Multiplayer Pursuit-Evasion Games With Distributed Nash Equilibrium Solution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper concentrates on solving the multiplayer pursuit-evasion (MPE) game issue. In the existing MPE game framework, the fact that the Nash equilibrium and distribution are two contradicting properties which can not be achieved simultaneously. To tackle this challenge, novel cost functions that combine the best response approach and min-max scheme, are introduced such that the coupling terms in the existing MPE game formulations are removed. Consequently, the corresponding Nash and distributed solutions are obtained. Furthermore, a more general situation that the pursuers are not aware of the global information of the communication topology is discussed. In this framework, the adaptive coupling gains are incorporated into the improved cost functions to further realize the Nash equilibrium and distributed control strategies without the necessity of the information of topology graph. The sufficient conditions in two scenarios are given while the stability of adaptive coupling gains are provided as well. Besides, the Nash equilibrium properties of the solutions in two scenarios are analyzed, respectively. Finally, a simulation example is displayed to validate the theoretical results.
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.
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 | 0.001 |
| 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