MA-DBN: Modeling Cooperative Agents for Approximate Online Monitoring
Bibliographic record
Abstract
Cooperative agents often need to reason about the states of a large and complex uncertain domain that evolves over time. Since exact calculation is usually impractical, we aim at providing a modeling tool that supports approximate online monitoring in such settings. Our proposed framework, the multi-agent dynamic Bayesian networks (MA-DBNs), models the dynamics of a group of cooperative agents approximately by utilizing weak interaction among them. Each dynamic agent maintains an individual chain of evolution, which enables a factorized and more efficient calculation of cooperative online monitoring. Meanwhile, agents are organized by an underlying hypertree structure to facilitate inter-agent communication. The error resulting from our model approximation is expected to be bounded over time, and a re-factorization method is proposed to improve the approximation quality. Moreover, MA-DBNs are flexible in admitting existing BN monitoring techniques for each agent's local evolution. As an example, we present an algorithm of distributed particle filters under our proposed model.
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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 | 0.000 | 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 | 0.000 |
| 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 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".