Cooperative and Deceptive Planning of Multiformations of Networked UCAVs in Adversarial Urban Environments
Bibliographic record
Abstract
We present an online decision policy built upon Markov decision processes for the cooperative path planning and weapons management of multiformations of UCAVs. Such cooperative control strategy provides optimal routing and weapons management despite conflicting objectives of opposing teams. The UCAVs constitute the blue team. They have for objective to reach prescribed tactical target locations, sequentially, from a common starting point, by following possibly different paths across an adversarial urban environment, within a prescribed time window and with maximum destruction capability once at close range. The UCAVs face an adversarial red team, which is composed of static ground units that can engage any nearby UCAV. The blue team’s planning thus aims at minimizing damages while maximizing the total number of remaining weapons at the time the tactical targets are reached. The blue team is modeled as controlled Markov processes with states expressing formations survival status and locations. The blue and red teams play the roles of cost-function minimizer and maximizer, respectively. Based on the assumption of known transition matrices, the worst-case minimization objective of the blue team is formulated as a finite-time optimization, which is solved by means of a dynamic programming equation with value function evolving according to a graph of feasible paths. Once the optimization problem is solved, the resulting decision policy takes the form of a lookup table. Online implementation necessitates that formations share information through a communications network. Real-time simulations show that the cooperative path planning and weapons management policy provides, on average, an improvement in performance when compared with single-formation routing. Furthermore, this high-level decision policy integrates seamlessly with robust formation flight control and decentralized team-level fault detection schemes proposed recently by the authors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".