Bibliographic record
Abstract
There has been an increasing interest in developing computational theories of autonomous robots. However, the previous work has focused on intelligent modifications to internal computational structure of a robot, ignoring modifications to external environments. Our work is the first to formalize the modification of an environment by an introduction of markers that replace the internal state. Replacing internal state by addition of markers increases communication through the world. Use of markers has been shown to improve the effectiveness of robots at American Association of Artificial Intelligence robot competitions and RoboCup competitions. We report on the semantics of markers using their logical description and the internal state they replace. We introduce several properties of markers and marker sets like redundancy, mutual exclusivity and efficiency. We show how the stimuli of behaviours can be modified when markers are introduced to replace internal state. We also report on a semi-automatic algorithm that allows robots to place markers in their world. We show how the algorithm can be extended for obtaining a higher replacement of internal state and for handling an autonomous removal of markers. We provide several guidelines for effectively introducing markers in a robot's world.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".