Rollout Algorithms for Wireless Sensor Network-Assisted Target Search
Bibliographic record
Abstract
We consider a mobile, autonomous searcher that aims to find the source of a broadcast message in a network of location-agnostic wireless sensor nodes. In certain types of networks, the hop count of the broadcast message, given the distance from the source node, is well approximated by a simple parametric distribution. The mobile searcher can interrogate a nearby node to obtain, with a given success probability, the hop count of the broadcast message. The search is modeled as an infinite horizon, undiscounted cost, and partially observable Markov decision process. A computationally efficient approximate online solution is obtained through policy rollout using a novel heuristic. Simulation results show that our rollout approach outperforms commonly used search methods based on a mutual information utility. We quantify the loss due to the use of an approximate hop count observation model and study the effect of statistical dependence between observations. Furthermore, we discuss how to account for this dependence by adapting an integer autoregressive model for the hop count.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".