Reducing Uncertainty in Collective Perception Using Self-Organizing Hierarchy
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Bibliographic record
Abstract
In collective perception, agents sample spatial data and use the samples to agree on some estimate. In this paper, we identify the sources of statistical uncertainty that occur in collective perception and note that improving the accuracy of fully decentralized approaches, beyond a certain threshold, might be intractable. We propose self-organizing hierarchy as an approach to improve accuracy in collective perception by reducing or eliminating some of the sources of uncertainty. Using self-organizing hierarchy, aspects of centralization and decentralization can be combined: robots can understand their relative positions system-wide and fuse their information at one point, without requiring, e.g., a fully connected or static communication network. In this way, multi-sensor fusion techniques that were designed for fully centralized systems can be applied to a self-organized system for the first time, without losing the key practical benefits of decentralization. We implement simple proof-of-concept fusion in a self-organizing hierarchy approach and test it against three fully decentralized benchmark approaches. We test the perceptual accuracy of the approaches for absolute conditions that are uniform time-invariant, time-varying, and spatially nonuniform with high heterogeneity, as well as the scalability and fault tolerance of their accuracy. We show that, under our tested conditions, the self-organizing hierarchy approach is generally more accurate, more consistent, and faster than the other approaches and also that its accuracy is more scalable and comparably fault-tolerant. Under spatially nonuniform conditions, our results indicate that the four approaches are comparable in terms of similarity to the reference samples. In future work, extending these results to additional methods, such as collective probability distribution fitting, is likely to be much more straightforward in the self-organizing hierarchy approach than in the decentralized approaches.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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