Decision-making in fuzzy environments using ontological control with fuzzy automata
Bibliographic record
Abstract
Decision-making is a process of thought. Goals and constrains in decision-making process are often treated theoretically as crisp sets. However, much of the decision-making in the real world takes place in an environment in which goals, as well as constraints and consequence of decision are not known precisely. In dealing with such sort of realistic problem, Zadeh and Bellman introduced a concept that maximizes the decision-making in fuzzy environment. Ontological control is a control methodology that deals with situations in which violation of ontological assumptions makes a programmable logic controller stay in an infinite repeating cycle. Ontological control with fuzzy automata has been introduced by Grantner to break the repeating cycle, and resume the control process. In this work, we review the basic concepts related to decision-making in fuzzy environments, then consider ontological control for system of systems (SoS) engineering applications, and use ModelSim to simulate such a process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".