A Patrolling Game for Adversaries with Limited Observation Time
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we consider a robot patrolling scenario on a weighted graph where an intruder can observe the patrolling path and use the information gained by observation to attack the graph's vertices. We pose the problem of finding a patrolling strategy as a multi-stage two player game. The patroller commits to a strategy that is unknown to the intruder. The intruder observes the patroller's actions for a finite amount of time to learn the patroller's strategy and then decides to either attack or renege based on its confidence in the learned strategy. We characterize the expected payoffs for the players and show that finding a k-factor approximation to the optimal patrolling strategy is NP-hard even when the patroller's strategy set is constrained to time homogeneous Markov chains. We propose a search algorithm to find a patrolling policy in such scenarios and illustrate the trade off between hard to learn and hard to attack strategies through simulations.
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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 | 0.000 |
| 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