Bibliographic record
Abstract
The problem considered in this paper is how to guide actor-critic learning based an information granules that reflect knowledge about acceptable behavior patterns. The solution to this problem stems from approximation spaces, which were introduced by Zdzistaw Pawlak starting in the early 1980s and which provide a basis for perception of objects that are imperfectly known. It was also observed by Ewa Orlowska En 1982 that approximation spaces serve as a formal counterpart of perception, or observation. In our case, approximation spaces provide a ground for deriving pattern-based behaviours as well as information granules that can be used to influence the policy structure of an actor in a beneficial way. This paper includes the results of a recent study of swarm behavior by collections of biologically-inspired bots carried out in the context of an artificial ecosystem. This ecosystem has an ethological basis that makes it possible to observe and explain the behavior of biological organisms that carries over into the study of actor-critic learning by interacting robotic devices. The contribution of this article is a framework for actor-critic learning defined in the context of approximation spaces and information granulation
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".