A Complex Adaptive System of Systems Approach to Human–Automation Interaction in Smart Grid
Bibliographic record
Abstract
This chapter expresses three cognitive genres: descriptive genre, normative/prescriptive genre, and know-how genre. The descriptive genre introduces and discusses on the following disciplines: the core concepts of complexity, complex adaptive system (CAS) of systems (CASoS), the application domains of human-automation interaction (HAI) and adaptive autonomy (AA), especially in Smart Grid, and two implementation ideas of expert systems and Petri nets. The chapter describes the automation related notions, that is, automation and HAI, followed by the investigation of HAI models' evolution from the perspectives of dimensions and dynamism. The idea of AA is then introduced as a dynamic HAI scheme, followed by the classification of AA implementation methods. Petri nets are introduced as powerful tools for modeling complex systems (CxS). Finally, Petri net realization of the adaptive autonomy expert system (AAES) is presented, followed by a performance evaluation study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".