Temporal fuzzy based modeling as applied to the class of man-machine interaction
Bibliographic record
Abstract
Cognitive robotics have been recently touted as effective tools that could be used in a number of applications. In order for cognitive robots to act adequately and safely in real world, they must be able to achieve effective human-machine interaction or collaboration. Toward this end, performance evaluation metrics are used as important measures to achieve these goals. Toward the efficient modelling of such metrics, we attempt to determine the true time that an operator has to dedicate to the robot. Therefore, we define the robot attention demand (RAD) as a function of both Direct Interaction Time (DIT) and Indirect Interaction Time (IIT), where the IIT is a direct consequence of human trust in automation. We then propose a two-level fuzzy temporal model to evaluate and estimate the human trust in automation while collaborating and interacting with robots and machines to complete some tasks. The model combines the advantages of fuzzy logic and finite state machines to best model this phenomenon, and reduces the system complexity and the size of the knowledge base by grouping perception into first- and second-order perceptions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".