Real-time multi-agent fleet management strategy for autonomous underground mines vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a real-time multi-agent fleet management strategy for autonomous underground mines vehicles. The fleet management strategy is based on multi-agent technology and includes a novel variation of the Contract-Net protocol. This paper also proposes a set of conflict management procedures to deal with the narrow nature of underground drifts, as well as the sequencing of trucks’ loading activities. This strategy is tested in a simulated environment based on an industrial case study. Both the strategy and the test environment were implanted using AnyLogic. More specifically, the fleet management activities addressed are dispatching, routing and traffic management of mining vehicles, which deal respectively with the assignment of the next destination to a vehicle that has just completed a task; the choice of the route to be followed to reach the selected destination; and traffic coordination in the underground transportation network, made up of one-lane bi-directional road segments. To evaluate the proposed solution, an agent-based simulation model of a Canadian underground gold mine is implemented with AnyLogic. Results show that the proposed coordination strategy outperform the one currently employed strategy by the mine under investigation.
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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 | 0.000 |
| 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