Agent-based resource management for smart robotic sensors
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
We propose an agent-based architecture for managing the resources of robotic intelligent sensor agents (R-ISAs) (Petriu, E. et al., 2002). The main idea is to share all the available sensors among the entire society of agents, in such a way that, even though some of the agents do not have the required physical sensor or actuator on-board, they can always use other agents' resources to overcome this deficiency. The main goal of the project is to allow a human being from a computer station to interact remotely with a society of autonomous robotic sensor agents. The interaction is done through querying and sending commands. The novelty is that the user request leads to an intelligent proactive behavior performed by the agent society. The communication protocols between the agents have been successfully implemented and tested. The development of the Sensor Explorer is underway. The approach we chose is motivated by the success of multi-agent based systems, peer-to-peer (P2P) computing (http://www.openp2p.com), and the flexibility of grid computing (http://www.gridcomputing.com).
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 | 0.000 |
| 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