Co-evolutionary information gathering for a cooperative unmanned aerial vehicle team
Bibliographic record
Abstract
Abstract- Persistent surveillance and reconnaissance tasks in mobile cooperative sensor networks are key to constructing recognized domain pictures over a variety of civilian and military problem instances. However, efficient information gathering for a task such as target search by a team of autonomous unmanned aerial vehicles (UAVs) still remains a major challenge to achieve system-wide performance objective. Given problem complexity, most proposed distributed target search solutions so far consider simplifying assumptions such as predetermined path planning coordination strategy with implicit communication and ad hoc heuristics, and severely constrained resources. In this paper, we extend previous work reported on multi-UAV target search by learning resource-bounded multi-agent coordination, involving explicit action control coordination. The approach first relies on a new information-theoretic co-evolutionary algorithm to solve cooperative search path planning over receding horizons, providing agents with mutually adaptive and self-organizing behavior. The anytime algorithm is coupled to an extended information-sharing policy to periodically exchange world-state information and projected agent intents. Preliminary results show the value of the proposed approach in comparison to existing techniques or methods.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".