An Adaptive Multi-agent System for Continuous Learning of Streaming Data.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to be effective in solving time-critical problems in complex dynamic environments with higher levels of uncertainty, an intelligent system must continuously adapt parameters of its learning system to variations in the incoming signals generated by the non-stationary environment in a real-time fashion. The task of continuous online unsupervised learning of streaming data in complex dynamic environments under conditions of uncertainty requires the maximizing (or minimizing) of a certain similarity-based objective function defining an optimal segmentation of the input data set into clusters, which is an NP-hard optimization problem in a general metric space and is computationally intractable for real-world problems of practical interest. This paper describes the developed adaptive multi-agent approach to continuous online clustering of streaming data, which is originally sensitive to environmental variations and provides a fast dynamic response with event-driven incremental improvement of optimization results, trading-off operating time and result quality. Our two main contributions include a computationally efficient market-based algorithm of continuous agglomerative hierarchical clustering of streaming data and a knowledge-based self-organizing multi-agent system for implementing it. Experimental results demonstrate the strong performance of the implemented multi-agent learning system for continuous online clustering of both synthetic datasets and datasets from the RoboCup Soccer and Rescue domains. Further research on extending the adaptive learning approach to support online semi-supervised classification by continuously deducing semantic-based classification rules from clustering results and performing automatic rule-based classification at run-time is outlined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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