Multi-robot target pursuit: towards an opportunistic control architecture
Bibliographic record
Abstract
This paper proposes an opportunistic control architecture to action selection in multi-robot, target pursuit problem. Proposed architecture views the problem of action selection in multi-robot environment as an instance of distributed probabilistic inference over the set of robotic agents' available actions, there by constructing a joint probability distribution from local evidence (e.g. robotic agents' respective views of the problem)and the higher level system task perspective. In present work, no explicit inter-robot communication is necessary, instead, robots attain necessary information such as other group members' as well as target's relative positioning information via communicating with a third party agent, the mediation unit. A novel rating system embedded within the control mechanism enables the system to not only determine robots' own action ranking (e.g. default rating) but also incorporates a tactical rating (e.g. opportunisitc rating) at group level. Performance of the opportunistic controller is evaluated in a multi-robot pursuit scenario so as to determine the utility of the proposed sub-ratings system. The analysis of the system performance has been carried out in two parts viz. presence or absence of mediator and absence of opportunistic sub-rating.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".