Efficient computation of sensor activation decisions in discrete-event systems
Bibliographic record
Abstract
This paper considers partially-observed discrete-event systems where sensors are associated with events observable to an agent monitoring the system. The agent is capable of turning the sensors for events on and off dynamically, depending on the trajectory of the system. Reading data from the sensors may be costly so it is imperative that their use be reduced for reasons such as energy, bandwidth or security. When a sensor for an event is on / active any occurrence of the event is detected by the agent and is not detected otherwise. The agent may employ different sensor activation policies, depending on the task at hand. Sensor activation policies are defined over the transitions of a state-transition representation of the system. From sensor activation policies a map from observed event sequences to sensor activation decisions can be computed which the agent can use to determine which sensors to turn on / off and when. In this paper, we consider two subclasses of sensor activation policies of increasing generality. For each subclass, we demonstrate ways to compute maps from observed event sequences to sensor activation decisions in polynomial time.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".