Bibliographic record
Abstract
Intelligent Agents An intelligent agent is a computational or natural system that senses its environment and takes actions intelligently according to its goals. We focus on computational (versus natural) agents that act in the interests of their human principals. Such intelligent agents aid humans in making decisions. Intelligent agents can play several possible roles in the human decision process. They may play the roles of a consultant, an assistant, or a delegate. For simplicity, we will refer to intelligent agents as just agents . When an agent acts as a consultant (Figure 1.1), it senses the environment but does not take actions directly. Instead, it tells the human principal what it thinks should be done. The final decision rests on the human principal. Many expert systems, such as medical expert systems (Teach and Shortliffe [75]), are used in this way. In one possible scenario, human doctors independently examine patients and arrive at their own opinions about the diseases in question. However, before the physicians finalize their diagnoses and treatments, the recommendations from expert systems are considered, possibly causing the doctors to revise their original opinions. Intelligent agents are used as consultants when the decision process can be conducted properly by humans with satisfactory results, the consequences of a bad decision are serious, and agent performance is comparable to that of humans but the agents have not been accorded high degrees of trust.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.419 | 0.265 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".