / destroyed to most accurately reflect the collective interests of the given members of said café. The performance evaluation of the proposed structure for the café using a testbed of multiple virtual users shows that the addition of multi-café component to ACORN's architecture improves its information sharing efficiency and leads to significant reduction in unnecessary mingling. Lastly, the concept of a fat and thin agent is introduced. A fat/thin agent architecture allows for minimizing network traffic as agents traverse the network in search of or distribution of knowledge.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".