GHOST and 3GEA: Task-driven Coalition and Planning Algorithms for Smart Airborne Vehicles
Bibliographic record
Abstract
Intelligent unmanned aerial vehicles (UAVs) can effortlessly execute large-scale and complex missions due to their maneuverability and autonomy. Networks of heterogenous UAVs carrying various equipment and resources offer more opportunities to execute the tasks that single UAV may fail to do it alone as multiple UAVs can form coalitions and cooperatively share their resources and complete the missions. In this paper, two novel algorithms have been proposed to tackle the challenges that engulf the problem of distributed task allocation and coalition formation with multiple vehicles. A dynamic game-theory-based algorithm named GHOST where the UAVs autonomously act as rational players and move according to their preferences to choose the members and sort the tasks for their coalitions. In addition to an evolutionary algorithm with 3-generations (3GEA) used for planning the coordinates of UAVs. This algorithm makes use of an archive of previous best solutions and a shallow FNN (feedforward neural network) trained with multiple supervised algorithms to improve the convergence and diversity of solutions. The comparative analyses with more than 20 state-of-the-art clustering and evolutionary algorithms proved that the proposed algorithms could achieve optimal coalition structures and complete missions with a success rate of 85-100%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".