Split agent-based routing in interconnected networks: Research Articles
Bibliographic record
Abstract
Adaptive behaviour of swarm-based agents (BT Technol. J. 1994; 12:104–113; AAMAS Conference '02, Melbourne, Australia, Month 1–2, 2002; Softcomput. J. 2001; 5(4):313–317.) is being studied in this paper with respect to network throughput for a certain amount of data traffic. Algorithmically complex problems like routing data packets in a network need to be faced with a dynamically adaptive approach such as agent-based scheme. Particularly in interconnected networks where multiple networks are participating in order to figure a large-scale network with different QoS levels and heterogeneity in the service of delay sensitive packets, routing algorithm must adopt in frequent network changes to anticipate such situations. Split agent-based routing technique (SART) is a variant of swarm-based routing (Adapt. Behav. 1997; 5:169–207; Proceedings of 2003 International Symposium on Performance Evaluation of Computer and Telecommunication Systems—SPECTS, Montreal, Canada, July 20–24, 2003; 240–247.) where agents are split after their departure to the next node on a hop-by-hop basis. Packets that are delay sensitive are marked as prioritized which agents recognize-as being a part of a packet- and try to influence the two-way routing tables. Thorough examination is made, for the performance of the proposed algorithm in the network and the QoS offered, taking into account a number of metrics. It is shown that the split agent routing scheme applied to interconnected networks offers a decentralized control in the network and an efficient way to increase overall performance and packet control reducing at the same time the packet loss concept. Copyright © 2004 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".