Public Policy Challenges, Regulations, Oversight, Technical, and Ethical Considerations for Autonomous Systems: A Survey
Bibliographic record
Abstract
One of the main features of autonomous control systems is solving complicated optimization problems without human intervention in the presence of uncertainty in real time[1]. Autonomous systems (ASs) must have the recognition and discretion potency, evaluation and estimate authority, and decision-making power to independently perform various tasks in a dynamic environment[3]. These systems have a variety of sensors to understand environmental information so that they can distinguish, evaluate, and make decisions based on them[2]. In addition to an autonomous single-agent system, the AS can be designed in the form of multiagents to identify high-risk, hazardous, or inaccessible areas[3],[4]. Robotics and AS fields have led to significant advances in a wide range of areas, including unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), unmanned maritime vehicles (UMVs), artificial intelligence (AI), and self-learning machines[5]. Navigation[6],[7], 3-D path following[8]for autonomous underwater vehicles (AUVs), AUVs for oceanographic research[9], and rescue robots[10]have benefited from the development of autonomy.
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.
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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".